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The purpose of this paper was to quickly design fixation plates for fractured proximal humerus and distal radius according to the requirements of surgical treatment. Therefore, a new method to quickly design cloverleaf plate appropriate for proximal humerus and volar plate appropriate for distal radius is put forward. First, three-dimensional (3D) reconstruction models of fractured proximal humerus and distal radius were generated based on deforming mean parametric models of proximal humerus and distal radius, respectively. Second, based on region-of-interest marked on the 3D reconstruction model of proximal humerus and distal radius, abutted surfaces of cloverleaf plate and volar plate were established, respectively. Then, parametric abutted surface was established after setting rational parameters for the surface of the cloverleaf plate. Parametric abutted surface of volar plate was established using the same method. Finally, parametric cloverleaf plate and volar plate are generated through thickening their respective parametric abutted surfaces. The parametric plates, acting as templates, accelerate and simplify the design process and therefore allow users to construct plate with editing valid parameters easily. Group of cloverleaf plates and volar plates with different sizes were generated quickly, showing that the proposed method is feasible and effective. [ABSTRACT FROM PUBLISHER]

Functional neuroimaging studies have identified several 'core' brain regions that are preferentially activated by scene stimuli, namely posterior parahippocampal gyrus (PHG), retrosplenial cortex (RSC), and transverse occipital sulcus (TOS). The hippocampus (HC), too, is thought to play a key role in scene processing, although no study has yet investigated scene-sensitivity in the HC relative to these other 'core' regions. Here, we characterised the frequency and consistency of individual scene-preferential responses within these regions by analysing a large dataset ( n = 51) in which participants performed a one-back working memory task for scenes, objects, and scrambled objects. An unbiased approach was adopted by applying independently-defined anatomical ROIs to individual-level functional data across different voxel-wise thresholds and spatial filters. It was found that the majority of subjects had preferential scene clusters in PHG (max = 100% of participants), RSC (max = 76%), and TOS (max = 94%). A comparable number of individuals also possessed significant scene-related clusters within their individually defined HC ROIs (max = 88%), evidencing a HC contribution to scene processing. While probabilistic overlap maps of individual clusters showed that overlap 'peaks' were close to those identified in group-level analyses (particularly for TOS and HC), inter-individual consistency varied across regions and statistical thresholds. The inter-regional and inter-individual variability revealed by these analyses has implications for how scene-sensitive cortex is localised and interrogated in functional neuroimaging studies, particularly in medial temporal lobe regions, such as the HC. Hum Brain Mapp 37:3779-3794, 2016. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The purpose of this study was to evaluate the diagnostic performances of semi-quantitative strain ratio measured by using one region-of-interest (ROI) on breast US elastography images. Two hundred one breast masses of 165 women (mean age: 47.2 y) were included. Ultrasonography (US) and elastography images of the masses were obtained and prospectively analyzed according to elasticity pattern, strain ratio, and final Breast Imaging-Reporting and Data System (BI-RADS) assessments. Of the 201 breast masses, 127 (63.2%) were benign and 74 (36.8%) were malignant. Elastography pattern and strain ratio (cut-off of 1.44) had significantly higher specificity than gray-scale US, 39.4% and 61.4% versus 29.1%, respectively (all p < 0.05). Area under the receiver operating characteristics curve (Az) was highest for gray-scale US (0.646), without statistical significances, than for elastography pattern (0.596, p = 0.159) or strain ratio (0.625, p = 0.610). Semi-quantitative strain ratio measured from one ROI has comparable diagnostic performances to gray-scale US, which may contribute to more accurate differential diagnosis of breast masses seen on US. [ABSTRACT FROM AUTHOR]

Region of interest (ROI) based image and video processing has attracted increased research efforts in recent years. The concept of perceptual ROI, however, is not always clearly defined leading to different interpretations between researchers related to bottom-up saliency (signal driven visual attention), top-down attention (subconscious, driven by higher cognitive factors, e.g. interest) or perceived interest. While all of these concepts are likely meaningful in the context of perceptual ROI based image and video processing, it is worth understanding how they are linked altogether. In this paper, the relationship between perceived interest and overt visual attention (which can cover both bottom-up and top-down attention) is studied. Towards this goal, a dedicated ROI selection experiment was performed and is analysed in detail, revealing deep insight into perceived interest in natural images. The outcomes are compared to an eye gaze tracking experiment representing overt visual attention in natural images. It is shown that there is indeed a strong relationship between perceived interest and overt visual attention for a wide range of natural scenes. We show that the relationship has a strong dependence on image content and presentation time during the eye gaze tracking experiment. Furthermore, eye gaze tracking data is revealed to have a high predictive value of primary ROI, particularly in case of the latter dominating over the remainder of the image. Both, the ROI and the eye gaze tracking databases are made publicly available to the research community. [ABSTRACT FROM AUTHOR]

The utility of the horizon for airborne sense-and-avoid (ABSAA) applications is explored in this work. The horizon is a feature boundary across which an airborne scene can be separated into surface and sky and serves as a salient, heading-independent feature that may be mapped into an electro-optical sensor. The virtual horizon as established in this paper represents the horizon that would be seen assuming a featureless earth model and infinite visibility and is distinct from the apparent horizon in an imaging sensor or the pilot's eye. For level flight, non-maneuvering collision course trajectories, it is expected that targets of interest will appear in close proximity to this virtual horizon. This paper presents a model for establishing the virtual horizon and its projection into a camera reference plane as part of the sensing element in an ABSAA system. Evaluation of the model was performed on a benchmark dataset of airborne collision geometries flown at the National Research Council (NRC) using the Cerberus camera array. The model was compared against ground truth flight test data collected using high accuracy inertial navigation systems aboard aircraft on several 'near-miss' intercepts. The paper establishes the concept of 'virtual horizon proximity' (VHP), the minimum distance from a detected target and the virtual horizon, and investigates the utility of using this metric as a means of rejecting false positive detections, and increasing range at first detection through the use of a region of interest (ROI) mask centred on the virtual horizon. The use of this horizon-centred ROI was shown to increase the range at first detection by an average factor of two, and was shown to reduce false positives for six popular feature detector algorithms applied across the suite of flight test imagery. [ABSTRACT FROM AUTHOR]

