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The article discusses various reports published within the issue, including one regarding Biomedical Robotics and Biomechatronics, one on Neuroengineering and Neuroprostheses, and regarding Emerging Technologies in Multi-Parameter Biomedical Optical Imaging and Image Analysis.

The attenuation coefficient is a relevant biomarker for many diagnostic medical applications. Recently, the Depth-Resolved Confocal (DRC) technique was developed to automatically estimate the attenuation coefficients from Optical Coherence Tomography (OCT) data with pixel-level resolution. However, DRC requires that the confocal function parameters (i.e., focal plane location and apparent Rayleigh range) be known a priori. In this paper, we present the autoConfocal algorithm: a simple, automatic method for estimating those parameters directly from OCT imagery when the focal plane is within the sample. We present autoConfocal+DRC results on phantom data, ex-vivo biological tissue data, and in-vivo clinical data. [ABSTRACT FROM AUTHOR]

Volumetric ultrasound is rapidly emerging as a viable imaging modality for routine prenatal examinations. Biometrics obtained from the volumetric segmentation shed light on the reformation of precise maternal and fetal health monitoring. However, the poor image quality, low contrast, boundary ambiguity, and complex anatomy shapes conspire toward a great lack of efficient tools for the segmentation. It makes 3-D ultrasound difficult to interpret and hinders the widespread of 3-D ultrasound in obstetrics. In this paper, we are looking at the problem of semantic segmentation in prenatal ultrasound volumes. Our contribution is threefold: 1) we propose the first and fully automatic framework to simultaneously segment multiple anatomical structures with intensive clinical interest, including fetus, gestational sac, and placenta, which remains a rarely studied and arduous challenge; 2) we propose a composite architecture for dense labeling, in which a customized 3-D fully convolutional network explores spatial intensity concurrency for initial labeling, while a multi-directional recurrent neural network (RNN) encodes spatial sequentiality to combat boundary ambiguity for significant refinement; and 3) we introduce a hierarchical deep supervision mechanism to boost the information flow within RNN and fit the latent sequence hierarchy in fine scales, and further improve the segmentation results. Extensively verified on in-house large data sets, our method illustrates a superior segmentation performance, decent agreements with expert measurements and high reproducibilities against scanning variations, and thus is promising in advancing the prenatal ultrasound examinations. [ABSTRACT FROM AUTHOR]

Objective: During an interactive image segmentation task, the outcome is strongly influenced by human factors. In particular, a reduction in computation time does not guarantee an improvement in the overall segmentation time. This paper characterizes user efficiency during scribble-based interactive segmentation as a function of computation time. Methods: We report a controlled experiment with users who experienced eight different levels of simulated latency (ranging from 100 to 2000 ms) with two techniques for refreshing visual feedback (either automatic, where the segmentation was recomputed and displayed continuously during label drawing, or user initiated, which was only computed and displayed each time the user hits a defined button). Results: For short latencies, the user's attention is focused on the automatic visual feedback, slowing down his/her labeling performance. This effect is attenuated as the latency grows larger, and the two refresh techniques yield similar user performance at the largest latencies. Moreover, during the segmentation task, participants spent in average $72.67\% \,\pm\, 2.42\%$ for automatic refresh and $96.23\% \,\pm\, 0.06\%$ for user-initiated refresh of the overall segmentation time interpreting the results. Conclusion: The latency is perceived differently according to the refresh method used during the segmentation task. Therefore, it is possible to reduce its impact on the user performance. Significance: This is the first time a study investigates the effects of latency in an interactive segmentation task. The analysis and recommendations provided in this paper help understanding the cognitive mechanisms in interactive image segmentation. [ABSTRACT FROM PUBLISHER]

The aim of this paper is to present a novel inversion technique to measure the volume of thrombus induced under blood flow conditions in a lab-on-a-chip device. The device is composed by a microscope slide where gold electrodes are sputtered and by a polydimethylsiloxane microchannel placed on the top of the slide. A thrombogenic substance is placed on the slide in such a way that hemostasis is induced when whole blood flows in the microchannel. The novel idea behind the inversion technique is to fuse optical and electrical impedance data to obtain a quasi-real-time reconstruction of thrombus volume. This is not possible with present state-of-the-art optical imaging based on confocal microscopy, which provides the thrombus volume estimation only at the end of the thrombus formation. [ABSTRACT FROM AUTHOR]

This paper aims to conduct fMRI-based causality analysis in brain connectivity by exploiting the directed information (DI) theory framework. Unlike the well-known Granger causality (GC) analysis, which relies on the linear prediction technique, the DI theory framework does not have any modeling constraints on the sequences to be evaluated and ensures estimation convergence. Moreover, it can be used to generate the GC graphs. In this paper, first, we introduce the core concepts in the DI framework. Second, we present how to conduct causality analysis using DI measures between two time series. We provide the detailed procedure on how to calculate the DI for two finite-time series. The two major steps involved here are optimal bin size selection for data digitization and probability estimation. Finally, we demonstrate the applicability of DI-based causality analysis using both the simulated data and experimental fMRI data, and compare the results with that of the GC analysis. Our analysis indicates that GC analysis is effective in detecting linear or nearly linear causal relationship, but may have difficulty in capturing nonlinear causal relationships. On the other hand, DI-based causality analysis is more effective in capturing both linear and nonlinear causal relationships. Moreover, it is observed that brain connectivity among different regions generally involves dynamic two-way information transmissions between them. Our results show that when bidirectional information flow is present, DI is more effective than GC to quantify the overall causal relationship. [ABSTRACT FROM AUTHOR]