Parkinson’s disease (PD) is the second most common neurodegenerative disorder of the central nervous system. For its early management and accurate prognosis, there is a need to develop automated and non-invasive computer-aided diagnosis (CAD) technique(s). The present study proposes a novel region-of-interest (ROI) based CAD technique using T1-weighted magnetic resonance imaging (MRI) to discriminate PD patients from healthy subjects. A volumetric 3D T1-weighted (1 mm isovoxel) MRI of 30 PD patients and age & gender matched 30 healthy subjects is acquired on a 1.5 T MRI scanner. Five well-documented regions affected in PD, namely substantia nigra (SN), thalamus, hippocampus, frontal lobe (FL) and mid-brain are analyzed individually and in combinations of pairs and triplets. Features are constructed from gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) values of voxel from these regions. A small set of discriminating and non-redundant features is selected using mutual information based approach in conjunction with unpaired two-tailed two-sample t-test based ranking. A decision model is built with the help of support vector machine (SVM) as a classifier. The performance of the decision model is evaluated in terms of sensitivity, specificity and accuracy with leave-one-out cross-validation scheme. Experimental results demonstrate that the proposed method is able to differentiate PD from healthy subjects with a maximum accuracy of 86.67% with SN for GM and combination of SN & FL for WM; and outperforms the voxel-based morphometry method. Furthermore, loss in GM and WM volume and gain in CSF volume is observed in PD patients in comparison to healthy subjects. The excellent performance of the proposed method is beneficial for clinicians as it can be used as a decision support system which requires less time and efforts in diagnosing PD. In addition, it also encourages the application of CAD in medical domain. [ABSTRACT FROM AUTHOR]

The impact of perceptual relevance information on content aware image retargeting is investigated. We integrated fixation density maps and region-of-interest maps into a contemporary image retargeting algorithm to test the hypothesis that the latter result in superior performance given their object level representation. We performed an experimental study with human participants to evaluate the performance gains relative to benchmark conditions. The experiment revealed that the kind of perceptual relevance information, image content, and retargeting ratio all have a strong impact on the overall performance. Recorded response times provided further insight into the difficulty that people experienced when performing the assessment task. These findings are instrumental for the image retargeting research community to further improve their algorithms by augmenting content awareness with perceptual relevance. [ABSTRACT FROM AUTHOR]

For high bit rate video, it is important to acquire the video contents with high resolution, the quality of which may be degraded due to the motion blur from the movement of an object(s) or the camera. However, conventional sharpness assessments are designed to find focal blur caused either by defocusing or by compression distortion targeted for low bit rates. To overcome this limitation, we present a no-reference framework of a visual sharpness assessment (VSA) for high-resolution video based on the motion and scene classification. In the proposed framework, the accuracy of the sharpness estimation can be improved via pooling weighted by the visual perception from the object and camera movements and by the strong influence from the region with the highest sharpness. Based on the motion blur characteristics, the variance and the contrast over the spectral domain are used to quantify the perceived sharpness. Moreover, for the VSA, we extract the highly influential sharper regions and emphasize them by utilizing the scene adaptive pooling. Based on the subjective results, we demonstrate that the VSA can measure the video sharpness more accurately than other sharpness measurements for high-resolution video. [ABSTRACT FROM PUBLISHER]

AutoCAD solid models are increasingly recognized in geological and geotechnical 3D modeling. In order to bridge the currently existing gap between AutoCAD solid models and the grid modeling realm, a Visual LISP program is presented that converts AutoCAD solid models into voxel arrays. Acad2Vox voxelizer works on a 3 d -model that is made up of arbitrary non-overlapping 3D-solids. After definition of the target voxel array geometry, 3D-solids are scanned at grid positions and properties are streamed to an ASCII output file. Acad2Vox has a novel voxelization strategy that combines a hierarchical reduction of sampling dimensionality with an innovative use of AutoCAD-specific methods for a fast and memory-saving operation. Acad2Vox provides georeferenced, voxelized analogs of 3D design data that can act as regions-of-interest in later geostatistical modeling and simulation. The Supplement includes sample geological solid models with instructions for practical work with Acad2Vox. [ABSTRACT FROM AUTHOR]

Abstract: Objectives: To evaluate the performance of region-of-interest (ROI)-based MRI R2* measurements by using the first-moment noise-corrected model (M1NCM) to correct the non-central Chi noise in magnitude images from phased arrays for hepatic iron content (HIC) assessment. Methods: R2* values were quantified using the M1NCM model. Three approaches were employed to determine the representative R2*: fitting of the ROI-averaged signal (average-then-fit, ATF); outputting the median and mean of R2*s from the pixel-wise fitting of decay signals within the ROI (denoted as PWFmed and PWFmea, respectively). The accuracy and precision of the three approaches were evaluated on synthesized data. The agreement among these approaches and their intra- and inter-observer reproducibility were assessed on 105 thalassaemia major patients. Results: Simulations showed that ATF consistently yielded the highest accuracy and precision at varying noise levels. By contrast, PWFmed and PWFmea slightly and significantly overestimated high R2* at poor signal-to-noise ratios, respectively. Patient study showed that ATF agreed well with PWFmed, whereas PWFmea produced high R2* measurements for patients with severe HIC. No significant difference was observed in the reproducibility of the three approaches. Conclusions: PWFmea tends to overestimate high R2*, whereas ATF and PWFmed can produce more accurate R2* measurements for HIC assessment. [Copyright &y& Elsevier]

Highlights: [•] The ROI-based full frame warping outperforms the conventional full-frame warping. [•] The processing speed becomes drastically faster by applying CPW only to the ROI. [•] The overall visual quality is similar to that of the state-of-the art [5]. [Copyright &y& Elsevier]

Enhancement of the overall image contrast and sharpness of the image are the associated tasks to be performed in digital image processing. However, with the introduction of contrast enhancement it does not essentially leads to sharpness enhancement. The sharpness of an image, minute details has to be enhanced at the cost of enhancing noise as well. Existing variation models based upon Higher Order Derivatives (Variation-HOD) minimizes the curvature of all level lines in the image. With the application of an efficient minimization algorithm based upon the graph cuts model it failed to have the exact relationship on gradient flow. The curvature based models do not fit directly to exploit the max flow computation into the framework. Also Nonlinear Dynamic Range Adjustment (NDRA) method combined and updated the existing processing blocks but a comprehensive and efficient analysis of different chosen design parameters were not performed. The effective optimization technique did not implement the pre-processing steps to reduce the number of instructions, noise and improve the quality. To discover a work plan for conquering the noise defects, Intensity Histogram Equalization (IHE) method is proposed. IHE preprocess the image to remove the noise present in the image and enhance the image contrast for disparity enhancement and in that way introduces intensity to improve the brightness. The preprocessing in IHE method includes mask production, enlightenment equalization, and color normalization for efficient analysis of different chosen design parameters. Mask production labels the pixels, and Region-of-Interest (ROI) in the entire image excludes the background of the image to generate a binary image for each band. The histogram threshold rate was calculated using pixel value statistics for exact relationship maintenance on gradient flow. The experimental performance of IHE method is precise in terms of noise removal ratio, brightness quality efficiency, Max-flow computational intricacy, and false positive error. The noise removal ratio is approximately 11 % improved when compared with the Variation-HOD. [ABSTRACT FROM AUTHOR]

Abstract: Introduction: To assess impact of size of regions-of-interest (ROI) on differentiation of RCC and renal cysts using multi-phase CT, with focus on differentiating papillary RCC (pRCC) and cysts given known hypovascularity of pRCC. Methods: 99 renal lesions (23 pRCC, 47 clear-cell RCC, 7 chromophobe RCC, 22 cysts) underwent multi-phase CT. Subjective presence of visual enhancement was recorded for each lesion. Whole-lesion (WL) ROIs, and small (≤5mm2), medium (average size of small and large ROIs), and large (half of lesion diameter) peripherally located partial-lesion (PL) ROIs, were placed on non-contrast and nephrographic phases. Impact of ROI size in separating cysts from all RCC and from pRCC based on increased attenuation between phases was assessed using ROC analysis. Results: Visual enhancement was perceived in 96% of ccRCC, 61% of pRCC, and 9% of cysts. AUCs for separating all RCC and cysts for WL-ROI and small, medium, and large PL-ROIs were 91%, 96%, 91% and 93%, and among lesions without visible enhancement were 60%, 79%, 67% and 67%. AUCs for separating pRCC and cysts for WL-ROI and small, medium, and large PL-ROIs were 78%, 92%, 82% and 84%, and among lesions without visible enhancement were 64%, 88%, 69% and 69%. Conclusion: Small PL-ROIs had higher accuracy than WL-ROI or other PL-ROIs in separating RCC from cysts, with greater impact in differentiating pRCC from cysts and differentiating lesions without visible enhancement. Thus, when evaluating renal lesions using multi-phase CT, we suggest placing small peripheral ROIs for highest accuracy in distinguishing renal malignancy and benign cysts. [Copyright &y& Elsevier]

Highlights: [•] Perceptual image assessment is proposed for coder performance evaluation. [•] Proposed assessment uses a linear combination of perceptual measures just based on features. [•] Region-of-Interest coder perceptual evaluation aims at identifying coder behavior. [•] Some perceptual assessments are adequate to evaluate test coders. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes a numerical method for refining the image of a region-of-interest (RoI) within an existing tomographic slice, provided that projection data are stored along with the image. Using the attributes of the image, projection values (ray-sums) are adjusted to compensate for the material outside the RoI. Advantage is taken of the high degree of overdetermination of common computed tomography systems to reconstruct an RoI image over smaller pixels. The smaller size of a region-of-interest enables the use of iterative methods for RoI image reconstruction, which are less prone to error propagation. Simulation results are shown for an anthropomorphic head phantom, demonstrating that the introduced approach enhances both the spatial resolution and material contrast of RoI images; without the need to acquire any additional measurements or to alter existing imaging setups and systems. [Copyright &y& Elsevier]