Goal: Thermography-based infection screening at international airports plays an important role in the prevention of pandemics. However, studies show that thermography suffers from low sensitivity and specificity. To achieve higher screening accuracy, we developed a screening system based on the acquisition of multiple vital-signs. This multimodal approach increases accuracy, but introduces the need for sophisticated classification methods. This paper presents a comprehensive analysis of the multimodal approach to infection screening from a machine learning perspective. Methods: We conduct an empirical study applying six classification algorithms to measurements from the multimodal screening system and comparing their performance among each other, as well as to the performance of thermography. In addition, we provide an information theoretic view on the use of multiple vital-signs for infection screening. The classification methods are tested using the same clinical data, which has been analyzed in our previous study using linear discriminant analysis. A total of 92 subjects were recruited for influenza screening using the system, consisting of 57 inpatients diagnosed to have seasonal influenza and 35 healthy controls. Results: Our study revealed that the multimodal screening system reduces the misclassification rate by more than 50% compared to thermography. At the same time, none of the multimodal classifiers needed more than 6 ms for classification, which is negligible for practical purposes. Conclusion: Among the tested classifiers k-nearest neighbors, support vector machine and quadratic discriminant analysis achieved the highest cross-validated sensitivity score of 93%. Significance: Multimodal infection screening might be able to address the shortcomings of thermography. [ABSTRACT FROM AUTHOR]

In magnetic resonance imaging and spectroscopy, a highly uniform magnetic field is desired for minimizing image distortions and line broadening, respectively. Typically, shim coils are employed to provide correcting fields for inhomogeneities brought about by magnetic interactions with the sample under study. Flexible field modeling is possible using an array of regularly shaped conducting loops that are independently electrically driven. In this paper, a design method is presented for generating coil winding patterns for shim arrays with irregular geometry elements. These designs are compared theoretically to the use of circular loop arrays and are shown to provide considerable improvements in field accuracy and efficiency for generating low-order correcting fields. [ABSTRACT FROM PUBLISHER]

This paper presents a set of validation procedures for nonrigid registration of functional EPI to anatomical M~ brain images. Although various registration techniques have been developed and validated for high-resolution anatomical MRI images, due to a lack of quantitative and qualitative validation procedures, the use of nonrigid registration between functional EPI and anatomical MRI images has not yet been deployed in neuroimaging studies. In this paper, the performance of a robust formulation of a nonrigid registration technique is evaluated in a quantitative manner based on simulated data and is further evaluated in a quantitative and qualitative manner based on in vivo data as compared to the commonly used rigid and affine registration techniques in the neuroimaging software packages. The nonrigid registration technique is formulated as a second-order constrained optimization problem using a free-form deformation model and mutual information similarity measure. Bound constraints, resolution level and cross-validation issues have been discussed to show the degree of accuracy and effectiveness of the nonrigid registration technique. The analyses performed reveal that the nonrigid approach provides a more accurate registration, in particular when the functional regions of interest lie in regions distorted by susceptibility artifacts. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm for estimating the location of the breast surface from scattered ultrawideband (UWB) microwave signals recorded across an antenna array. Knowing the location of the breast surface can improve imaging performance if incorporated as a priori information into recently proposed microwave imaging algorithms. These techniques transmit low-power microwaves into the breast using an antenna array, which in turn measures the scattered microwave signals for the purpose of detecting anomalies or changes in the dielectric properties of breast tissue. Our proposed surface identification algorithm consists of three procedures, the first of which estimates M points on the breast surface given M channels of measured microwave backscatter data. The second procedure applies interpolation and extrapolation to these M points to generate N > M points that are approximately uniformly distributed over the breast surface, while the third procedure uses these N points to generate a 3-D estimated breast surface. Numerical as well as experimental tests indicate that the maximum absolute error in the estimated surface generated by the algorithm is on the order of several millimeters. An error analysis conducted for a basic microwave radar imaging algorithm (least-squares narrowband beamforming) indicates that this level of error is acceptable. A key advantage of the algorithm is that it uses the same measured signals that are used for UWB microwave imaging, thereby minimizing patient scan time and avoiding the need for additional hardware. [ABSTRACT FROM AUTHOR]

One of the major reasons why the elderly lose their ability to live independently at home is the decline in gait performance. A measure to assess gait performance using accelerometers is step counting. The main problem with most step detection algorithms is the loss of accuracy at low speeds ($<$ 0.8 m/s) which limits their use in frail elderly populations. In this paper, a step detection algorithm was developed and validated using data from 10 healthy adults and 21 institutionalized seniors, predominantly frail older adults. Data were recorded using a single waist-worn triaxial accelerometer as each of the subjects performed one 10-m-walk trial. The algorithm demonstrated high mean sensitivity (99 $\pm$ 1%) for gait speeds between 0.2–1.5 m/s. False positives were evaluated with a series of motion activities performed by one subject. These activities simulate acceleration patterns similar to those generated near the body's center of mass while walking in terms of amplitude signal and periodicity. Cycling was the activity which led to a higher number of false positives. By applying template matching, we reduced by $73\%$ the number of false positives in the cycling activity and eliminated all false positives in the rest of activities. Using K-means clustering, we obtained two different characteristic step patterns, one for normal and one for frail walking, where particular gait events related to limb impacts and muscle flexions were recognized. The proposed system can help to identify seniors at high risk of functional decline and monitor the progress of patients undergoing exercise therapy interventions. [ABSTRACT FROM AUTHOR]