Abstract: The ability to interact with the readout mechanism of contemporary image sensors offers new possibilities that can be utilized to improve the performance of digital particle image velocimetry (DPIV) systems. For instance, an intelligent selective region-of-interest (ROI) option can increase the frame rate of a camera, which would allow flow assessment of higher speeds thus ameliorate the dynamic velocity range (DVR) of a DPIV system. However, ROI increases the frame rate by narrowing down the field-of-view (FOV) of the PIV system, which prevents embracing the complete field of flow. To overcome this constraint and maintain the full FOV, we propose a technique, where a fixed height ROI strip is slid over a sensor array and partial flow fields corresponding to each ROI position are obtained. Then, the overall flow field is reconstructed by stitching these partial flow fields. We demonstrate an application of the proposed method by a series of experiments with laminar flows inside a rectangular microchannel. Specifically, we show that by applying the proposed technique the maximum assessable flow velocity by an ordinary DPIV system can be increased at least 4 times without any extra hardware investment. [Copyright &y& Elsevier]

Abstract: For an effective and efficient visual recognition system, the region-of-interest extraction of users is one of most important image processing duties. Generally, the position of the pupil in an eyeball is directly related to the user''s interest in an input visual image. However, when using a monocular eye tracking system, it is not easy to discern an accurate position of the region-of-interest of the user in a three-dimensional space. In this paper, an eye tracking system based on binocular gaze detection is presented. The proposed system is designed as a wearable device with three mini-cameras and two hot-mirrors for users to see through. The two eye monitoring system acquires the pupil images of both eyes through the hot-mirrors illuminated by infrared LEDs; the front-view camera acquires the image visible to the user. The experiment results show that the proposed system improves upon the region-of-interest localization error of the users in both the horizontal and vertical directions for the front-view images acquired by the user. Through the proposed system, it became possible to accurately extract the user''s region-of-interest, and so can be used to improve the image processing speed through focused information processing for the region-of-interest and narrow the selective information acquisition needed for users. [Copyright &y& Elsevier]

Objectives: To create and evaluate an interactive software tool for measuring imaging data in situations where hand-drawn region-of-interest measurements are unfeasible, for example, when the structure of interest is patchy with ill-defined boundaries. Methods: An interactive grid overlay software tool was implemented that enabled coding of voxels dependent on their imaging appearance with a series of user-defined classes. The Grid Analysis Tool (GAT) was designed to automatically extract quantitative imaging data, grouping the results by tissue class. Inter- and intra-observer reproducibility was evaluated by six observers of various backgrounds in a study of acute stroke patients. Results: The software tool enabled a more detailed classification of the stroke lesion than would be possible with a region-of-interest approach. However, inter-observer coefficients of variation (CVs) were relatively high, reaching 70% in 'possibly abnormal' tissue and around 15-20% in normal appearing tissues, while intra-observer CVs were no more than 13% in 'possibly abnormal' tissue and generally less than 1% in normal-appearing tissues. Conclusions: The grid-overlay method overcomes some of the limitations of conventional Region Of Interest (ROI) approaches, providing a viable alternative for segmenting patchy lesions with ill-defined boundaries, but care is required to ensure acceptable reproducibility if the method is applied by multiple observers. Key Points: • Computer software developed to overcome limitations of conventional regions of interest measurements • This software is suitable for patchy lesions with ill-defined borders • Allows a more detailed assessment of imaging data [ABSTRACT FROM AUTHOR]

Abstract: A critical assumption underlying the practice of functional localization is that the voxels identified by functional localization are essentially the same as those activated in the main experiment for a particular anatomical area. Violations of this assumption bias the resulting analyses and can dramatically increase the likelihood of both Type I and Type II errors. Here we investigated how the amount of data affects the reliability of a set of common functionally-defined regions-of-interest (fROIs). Four participants were scanned ten times each to functionally localize extrastriate regions sensitive to visually presented words, objects and faces. A within-subject random-effects analysis was used as the “gold standard” for identifying the fROIs and the results were compared to within-subject, fixed-effect analyses typically used for functional localization. By varying the quantity of data included in the analyses, we empirically assessed the amount needed to ensure reliable identification of the fROIs. The results demonstrated that the most consistent fROIs were based on either stringent statistical thresholding (Z>5.0) of large quantities of data or on lenient thresholding (Z>2.3) of a modest amount of data, with both methods yielding 70–80% overlap between the functional localization results and the “gold standard.” Stringent statistical thresholds on typical quantities of localizer data led to the poorest reliability (<20% overlap). These findings suggest that the most reliable and cost-efficient method for functional localization involves collecting a relatively small amount of data (~10min) and using a lenient statistical threshold to identify all voxels in a given region that are sensitive to the process-of-interest. [Copyright &y& Elsevier]

The ability to create super high-resolution video is becoming relative easy to do either through a single high-definition video camera or panoramic video that automatically stitches multiple views together. As an example of the former, the motion picture industry now has 6000 x 4000 pixel full-rate video cameras available. This means that supporting region-of-interest cropping will become more important in the future. In this article, we propose a mechanism to support region-of-interest adaptation of stored video. The proposed approach creates a compression-compliant stream (e.g., MPEG-2), while still allowing it to be cropped. Fortunately, video standards like MPEG-2 specify the format of a compliant stream, and not the algorithm to get there. As a result, there is an opportunity to allow system researchers and implementers ways to optimize for applications. We show various fundamental tradeoffs that are made in order to support region-of-interest cropping with super high-resolution video which we received from a local motion-picture firm. [ABSTRACT FROM AUTHOR]