Pulse transit time (PTT) has attracted much interest for cuffless blood pressure (BP) measurement. However, its limited accuracy is one of the main problems preventing its widespread acceptance. Arterial BP oscillates mainly at high frequency (HF) because of respiratory activity, and at low frequency (LF) because of vasomotor tone. Prior studies suggested that PTT can track BP variation in HF range, but was inadequate to follow the LF variation, which is probably the main reason for its unsatisfactory accuracy. This paper presents a new indicator, the photoplethysmogram intensity ratio (PIR), which can be affected by changes in the arterial diameter, and, thus, trace the LF variation of BP. Spectral analysis of BP, PTT, PIR, and respiratory signal confirmed that PTT was related to BP in HF at the respiratory frequency, while PIR was associated with BP in LF range. We, therefore, develop a novel BP estimation algorithm by using both PTT and PIR. The proposed algorithm was validated on 27 healthy subjects with continuous Finapres BP as reference. The results showed that the mean ± standard deviation (SD) for the estimated systolic, diastolic, and mean BP with the proposed method against reference were $-0.37 \pm 5.21$, $-0.08 \pm 4.06$, $-0.18 \pm 4.13$ mmHg, and mean absolute difference (MAD) were 4.09, 3.18, 3.18 mmHg, respectively. Furthermore, the proposed method outperformed the two most cited PTT algorithms for about 2 mmHg in SD and MAD. These results demonstrated that the proposed BP model using PIR and PTT can estimate continuous BP with improved accuracy. [ABSTRACT FROM AUTHOR]

Physical rehabilitation supported by the computer-assisted-interface is gaining popularity among health-care fraternity. In this paper, we have proposed a computer-vision-assisted contactless methodology to facilitate palm and finger rehabilitation. Leap motion controller has been interfaced with a computing device to record parameters describing 3-D movements of the palm of a user undergoing rehabilitation. We have proposed an interface using Unity3D development platform. Our interface is capable of analyzing intermediate steps of rehabilitation without the help of an expert, and it can provide online feedback to the user. Isolated gestures are classified using linear discriminant analysis (DA) and support vector machines (SVM). Finally, a set of discrete hidden Markov models (HMM) have been used to classify gesture sequence performed during rehabilitation. Experimental validation using a large number of samples collected from healthy volunteers reveals that DA and SVM perform similarly while applied on isolated gesture recognition. We have compared the results of HMM-based sequence classification with CRF-based techniques. Our results confirm that both HMM and CRF perform quite similarly when tested on gesture sequences. The proposed system can be used for home-based palm or finger rehabilitation in the absence of experts. [ABSTRACT FROM AUTHOR]

Retrieving medical images that present similar diseases is an active research area for diagnostics and therapy. However, it can be problematic given the visual variations between anatomical structures. In this paper, we propose a new feature extraction method for similarity computation in medical imaging. Instead of the low-level visual appearance, we design a CCA-PairLDA feature representation method to capture the similarity between images with high-level semantics. First, we extract the PairLDA topics to represent an image as a mixture of latent semantic topics in an image pair context. Second, we generate a CCA-correlation model to represent the semantic association between an image pair for similarity computation. While PairLDA adjusts the latent topics for all image pairs, CCA-correlation helps to associate an individual image pair. In this way, the semantic descriptions of an image pair are closely correlated, and naturally correspond to similarity computation between images. We evaluated our method on two public medical imaging datasets for image retrieval and showed improved performance. [ABSTRACT FROM AUTHOR]

Widely used medical imaging systems in clinics currently rely on X-rays, magnetic resonance imaging, ultrasound, computed tomography, and positron emission tomography. The aforementioned technologies provide clinical data with a variety of resolution, implementation cost, and use complexity, where some of them rely on ionizing radiation. Microwave sensing and imaging (MSI) is an alternative method based on nonionizing electromagnetic (EM) signals operating over the frequency range covering hundreds of megahertz to tens of gigahertz. The advantages of using EM signals are low health risk, low cost implementation, low operational cost, ease of use, and user friendliness. Advancements made in microelectronics, material science, and embedded systems make it possible for miniaturization and integration into portable, handheld, mobile devices with networking capability. MSI has been used for tumor detection, blood clot/stroke detection, heart imaging, bone imaging, cancer detection, and localization of in-body RF sources. The fundamental notion of MSI is that it exploits the tissue-dependent dielectric contrast to reconstruct signals and images using radar-based or tomographic imaging techniques. This paper presents a comprehensive overview of the active MSI for various medical applications, for which the motivation, challenges, possible solutions, and future directions are discussed. [ABSTRACT FROM PUBLISHER]

Mosaicing is a commonly used technique in many medical imaging applications where subimages are stitched together in order to obtain a larger field of view. However, stitching, which involves alignment or registration in overlapping regions, is often challenging when the information shared by subimages is absent or small. While it is not possible to perform an alignment without overlap using existing techniques, imaging artifacts such as distortions towards image boundaries present further complications during registration by decreasing the reliability of available information. Without taking these into consideration, a registration approach might violate the continuity and the smoothness of structures across subimages. In this paper, we propose a novel registration approach for the stitching of subimages in such challenging scenarios. By using a perceptual grouping approach, we extend subimages beyond their boundaries by propagating available structures in order to obtain structural maps in the extended regions. These maps are then used to establish correspondences between subimages when the shared information is absent, small or unreliable. Using our approach ensures the continuity and the smoothness of structures across subimage boundaries. Furthermore, since only structures are used, the proposed method can also be used for the stitching of multi-modal images. Our approach is unique in that it also enables contactless stitching. We demonstrate the effectiveness of the proposed method by performing several experiments on synthetic and medical images. Moreover, we show how stitching is possible in the presence of a physical gap between subimages. [ABSTRACT FROM PUBLISHER]

This paper presents a method for localizing and labeling the lumbar vertebrae and intervertebral discs in mid-sagittal MR image slices. The approach is based on a Markov-chain-like graphical model of the ordered discs and vertebrae in the lumbar spine. The graphical model is formulated by combining local image features and semiglobal geometrical information. The local image features are extracted from the image by employing pyramidal histogram of oriented gradients (PHOG) and a novel descriptor that we call image projection descriptor (IPD). These features are trained with support vector machines (SVM) and each pixel in the target image is locally assigned a score. These local scores are combined with the semiglobal geometrical information like the distance ratio and angle between the neighboring structures under the Markov random field (MRF) framework. An exact localization of discs and vertebrae is inferred from the MRF by finding a maximum a posteriori solution efficiently using dynamic programming. As a result of the novel features introduced, our system can scale-invariantly localize discs and vertebra at the same time even in the existence of missing structures. The proposed system is tested and validated on a clinical lumbar spine MR image dataset containing 80 subjects of which 64 have disc- and vertebra-related diseases and abnormalities. The experiments show that our system is successful even in abnormal cases and our results are comparable to the state of the art. [ABSTRACT FROM PUBLISHER]