High-spatial-resolution videos offer the possibility of viewing an arbitrary region-of-interest (RoI) interactively. Zoom functionality enables watching high-resolution content even on displays of lower spatial resolution. If arbitrary regions corresponding to arbitrary zoom factors can be served to the user, the transmission and/or decoding of the entire high-spatial-resolution video can be avoided. Moreover, if the video content can be encoded such that arbitrary RoIs corresponding to different zoom factors can be simply extracted from the compressed bitstream, we can avoid dedicated video encoding for each user. We propose such a video coding scheme that is vital in allowing the system to scale to large numbers of remote users as well as to encode and store the content for subsequent repeated playback. Apart from generating a multi-resolution representation, our coding scheme uses P slices from H.264/AVC. We study the tradeoff in the choice of slice size. A larger slice size enables higher coding efficiency for representing the entire scene but increases the number of pixels that have to be transmitted. The optimal slice size achieves the best tradeoff and minimizes the expected transmission bitrate. Experimental results confirm the optimality of our predicted slice size for various test cases. Furthermore, we propose an improvement based on background extraction and long-term memory motion-compensated prediction. Experiments indicate up to 85% bitrate reduction while retaining efficient random access capability. [ABSTRACT FROM AUTHOR]

Abstract: A major challenge in positron emission tomography (PET) amyloid imaging studies of Alzheimer''s disease (AD) is the reliable detection of early amyloid deposition in human brain. Manual region-of-interest (ROI) delineation on structural magnetic resonance (MR) images is generally the reference standard for the extraction of count-rate data from PET images, as compared to automated MR-template(s) methods that utilize spatial normalization and a single set of ROIs. The goal of this work was to assess the inter-rater reliability of manual ROI delineation for PiB PET amyloid retention measures and the impact of CSF dilution correction (CSF) on this reliability for data acquired in elderly control (n=5) and AD (n=5) subjects. The intraclass correlation coefficient (ICC) was used to measure reliability. As a secondary goal, ICC scores were also computed for PiB outcome measures obtained by an automated MR-template ROI method and one manual rater; to assess the level of reliability that could be achieved using different processing methods. Fourteen ROIs were evaluated that included anterior cingulate (ACG), precuneus (PRC) and cerebellum (CER). The PiB outcome measures were the volume of distribution (VT), summed tissue uptake (SUV), and corresponding ratios that were computed using CER as reference (DVR and SUVR). Substantial reliability (ICC≥0.932) was obtained across 3 manual raters for VT and SUV measures when CSF correction was applied across all outcomes and regions and was similar in the absence of CSF correction. The secondary analysis revealed substantial reliability in primary cortical areas between the automated and manual SUV [ICC≥0.979 (ACG/PRC)] and SUVR [ICC≥0.977/0.952 (ACG/PRC)] outcomes. The current study indicates the following rank order among the various reliability results in primary cortical areas and cerebellum (high to low): 1) VT or SUV manual delineation, with or without CSF correction; 2) DVR or SUVR manual delineation, with or without CSF correction; 3) SUV automated delineation, with CSF correction; and 4) SUVR automated delineation, with or without CSF correction. The high inter-rater reliability of PiB outcome measures in primary cortical areas (ACG/PRC) is important as reliable methodology is needed for the detection of low levels of amyloid deposition on a cross-sectional basis and small changes in amyloid deposition on a longitudinal basis. [Copyright &y& Elsevier]

In this paper, we present a video coding scheme which applies the technique of visual saliency computation to adjust image fidelity before compression. To extract visually salient features, we construct a spatio-temporal saliency map by analyzing the video using a combined bottom-up and top-down visual saliency model. We then use an extended bilateral filter, in which the local intensity and spatial scales are adjusted according to visual saliency, to adaptively alter the image fidelity. Our implementation is based on the H.264 video encoder JM12.0. Besides evaluating our scheme with the H.264 reference software, we also compare it to a more traditional foreground-background segmentation-based method and a foveation-based approach which employs Gaussian blurring. Our results show that the proposed algorithm can improve the compression ratio significantly while effectively preserving perceptual visual quality. [ABSTRACT FROM AUTHOR]

Region-of-interested (ROI) segmentation is one of the important applications in content-based processing. Although much work has been done in this area, relatively little work has been reported to the ROI segmentation with an implementation consideration. This paper presents a system level design with edge-based ROI segmentation. In order to improve the overall system performance, we propose a computationally efficient algorithm to reduce the complexity in segmentation. Based on the algorithm level development, we put more efforts on the realization with a single board platform system. The system is based on the hybrid architecture, including the associating synchronous modules and asynchronous modules. Detailed task partitions are well scheduled. The system integrates the acquisition and visualization modules for video stream input and output, the transition module for video format transformation, and the DSP processing module for ROI segmentation. We also present a number of optimization techniques in fixed-point arithmetic, memory usage, data transfer and parallel processing. Experiments are performed in the dynamic natural environment. The proposed system can segment ROI well. The run-time results achieve 66 images per second on QCIF format and validate the perspective of using ROI segmentation in content-based applications. [ABSTRACT FROM AUTHOR]

Purpose: The region-of-interest (ROI) selection and evaluation is one of the key factors in the successful evaluation of radiological images. However, the presence of noise in images may lead to incorrect diagnosis. The aim of this study was to test the hypothesis that the weighting by error estimation in ROI assessment might significantly improve the validity of the results. Methods: As a model, the data maps of the transverse relaxation time constants (T2) from patients who underwent a matrix-associated chondrocyte transplantation procedure on the femoral condyle were analyzed. Artificial noise with a Rician density probability distribution was added to each TE image. ROIs were processed either as a regular arithmetic mean or as a weighted mean, in which weighted coefficients were calculated with regard to fitting error estimates [coefficient of determination (R2); root mean squared error (RMSE), mean absolute error (MSE), mean squared error (MAE), and chi-squared error (χ2)]. Results: The global T2 values in repair tissue (mean±standard deviation, 62±7 ms; range 51–70 ms) and in healthy cartilage (mean±SD, 49±6 ms; range 40–60 ms) were significantly different (p<0.001). With a 45% or greater decrease from the original SNR value (corresponding to a noise level of 35% of random value), the statistical significance was lost (P>0.05); however, the use of the coefficient of determination (R2) as a correction factor was able to maintain the p-value of <0.05 up to a 56% decrease from the original SNR value. Conclusions: The results of this study can prospectively be applied in a wide range of radiological imaging techniques in cases when error estimation is possible. Our analysis on MR images with artificially added noise showed that utilization of the correlation of determination (R2) as a weighting parameter in ROI evaluation may significantly improve the differentiation between native and transplanted cartilage tissue in noisy images. This could be an added benefit in the non-invasive monitoring of the post-operative status of patients with cartilage transplants if the MR images are not ideal (e.g., lower field strength or lower SNR). [ABSTRACT FROM AUTHOR]