In recent years, active microwave breast imaging is increasingly being viewed as a promising complementary imaging modality for cancer detection. In this paper, we present a novel deformable reflector microwave tomography technique for noninvasive characterization of the breast tissue. In contrast to conventional multitransceiver designs, the proposed technique utilizes a continuously deformable reflector with metallic coating to acquire field measurements for imaging. Computational feasibility of the proposed technique to image heterogeneous dielectric tissue property is evaluated using simplified 2-D breast models. The robustness of the deformable reflector-based tomography technique in imaging the spatial distribution of the tissue dielectric property in the presence of measurement noise is investigated using first-order Tikhonov regularization. Preliminary results obtained for the 2-D breast models appear promising and indicate further investigation of the new microwave tomography technique for breast imaging. [ABSTRACT FROM AUTHOR]

Characterization of architectural tissue features such as the shape, margin, and size of a suspicious lesion is commonly performed in conjunction with medical imaging to provide clues about the nature of an abnormality. In this paper, we numerically investigate the feasibility of using multichannel microwave backscatter in the 1-11 GHz band to classify the salient features of a dielectric target. We consider targets with three shape characteristics: smooth, microlobulated, and spiculated; and four size categories ranging from 0.5 to 2 cm in diameter. The numerical target constructs are based on Gaussian random spheres allowing for moderate shape irregularities. We perform shape and size classification for a range of signal-to-noise ratios (SNRs) to demonstrate the potential for tumor characterization based on ultrawideband (UWB) microwave backscatter. We approach classification with two basis selection methods from the literature: local discriminant bases and principal component analysis. Using these methods, we construct linear classifiers where a subset of the bases expansion vectors are the input features and we evaluate the average rate of correct classification as a performance measure. We demonstrate that for 10 dB SNR, the target size is very reliably classified with over 97% accuracy averaged over 360 targets; target shape is classified with over 70% accuracy. The relationship between the SNR of the test data and classifier performance is also explored. The results of this study are very encouraging and suggest that both shape and size characteristics of a dielectric target can be classified directly from its UWB backscatter. Hence, characterization can easily be performed in conjunction with UWB radar-based breast cancer detection without requiring any special hardware or additional data collection. [ABSTRACT FROM AUTHOR]

Microwave-induced thermal acoustic imaging (TAI) is a promising early breast cancer detection technique, which combines the advantages of microwave stimulation and ultrasound imaging and offers a high imaging contrast, as well as high spatial resolution at the same time. A new multifrequency microwave-induced thermal acoustic imaging scheme for early breast cancer detection is proposed in this paper. Significantly more information about the human breast can be gathered using multiple frequency microwave stimulation. A multifrequency adaptive and robust technique (MART) is presented for image formation. Due to its data-adaptive nature, MART can achieve better resolution and better interference rejection capability than its data-independent counterparts, such as the delay-and-sum method. The effectiveness of this procedure is shown by several numerical examples based on 2-D breast models. The finite-difference time-domain method is used to simulate the electromagnetic field distribution, the absorbed microwave energy density, and the thermal acoustic field in the breast model. [ABSTRACT FROM AUTHOR]

Simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) has been studied to identify areas related to EEG events. EEG data recorded in the magnetic resonance (MR) scanner with MR imaging is suffered from two specific artifacts, imaging artifact, and ballistocardiogram (BCG). In this paper, we focus on BCG. In preceding studies, average subtraction was often used for this purpose. However, average subtraction requires an assumption that BCG waveforms are precisely periodic, which seems unrealistic because BCG is a biomedical artifact. We propose the application of independent component analysis (ICA) with a postprocessing of high-pass filtering for the removal of BCG. With this approach, it is not necessary to assume that the BCG waveform is periodic. Empirically, we show that our proposed method removes BCG artifacts as well as does the average subtraction method. Power spectral density analysis of the two approaches shows that, with ICA, distortion of recovered EEG data is also as small as that associated with the average subtraction approach. We also propose a hypothesis for how head movement causes BCGs and show why ICA can remove BCG artifacts arising from this source. [ABSTRACT FROM AUTHOR]

This paper presents an in vivo validation of a method for the three-dimensional (3-D) high-resolution modeling of the human spine, rib cage, and pelvis for the study of spinal deformities. The method uses an adaptation of a standard close-range photogrammetry method called direct linear transformation to reconstruct the 3-D coordinates of anatomical landmarks from three radiographic images of the subject's trunk. It then deforms in 3-D 1-mm-resolution anatomical primitives (reference bones) obtained by serial computed tomography-scan reconstruction of a dry specimen. The free-form deformation is calculated using dual kriging equations. In vivo validation of this method on 40 scoliotic vertebrae gives an overall accuracy of 3.3 ± 3.8 mm, making it an adequate tool for clinical studies and mechanical analysis purposes. [ABSTRACT FROM AUTHOR]

Provides instructions and guidelines to prospective authors who wish to submit manuscripts. [ABSTRACT FROM PUBLISHER]

Metal implants such as hip prostheses and dental fillings produce streak and star artifacts in the reconstructed computed tomography (CT) images. Due to these artifacts, the CT image may not be diagnostically usable. A new reconstruction procedure is proposed that reduces the streak artifacts and that might improve the diagnostic value of the CT images. The procedure starts with a maximum a posteriori (MAP) reconstruction using an iterative reconstruction algorithm and a multimodal prior. This produces an artifact-free constrained image. This constrained image is the basis for an image-based projection completion procedure. The algorithm was validated on simulations, phantom and patient data, and compared with other metal artifact reduction algorithms. [ABSTRACT FROM AUTHOR]

A fast algorithm based on the nonlinear dynamical model for the electrocardiogram (ECG) is presented for the precise extraction of the characteristic points of these signals with baseline drift. Using the adaptive bionic wavelet transform, the baseline wander is removed efficiently. In fact by the means of the bionic wavelet transform, the resolution in the time-frequency domain can be adaptively adjusted not only by the signal frequency but also by the signal instantaneous amplitude and its first-order differential, which results in a better baseline wander cancellation. At the next step the parameters of the model are chosen to have the least square error with the original ECG. Determining the precise position of the waveforms of an ECG signal with baseline wander is complicated due to the varying amplitudes of its waveforms, the ambiguous and changing form of the complex and the unknown drift. A model-based approach handles these complications, therefore a method based on this concept has been developed and the fiducial points are accurately detected using the center and spread parameters of Gaussian-functions of the model. Simulation results show that the proposed method has an average sensitivity of 99.58%, average detection accuracy of 99.64%, and specificity of 100%. [ABSTRACT FROM AUTHOR]

An in vivo method for joint kinematics visualization and analysis is described. Low-dose computed tomography allowed three—dimensional joint modeling, and electrogoniometry collected joint kinematic data. Data registration occurred using palpated anatomical landmarks to obtain interactive computer joint simulation. The method was applied on one volunteer's ankle, and reproducibility was tested (maximal discrepancy: 3.6 deg and 5.5 mm for rotation and translation respectively). [ABSTRACT FROM AUTHOR]

Describes the above-named upcoming conference event. May include topics to be covered or calls for papers. [ABSTRACT FROM AUTHOR]

Accurate lung segmentation from large-size 3-D chest-computed tomography images is crucial for computer-assisted cancer diagnostics. To efficiently segment a 3-D lung, we extract voxel-wise features of spatial image contexts by unsupervised learning with a proposed incremental constrained nonnegative matrix factorization (ICNMF). The method applies smoothness constraints to learn the features, which are more robust to lung tissue inhomogeneities, and thus, help to better segment internal lung pathologies than the known state-of-the-art techniques. Compared to the latter, the ICNMF depends less on the domain expert knowledge and is more easily tuned due to only a few control parameters. Also, the proposed slice-wise incremental learning with due regard for interslice signal dependencies decreases the computational complexity of the NMF-based segmentation and is scalable to very large 3-D lung images. The method is quantitatively validated on simulated realistic lung phantoms that mimic different lung pathologies (seven datasets), in vivo datasets for 17 subjects, and 55 datasets from the Lobe and Lung Analysis 2011 (LOLA11) study. For the in vivo data, the accuracy of our segmentation w.r.t. the ground truth is 0.96 by the Dice similarity coefficient, 9.0 mm by the modified Hausdorff distance, and 0.87% by the absolute lung volume difference, which is significantly better than for the NMF-based segmentation. In spite of not being designed for lungs with severe pathologies and of no agreement between radiologists on the ground truth in such cases, the ICNMF with its total accuracy of 0.965 was ranked fifth among all others in the LOLA11. After excluding the nine too pathological cases from the LOLA11 dataset, the ICNMF accuracy increased to 0.986. [ABSTRACT FROM AUTHOR]

Correct assessment of bradykinesia is a key element in the diagnosis and monitoring of Parkinson's disease. Its evaluation is based on a careful assessment of symptoms and it is quantified using rating scales, where the Movement Disorders Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is the gold standard. Regardless of their importance, the bradykinesia-related items show low agreement between different evaluators. In this study, we design an applicable tool that provides an objective quantification of bradykinesia and that evaluates all characteristics described in the MDS-UPDRS. Twenty-five patients with Parkinson's disease performed three of the five bradykinesia-related items of the MDS-UPDRS. Their movements were assessed by four evaluators and were recorded with a nine degrees-of-freedom sensor. Sensor fusion was employed to obtain a 3-D representation of movements. Based on the resulting signals, a set of features related to the characteristics described in the MDS-UPDRS was defined. Feature selection methods were employed to determine the most important features to quantify bradykinesia. The features selected were used to train support vector machine classifiers to obtain an automatic score of the movements of each patient. The best results were obtained when seven features were included in the classifiers. The classification errors for finger tapping, diadochokinesis and toe tapping were 15–16.5%, 9.3–9.8%, and 18.2–20.2% smaller than the average interrater scoring error, respectively. The introduction of objective scoring in the assessment of bradykinesia might eliminate inconsistencies within evaluators and interrater assessment disagreements and might improve the monitoring of movement disorders. [ABSTRACT FROM AUTHOR]

Objectives: The development of neurorehabilitation technologies requires the profound understanding of the mechanisms underlying an individual's motor ability and impairment. A major factor limiting this understanding is the difficulty of bridging between events taking place at the neurophysiologic level (i.e., motor neuron firings) with those emerging at the musculoskeletal level (i.e. joint actuation), in vivo in the intact moving human. This review presents emerging model-based methodologies for filling this gap that are promising for developing clinically viable technologies. Methods: We provide a design overview of musculoskeletal modeling formulations driven by recordings of neuromuscular activity with a critical comparison to alternative model-free approaches in the context of neurorehabilitation technologies. We present advanced electromyography-based techniques for interfacing with the human nervous system and model-based techniques for translating the extracted neural information into estimates of motor function. Results: We introduce representative application areas where modeling is relevant for accessing neuromuscular variables that could not be measured experimentally. We then show how these variables are used for designing personalized rehabilitation interventions, biologically inspired limbs, and human–machine interfaces. Conclusion: The ability of using electrophysiological recordings to inform biomechanical models enables accessing a broader range of neuromechanical variables than analyzing electrophysiological data or movement data individually. This enables understanding the neuromechanical interplay underlying in vivo movement function, pathology, and robot-assisted motor recovery. Significance: Filling the gap between our understandings of movement neural and mechanical functions is central for addressing one of the major challenges in neurorehabilitation: personalizing current technologies and interventions to an individual's anatomy and impairment. [ABSTRACT FROM AUTHOR]