In the current methods of ROI coding methods, after all the pixels have been scanned in order and the ROI is distinguished, an ROI mask is generated. So, to support a dynamic Region-of-Interest (ROI) in JPEG2000, fast ROI mask generation is needed. Our method scans 4 pixels of the corners in one code block and then, based on the information, scans the edges from the corners to get the boundaries of the ROI and background. This information consists of distributed information about the ROI and two coordinates of the pixels, which are the points where the edges and boundaries meet. This information is transmitted to the encoder and supported for fast ROI mask generation. There are no great differences between the proposed method and the existing methods in terms of quality, but the proposed method showed superiority in speed. [ABSTRACT FROM AUTHOR]

Abstract: Near-duplicate detection techniques are exploited to facilitate representative photo selection and region-of-interest (ROI) determination, which are important functionalities for efficient photo management and browsing. To make near-duplicate detection module resist to noisy features, three filtering approaches, i.e., point-based, region-based, and probabilistic latent semantic (pLSA), are developed to categorize feature points. For the photos taken in travels, we construct a support vector machine classifier to model matching patterns between photos and determine whether photos are near-duplicate pairs. Relationships between photos are then described as a graph, and the most central photo that best represents a photo cluster is selected according to centrality values. Because matched feature points are often located in the interior or at the contour of important objects, the region that compactly covers the matched feature points is determined as the ROI. We compare the proposed approaches with conventional ones and demonstrate their effectiveness. [Copyright &y& Elsevier]

Abstract: Alzheimer''s Disease (AD) is a neurodegenerative disorder that mainly affects grey matter (GM). Nevertheless, a number of investigations have documented white matter (WM) pathology associated with AD. The corpus callosum (CC) is the largest WM fiber bundle in the human brain. It has been shown to be susceptible to atrophy in AD mainly as a correlate of Wallerian degeneration of commissural nerve fibers of the neocortex. The aim of this study was to investigate which callosal regions are affected and whether callosal degeneration is associated with the stage of the disease. For this purpose, we analyzed high-resolution MRI data of patients with amnesic mild cognitive impairment (MCI) (n =20), mild AD (n =20), severe AD (n =10), and of healthy controls (n =20). Callosal morphology was investigated applying two different structural techniques: mesh-based geometrical modeling methods and whole-brain voxel-based analyses. Our findings indicate significant reductions in severe AD patients compared to healthy controls in anterior (genu and anterior body) and posterior (splenium) sections. In contrast, differences between healthy controls and mild AD patients or amnesic MCI patients were less pronounced and did not survive corrections for multiple comparisons. When correlating anterior and posterior WM density of the CC with GM density of the cortex in the severe AD group, we detected significant positive relationships between posterior sections of the CC and the cortex. We conclude that callosal atrophy is present predominantly in the latest stage of AD, where two mechanisms might contribute to WM alterations in severe AD: the Wallerian degeneration in posterior subregions and the myelin breakdown process in anterior subregions. [Copyright &y& Elsevier]

Objectives: Functional and postmortem studies suggest that the orbitofrontal cortex (OFC) is involved in the pathophysiology of bipolar disorder (BD). This anatomical magnetic resonance imaging (MRI) study examined whether BD patients have smaller OFC gray matter volumes compared to healthy comparison subjects (HC). Methods: Twenty-eight BD patients were compared to 28 age- and gender-matched HC. Subjects underwent a 1.5T MRI with 3D spoiled gradient recalled acquisition. Total OFC and medial and lateral subdivisions were manually traced by a blinded examiner. Images were segmented and gray matter volumes were calculated using an automated method. Results: Analysis of covariance, with intracranial volume as covariate, showed that BD patients and HC did not differ in gray matter volumes of total OFC or its subdivisions. However, total OFC gray matter volume was significantly smaller in depressed patients (n = 10) compared to euthymic patients (n = 18). Moreover, total OFC gray matter volumes were inversely correlated with depressive symptom intensity, as assessed by the Hamilton Depression Rating Scale. OFC gray matter volumes were not related to lithium treatment, age at disease onset, number of episodes, or family history of mood disorders. Conclusions: Our results suggest that abnormal OFC gray matter volumes are not a pervasive characteristic of BD, but may be associated with specific clinical features of the disorder. [ABSTRACT FROM AUTHOR]