Goal: We present the development of a bone-anchored port for the painless long-term hemodialytic treatment of patients with renal failure. This port is implanted behind the ear. Methods: The port was developed based on knowledge obtained from long-term experience with implantable hearing devices, which are firmly anchored to the bone behind the ear. This concept of bone anchoring was adapted to the requirements for a vascular access during hemodialysis. The investigational device is comprised of a base plate that is firmly fixed with bone screws to the bone behind the ear (temporal bone). A catheter leads from the base plate valve block through the internal jugular vein and into the right atrium. The valves are opened using a special disposable adapter, without any need to puncture the blood vessels. Between hemodialysis sessions, the port is protected with a disposable cover. Results: Flow rate, leak tightness, and purification were tested on mockups. Preoperative planning and the surgical procedure were verified in 15 anatomical human whole head specimens. Conclusion: Preclinical evaluations demonstrated the technical feasibility and safety of the investigational device. Significance: Approximately 1.5 million people are treated with hemodialysis worldwide, and 25% of the overall cost of dialysis therapy results from vascular access problems. New approaches toward enhancing vascular access could potentially reduce the costs and complications of hemodialytic therapy. [ABSTRACT FROM AUTHOR]

Goal: Development of a new medical device class generally termed robotic capsule endoscopes (RCE) is currently being pursued by multiple research groups. These maneuverable devices will allow minimally invasive diagnosis and treatment of intestinal pathologies. While the intraluminal pressures related to the migrating motor complex (MMC) are well understood, no previous study has measured the active contact forces exerted by the human small bowel wall on a solid, or near solid bolus such as an RCE. Understanding and quantifying the active contact force are critical for the advancement of RCE technology. Methods: In this study, the authors develop a novel manometric contact force sensor for human studies and validate the feasibility of the design, sterilization method, and minimally invasive surgical procedure in a multianimal study, followed by a multihuman study. Results: Four porcine tests of the sensor were conducted. The mean porcine myenteric contact force measured using the new sensor is 1.20 ± 0.08 N·cm−1. The mean myenteric contact force recorded for all five human test subjects is 0.18 ± 0.33 N·cm−1. Conclusion: This study demonstrates the feasibility of operating an MMC force sensor in a live human with a minimally invasive surgical technique and presents force data necessary for RCE design. Significance: This study represents the first known myenteric contact force measurements on a solid bolus in the human small intestine. [ABSTRACT FROM AUTHOR]

Goal: This research introduces an original method for the design of MRI gradient coils that reduces secondary field effects created by eddy current coupling. The method is able to deal with asymmetric coils and provides a new way to ensure a reduction in the magnitude of the eddy current induced fields. Methods: New constraints are introduced at the surface of passive objects to bind the normal field component below a given value. This value is determined by first treating the passive surface as an active surface, and then, calculating the ideal stream function on that surface to produce the desired secondary field. Two coils were designed, one to image the knee and the other to image the head and neck. Results: The secondary field was analyzed using linear regression and was found to improve the secondary field from 10.41 to 0.498 mT/m and from 7.84 to 0.286 mT/m in the examples used. The power loss in the passive structure also decreased to below 1% of the original value using the new method. Conclusion: The method shows the ability to constrain the field to values below the minimum seen under the traditional approaches. Significance: This will allow the design of asymmetric systems with highly linear, reduced magnitude of secondary fields and may lead to better image quality. [ABSTRACT FROM AUTHOR]

The goal of this study is to develop an automated algorithm to quantify background electroencephalography (EEG) dynamics in term neonates with hypoxic ischemic encephalopathy. The recorded EEG signal is adaptively segmented and the segments with low amplitudes are detected. Next, depending on the spatial distribution of the low-amplitude segments, the first part of the algorithm detects (dynamic) interburst intervals (dIBIs) and performs well on the relatively artifact-free EEG periods and well-defined burst-suppression EEG periods. However, on testing the algorithm on EEG recordings of more than 48 h per neonate, a significant number of misclassified and dubious detections were encountered. Therefore, as the next step, we applied machine learning classifiers to differentiate between definite dIBI detections and misclassified ones. The developed algorithm achieved a true positive detection rate of 98%, 97%, 88%, and 95% for four duration-related dIBI groups that we subsequently defined. We benchmarked our algorithm with an expert diagnostic interpretation of EEG periods (1 h long) and demonstrated its effectiveness in clinical practice. We show that the detection algorithm effectively discriminates challenging cases encountered within mild and moderate background abnormalities. The dIBI detection algorithm improves identification of neonates with good clinical outcome as compared to the classification based on the classical burst-suppression interburst interval. [ABSTRACT FROM AUTHOR]

Objective: This study aims to develop a new quantitative image feature analysis scheme and investigate its role along with two genomic biomarkers, namely protein expression of the excision repair cross-complementing 1 genes and a regulatory subunit of ribonucleotide reductase (RRM1), in predicting cancer recurrence risk of stage I nonsmall-cell lung cancer (NSCLC) patients after surgery. Methods: By using chest computed tomography images, we developed a computer-aided detection scheme to segment lung tumors and computed tumor-related image features. After feature selection, we trained a Naïve Bayesian network-based classifier using eight image features and a multilayer perceptron classifier using two genomic biomarkers to predict cancer recurrence risk, respectively. Two classifiers were trained and tested using a dataset with 79 stage I NSCLC cases, a synthetic minority oversampling technique and a leave-one-case-out validation method. A fusion method was also applied to combine prediction scores of two classifiers. Results: Areas under ROC curves (AUC) values are 0.78 ± 0.06 and 0.68 ± 0.07 when using the image feature and genomic biomarker-based classifiers, respectively. AUC value significantly increased to 0.84 ± 0.05 ( $p < 0.05$) when fusion of two classifier-generated prediction scores using an equal weighting factor. Conclusion: A quantitative image feature-based classifier yielded significantly higher discriminatory power than a genomic biomarker-based classifier in predicting cancer recurrence risk. Fusion of prediction scores generated by the two classifiers further improved prediction performance. Significance: We demonstrated a new approach that has potential to assist clinicians in more effectively managing stage I NSCLC patients to reduce cancer recurrence risk. [ABSTRACT FROM AUTHOR]