Presurgical determination of language laterality is an important step for assessing potential risk of dysfunction resulting from brain resection within or near suspected language areas. Image-based functional MRI (fMRI) methods seek to address limitations to the clinical gold-standard technique by offering a safer, less costly, and non-invasive alternative. In this study we outline a set of protocols for objective determination of langue-specific asymmetry from fMRI activation maps. We studied 13 healthy, right-handed volunteers using a vocalized antonym-generation task. Initially, using the standard threshold-dependent laterality index (LI) procedure, we demonstrated an undesirably high degree of intra-subject variability and indeterminacy in LI value. We addressed this issue by implementing a novel threshold-independent method, resulting in a single, unambiguous LI for each subject. These LIs were then averaged across the group and used to compare functional laterality within the whole hemispheric volumes and six intra-hemispheric regions-of-interest (ROIs). We noted that as a result of increased bilateral activation from vocalizations, laterality assessment calculated from the whole hemisphere resulted in insignificant asymmetry. However, by focusing the LI exclusively on the inferior frontal (IFG) and supramarginal gyri (SMG), robust leftward asymmetries were observed. We also examined the influence of stimulus mode on the group mean ROI LI, and observed an increase in IFG asymmetry using visual mode, and in SMG using the auditory mode. Based on these findings, we make recommendations for optimized presurgical protocols. [ABSTRACT FROM AUTHOR]

Abstract: Region-of-interest (ROI) is an essential task that one must undertake in low bit-rate multimedia communications because of the limited bandwidth of the channels and the transcoder between different standards. In this paper, an effective ROI determination method is proposed. The skin-color extraction is first employed to determine the ROI macroblocks; then, the framework for the adjustment of the quantization parameters (QPs) of ROI macroblocks according to the distortion and bit-rate variations is proposed to fit the target bit rate. In view of the residual distortion information, the fuzzy logic controller proposed in our framework can adaptively adjust the weighting factor for corresponding QPs by the distortion variation in the macroblock layer. A linear prediction formula, derived from the rate variation, is proposed to allocate appropriate bits for each ROI macroblock and maintain the target bit rate and buffer fullness. Experiment results on TMN8 reveal that the proposed ROI video coding can significantly enhance the quality at ROIs. Furthermore, the proposed framework obtains about 0.5dB gain in the objective performance and also provides the better subjective quality compared with previous works. [Copyright &y& Elsevier]

Abstract: The aim of this study was to determine the efficacy of using sound velocity and tissue attenuation to clinically discriminate breast cancer from healthy tissues. The methods for reconstructing the sound-velocity and attenuation-coefficient distributions were previously proposed and tested on tissue-mimicking phantoms. The methods require only raw channel data acquired by a linear transducer array and can therefore be implemented on existing clinical systems. In this paper, these methods are tested on clinical data. A total of 19 biopsy-proven cases, consisting of five carcinomas (CAs), seven fibroadenomas (FAs), six adipose tissue (fat) and one oil cyst, were evaluated. A single imaging setup consisting of a 5-MHz, 128-channel linear array was used to simultaneously obtain B-mode image data, time-of-flight data and attenuation data. The sound velocity and attenuation coefficient can be reconstructed inside and outside a region of interest manually selected in the B-mode image. To reduce distortion caused by tissue inhomogeneities, an optimal filter derived from pulse-echo data—with water replacing the breast tissue—is applied. We found that the sound velocities in CA, FA and fat tissues relative to those in the surrounding tissues were 49.8 ± 35.2, 2.6 ± 27.3 and −25.1 ± 44.9 m/s (mean ± SD), respectively, whereas the relative attenuation coefficients were 0.21 ± 0.58, 0.27 ± 0.62 and −0.02 ± 0.59 dB/cm/MHz. These results indicate that CA can be discriminated from FA and fat by choosing an appropriate threshold for the relative sound velocity (i.e., 18.5 m/s). However, the large variations in the attenuation within the same type of tissue make simple thresholding ineffective. Nevertheless, the method described in this paper has the potential to reduce negative biopsies and to improve the accuracy of breast cancer detection in clinics. (E-mail: paichi@cc.ee.ntu.edu.tw) [Copyright &y& Elsevier]

Abstract: The computer algorithms for the delineation of anatomical structures and other regions of interest on the medical imagery are important component in assisting and automating specific radiological tasks. In addition, the segmentation of region is an important first step for variety image related application and visualization tasks. In this paper, we propose a fast and automated connectivity-based local adaptive thresholding (CLAT) algorithm to segment the carotid artery in sequence medical imagery. This algorithm provides the new feature that is the circumscribed quadrangle on the segmented carotid artery for region-of-interest (ROI) determination. By using the preserved connectivity between consecutive slice images, the size of the ROI is adjusted like a moving window according to the segmentation result of previous slice image. The histogram is prepared for each ROI and then smoothed by local averaging for the threshold selection. The threshold value for carotid artery segmentation is locally selected on each slice image and is adaptively determined through the sequence image. In terms of automated features and computing time, this algorithm is more effective than region growing and deformable model approaches. This algorithm is also applicable to segment the cylinder shape structures and tree-like blood vessels such as renal artery and coronary artery in the medical imagery. Experiments have been conducted on synthesized images, phantom and clinical data sets with various Gaussian noise. [Copyright &y& Elsevier]