Describes the above-named upcoming conference event. May include topics to be covered or calls for papers. [ABSTRACT FROM AUTHOR]

The usefulness of breast ultrasound could be extended by improving the detection of microcalcifications by being able to detect and enhance microcalcifications while simultaneously eliminating hyperechoic spots (e.g., speckle noise and fibrocystic changes) that can be mistaken for microcalcifications (i.e., false microcalcifications). This study investigated the use of a strain-compounding technique with speckle factor (SF) imaging to analyze the degree of scatterer redistributions in breast tissues under strain conditions for identifying microcalcifications and false microcalcifications. The efficacy of the proposed method was tested by collecting raw data of ultrasound backscattered signals from 26 lesions at BI-RADS category 4 or 5 with suspicious microcalcifications. The different strain conditions were created by applying manual compression to deform the breast lesion. For each region in which microcalcifications were suspected, estimates of the SNR of the strain-compounding B-scan images and estimates of the mean SF (SFavg) in the strain-compounding SF images were calculated. Compared with microcalcifications, the severity of speckle of the false microcalcifications would be easily degraded under compressive strain conditions. The results demonstrated that the SNR estimates in the strain-compounding B-scan images for microcalcifications and false microcalcifications were 5.22 ± 1.04 (mean ± standard deviation) and 4.62 ± 1.09, respectively; the corresponding SFavg estimates in the strain-compounding SF images were 0.47 ± 0.10 and 0.22 ± 0.10 (p < 0.01). The mean area under the receiver operating characteristic curve using the SNR estimate was 0.71, whereas that using the SFavg estimate was 0.94. These findings indicate that the strain-compounding SF imaging method is more effective at discriminating between microcalcifications and false microcalcifications. [ABSTRACT FROM AUTHOR]

Real-time ultrasound image acquisition is a pivotal resource in the medical community, in spite of its limited image quality. This poses challenges to image registration methods, particularly to those driven by intensity values. We address these difficulties in a novel diffeomorphic registration technique for tumor tracking in series of 2-D liver ultrasound. Our method has two main characteristics: 1) each voxel is described by three image features: intensity, local phase, and phase congruency; 2) we compute a set of forces from either local information (Demons-type of forces), or spatial correspondences supplied by a block-matching scheme, from each image feature. A family of update deformation fields which are defined by these forces, and inform upon the local or regional contribution of each image feature are then composed to form the final transformation. The method is diffeomorphic, which ensures the invertibility of deformations. The qualitative and quantitative results yielded by both synthetic and real clinical data show the suitability of our method for the application at hand. [ABSTRACT FROM PUBLISHER]

Labor and delivery are routinely monitored electronically with sensors that measure and record maternal uterine pressure (UP) and fetal heart rate (FHR), a procedure referred to as cardiotocography (CTG). Delay or failure to recognize abnormal patterns in these recordings can result in a failure to prevent fetal injury. We address the challenging problem of interpreting intrapartum CTG in a novel way by modeling the dynamic relationship between UP (as an input) and FHR (as an output). We use a nonparametric approach to estimate the dynamics in terms of an impulse response function (IRF). We apply singular value decomposition to suppress noise, IRF delay, and memory estimation to identify the temporal extent of the response and surrogate testing to assess model significance. We construct models for a database of CTG recordings labeled by outcome, and compare the models during the last 3 h of labor as well as across outcome classes. The results demonstrate that the UP-FHR dynamics can be successfully modeled as an input-output system. Models for pathological cases had stronger, more delayed, and more predictable responses than those for normal cases. In addition, the models evolved in time, reflecting a clinically plausible evolution of the fetal state due to the stress of labor. [ABSTRACT FROM AUTHOR]

Bio-electric impedance signatures arise primarily from differences in cellular morphologies within an organ and can be used to differentiate benign and malignant pathologies, specifically in the breast. Electrical impedance tomography (EIT) is an imaging modality that determines the impedance distribution within tissue and has been used in prior work to map the electrical properties of breast at signal frequencies ranging from a few kHz to 1 MHz. It has been suggested that by extending the frequency range, additional information of clinical significance may be obtained. We have, therefore, developed a new EIT system for breast imaging which covers the frequency range from 10 kHz to 10 MHz. The instrument developed here is a distributed processor tomograph with 64 channels, capable of generating and measuring voltages and currents. Electrical benchmarking has shown the system to have a SNR greater than 94 dB up to 2 MHz, 90 dB up to 7 MHz, and 65 dB at 10 MHz. In addition, the system measures impedances to an accuracy of 99.7 % and has channel-to-channel variations of less than 0.05 %. Phantom imaging has demonstrated the ability to image across the entire frequency range in both single- and multiplane configurations. Further, 96 women have participated safely in breast exams with the system and the associated conductivity spectra obtained from 3-D image reconstructions range from 0.0237 S/m at 10 kHz to 0.2174 S/m at 10 MHz. These findings are consistent with impedance values reported in the literature. [ABSTRACT FROM AUTHOR]

Cancers located on the internal wall of bladders can be detected in image sequences acquired with endoscopes. The clinical diagnosis and follow-up can be facilitated by building a unique panoramic image of the bladder with the images acquired from different viewpoints. This process, called image mosaicing, consists of two steps. In the first step, consecutive images are pairwise registered to find the local transformation matrices linking geometrically consecutive images. In the second step, all images are placed in a common and global coordinate system. In this contribution, a mutual information-based similarity measure and a stochastic gradient optimization method were implemented in the registration process. However, the images have to be preprocessed in order to register the data in a robust way. Thus, a simple correction method of the distortions affecting endoscopic images is presented. After the placement of all images in the global coordinate system, the parameters of the local transformation matrices are all adjusted to improve the visual aspect of the panoramic images. Phantoms are used to evaluate the global mosaicing accuracy and the limits of the registration algorithm. The mean distances between ground truth positions in the mosaiced image range typically in 1-3 pixels. Results given for in vivo patient data illustrate the ability of the algorithm to give coherent panoramic images in the case of bladders. [ABSTRACT FROM AUTHOR]

A new ultrawideband (UWB) microwave method to estimate tumor size based upon detection of the tumor/liver interface is proposed. This method involves monitoring the response of a broadband pulse launched down a coaxial treatment antenna and radiated into the tumor. By monitoring the peak in the returned signal, and estimating the propagation velocity within the tumor, the location of the tumor/liver interface can be determined and the size of a spherical lesion estimated. The feasibility of this technique is demonstrated by finite element (FE) electromagnetic simulations of a spherical tumor in the liver. Robustness to noise is also investigated as well as the effects of insertion depth. The promising outcome of this feasibility study suggests that further development of this technique should be pursued. [ABSTRACT FROM AUTHOR]

A novel power-line interference (PLI) detection and suppression algorithm is presented to preprocess the electrocardiogram (ECG) signals. A distinct feature of this proposed algorithm is its ability to detect the presence of PLI in the ECG signal before applying the PLI suppression algorithm. No PLI suppression operation will be performed if PLI is not detected. We propose a PLI detector that employs an optimal linear discriminant analysis (LDA) algorithm to make a decision for the PLI presence. An efficient recursive least-squares (RLS) adaptive notch filter is also developed to serve the purpose of PLI suppression. Experimental results demonstrate superior performance of this proposed algorithm. [ABSTRACT FROM AUTHOR]

Several important clinical applications depend on accurate ultrasound image frame-to-frame motion estimation. Assuming that there is a degree of finite noise in the image frames and that speckle partially decorrelates between successive frames during freehand scanning, we hypothesize that an optimal inter-frame interval (step size) must exist that provides the smallest relative dimensional error over a set of accumulated motion estimates. Smaller frame increments suffer from less decorrelation-related inaccuracy but present greater potential for cumulative error because more estimates are used over any specific dimensional interval. We studied these effects using a combination of theoretical modeling, numerical simulation, and experiments. Components of diagonal motion due to the limitations of manual transducer movement were considered as the cause of decorrelation. The results were examined for four different angles of the diagonal motion and two different signal-to-noise ratio (SNR) values. These indicate that an optimal step size does exist and that this is dependent on many variables including SNR, angle of the diagonal motion, transducer geometry, lens focusing parameters, transducer operating frequency, and beamforming parameters. In practical experiments, we found that the optimal step size generally required using every available image frame rather than 'skipping' any intermediate frames. [ABSTRACT FROM AUTHOR]

We present an object-oriented framework for the finite-element (FE)-based simulation of the human knee joint motion. The FE model of the knee joint is acquired from the patients in vivo by using magnetic resonance imaging. The MEl images are converted into a three-dimensional model and finally an all-hexahedral mesh for the FE analysis is generated. The simulation environment uses nonlinear finite-element analysis (FEA) and is capable of handling contact of the model to handle the complex rolling/sliding motion of the knee joint. The software strictly follows object-oriented concepts of software engineering in order to guarantee maximum extensibility and maintainability. The final goal of this work-in-progress is the creation of a computer-based biomechanical model of the knee joint which can be used in a variety of applications, ranging from prosthesis design and treatment planning (e.g., optimal reconstruction of ruptured ligaments) over surgical simulation to impact computations in crash-worthiness simulations. [ABSTRACT FROM AUTHOR]

Discusses an approach for medical image analysis. Information on sensor array conversion; Problem formulation; Details on independent component analysis.

Presents an imaging method based on the classical Hall effect (HE), which describes the origin of a detectable voltage from a conductive object moving in a magnetic field. Methodology used in study; How HE images were formed; Demonstration of the feasibility of the method; Discussion on the contrast mechanism and signal-to-noise issues.

Vessel extraction is one of the critical tasks in clinical practice. This communication presents a new approach for vessel extraction using a level-set-based active contour by defining a novel local term that takes local image contrast into account. The proposed model not only preserves the performance of the existing models on blurry images, but also overcomes their inability to handle nonuniform illumination. The efficacy of the approach is demonstrated with experiments involving both synthetic images and clinical angiograms. [ABSTRACT FROM AUTHOR]

This communication describes a fully automated method by which the position of the diaphragm surface can be estimated by deforming a thin-plate model to match the bottom surface of the lung in CT images. This method was applied to 338 X-ray CT scans, and its validity was proved by the experimental results. [ABSTRACT FROM AUTHOR]

Medical ultrasound systems are limited by a tradeoff between axial resolution and the maximum imaging depth which may be achieved. The technique of coded excitation has been used extensively in the field of RADAR and SONAR for some time, but has only relatively recently been exploited in the area of medical ultrasound. This technique is attractive because allows the relationship between the pulse length and the maximum achievable spatial resolution to be changed. The work presented here explores the possibility of using the fractional Fourier transform as an effective means for the processing of signals received after the transmission of linear frequency modulated chirps. Results are presented which demonstrate that this technique is able to offer spatial resolutions similar to those obtained with a single cycle duration signal. [ABSTRACT FROM AUTHOR]