Rationale and Objectives: BOLD MRI provides functional information based on minimal changes. Problems inherent in data processing of the very low signal-to-noise-ratio of BOLD experiments have created obstacles for validation of certain techniques using standard strength-field MR scanners. Measures of diagnostic accuracy of clustered data are directly related to the reading parameters used to define regions-of-interest (ROIs). Our primary aim was to determine the combination of ROI-related reading parameters that provides highest accuracy for discrimination of presence or absence of arthritis in acute and subacute stages of the disease using paired comparisons of BOLD MRI data. Materials and Methods: Six male New Zealand white rabbits were injected with albumin into one knee and saline into the contralateral knee, 3 animals had albumin injected into only one of the knees, 2 had saline injected into one of the knees, and 3 animals were not injected. The rabbits’ knees underwent BOLD MRI on days 1 and 28 after induction of arthritis, except for the knees of 3 animals (albumin- vs saline-injected knees, n = 2 animals; saline- vs noninjected knees, n = 1 animal) that died before expected and had only the first MRI examination done. Percentage of activated voxels and differences in on-and-off signal intensities were the BOLD MRI methods applied. Data were analyzed using anatomic-driven small ROI, voxel-chaser-driven small ROI and anatomic-driven large ROI techniques. Results: Diagnostic areas-under-the curve (AUCs) were obtained only for acute arthritis and only when percentage of activated voxels was used. Low threshold, positive voxel activations and small ROIs generated the largest AUCs (AUC ± SE, .911 ± .092, P = .014) using either anatomic-driven or voxel-chaser-driven techniques. A sensitivity analysis confirmed the importance of threshold as a parameter for analysis. Conclusion: Low threshold, positive voxel activations and small ROIs constituted the set of reading parameters that provided the most accurate BOLD MRI results. [Copyright &y& Elsevier]

Region-of-interest (ROI) techniques are often utilized to improve coding for detailed regions in natural still-image coding standards such as JPEG2000 [1], but no specific method is stated for determining the ROI map. In this paper, an ROI-based method, in which rectangular regions are extracted using document image analysis (DIA), is proposed specifically for document image coding. These rectangular regions can be efficiently coded using wavelets, and DIA may also be used to distinguish between important and unwanted foreground regions, allowing further coding gains (as illustrated in one of the example documents in the paper). Compared to multilayer methods currently used for document image coding [2], the method is simpler and scalable, while improving visual quality and the peak-signal-to-noise ratio (PSNR). [ABSTRACT FROM AUTHOR]

In this paper, a new set of orthogonal moments based on the discrete classical Krawtchouk polynomials is introduced. The Krawtchouk polynomials are scaled to ensure numerical stability, thus creating a set of weighted Krawtchouk polynomials. The set of proposed Krawtchouk moments is then derived from the weighted Krawtchouk polynomials. The orthogonality of the proposed moments ensures minimal information redundancy. No numerical approximation is involved in deriving the moments, since the weighted Krawtchouk polynomials are discrete. These properties make the Krawtchouk moments well suited as pattern features in the analysis of two-dimensional images. It is shown that the Krawtchouk moments can be employed to extract local features of an image, unlike other orthogonal moments, which generally capture the global features. The computational aspects of the moments using the recursive and symmetry properties are discussed. The theoretical framework is validated by an experiment on image reconstruction using Krawtchouk moments and the resalts are compared to that Of Zernike, Pseudo-Zernike, Legendre, and Tchebichef moments. Krawtchouk moment invariants is constructed using a linear combination of geometric moment invariants and an object recogm'tf0n experiment shows Krawtchouk moment invariants perform Significantly better than Hu's moment invariants in both noise-free and noisy conditions. [ABSTRACT FROM AUTHOR]

Remote photoplethysmography (rPPG) is a video-based non-contact heart rate measurement technology. It is a fact that most existing rPPG methods fail to deal with the spatiotemporal features of the video, which is significant for the extraction of the rPPG signal. In this paper, we propose a 3D central difference convolutional network (CDCA-rPPGNet) to measure heart rate, with an attention mechanism to combine spatial and temporal features. First, we crop and stitch the region of interest together through facial landmarks. Next, the high-quality regions of interest are fed to CDCA-rPPGNet based on a central difference convolution, which can enhance the spatiotemporal representation and capture rich relevant time contexts by collecting time difference information. In addition, we integrate the attention module into the neural network, aiming to strengthen the ability of the neural network to extract video channels and spatial features, so as to obtain more accurate rPPG signals. In summary, the three main contributions of this paper are as follows: (1) the proposed network base on central difference convolution could better capture the subtle color changes to recover the rPPG signals; (2) the proposed ROI extraction method provides high-quality input to the network; (3) the attention module is used to strengthen the ability of the network to extract features. Extensive experiments are conducted on two public datasets—the PURE dataset and the UBFC-rPPG dataset. In terms of the experiment results, our proposed method achieves 0.46 MAE (bpm), 0.90 RMSE (bpm) and 0.99 R value of Pearson's correlation coefficient on the PURE dataset, and 0.60 MAE (bpm), 1.38 RMSE (bpm) and 0.99 R value of Pearson's correlation coefficient on the UBFC dataset, which proves the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel approach to image indexing by incorporating a neural network model, Kohonen’s Self-Organising Map (SOM), for content-based image retrieval. The motivation stems from the idea of finding images by regarding users’ specifications or requirements imposed on the query, which has been ignored in most existing image retrieval systems. An important and unique aspect of our interactive scheme is to allow the user to select a Region-Of-Interest (ROI) from the sample image, and subsequent query concentrates on matching the regional colour features to find images containing similar regions as indicated by the user. The SOM algorithm is capable of adaptively partitioning each image into several homogeneous regions for representing and indexing the image. This is achieved by unsupervised clustering and classification of pixel-level features, called Local Neighbourhood Histograms (LNH), without a priori knowledge about the data distribution in the feature space. The indexes generated from the resultant prototypes of SOM learning demonstrate fairly good performance over an experimental image database, and therefore suggest the effectiveness and significant potential of our proposed indexing and retrieval strategy for application to content-based image retrieval. [ABSTRACT FROM AUTHOR]

Abstract No reliable anatomical or functional alterations have been confirmed in psychiatric neuroimaging; however it can become reliable with translational impact on clinical practice when considering crucial methodological issues. We provide guidelines to authors, editors and reviewers in the implementation/evaluation of neuroimaging studies to bend neuroimaging to be more than basic neuroscience.