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In this study, we proposed a universal n-th order partial differential equation (PDE) of 2-D Radon transform to disclose the relationship of Radon transform over a neighborhood of the integral line, named as local correlation equation (LCE). It is independent to the imaging object while in present CT theory, the relationship of Radon transform over neighboring integral line had been described depended on the imaging objection. Hence, the LCE is the first PDE to reveal the universal correlation property of Radon transform. The LCE can be applied to either of 2D CT projections or any 2-D profile of 3-D CT projections. The correlation also provides the redundancy property of Radon transform. In this regard, we carried out a preliminary study on sparse-view CT reconstruction by using a discrete first order LCE to interpolate missing projections in sparse-view sampling without knowing image prior. Meanwhile, we also proposed a unified reconstruction framework that combines a regularized iterative reconstruction with the LCE based interpolation method to handle the sparse-view CT problem with higher sparsity level. The conducted experiments have credibly validated the proposed LCE, projection interpolation method, and the unified reconstruction scheme. The result of this study suggests an attractive possibility that a sparse-view projection may contain enough information of the complete projection, by which projection completeness in CT scanning may not be necessity. This possibility would bring profound changes in CT geometry designs and reconstruction algorithms. Moreover, this study initiates an appealing research topic of exploring the redundancy property of Radon transform and investigating new CT theories based on the redundancy property, which will boost the further development of CT reconstructions., Comment: correction of typos and some results have been updated

Lowering radiation dose per view and utilizing sparse views per scan are two common CT scan modes, albeit often leading to distorted images characterized by noise and streak artifacts. Blind image quality assessment (BIQA) strives to evaluate perceptual quality in alignment with what radiologists perceive, which plays an important role in advancing low-dose CT reconstruction techniques. An intriguing direction involves developing BIQA methods that mimic the operational characteristic of the human visual system (HVS). The internal generative mechanism (IGM) theory reveals that the HVS actively deduces primary content to enhance comprehension. In this study, we introduce an innovative BIQA metric that emulates the active inference process of IGM. Initially, an active inference module, implemented as a denoising diffusion probabilistic model (DDPM), is constructed to anticipate the primary content. Then, the dissimilarity map is derived by assessing the interrelation between the distorted image and its primary content. Subsequently, the distorted image and dissimilarity map are combined into a multi-channel image, which is inputted into a transformer-based image quality evaluator. Remarkably, by exclusively utilizing this transformer-based quality evaluator, we won the second place in the MICCAI 2023 low-dose computed tomography perceptual image quality assessment grand challenge. Leveraging the DDPM-derived primary content, our approach further improves the performance on the challenge dataset., Comment: 10 pages, 6 figures

A variety of supervise learning methods are available for low-dose CT denoising in the sinogram domain. Traditional model observers are widely employed to evaluate these methods. However, the sinogram domain evaluation remains an open challenge for deep learning-based low-dose CT denoising. Since each lesion in medical CT images corresponds to a narrow sinusoidal strip in sinogram domain, here we proposed a transformer-based model observer to evaluate sinogram domain supervised learning methods. The numerical results indicate that our transformer-based model well-approximates the Laguerre-Gauss channelized Hotelling observer (LG-CHO) for a signal-known-exactly (SKE) and background-known-statistically (BKS) task. The proposed model observer is employed to assess two classic CNN-based sinogram domain denoising methods. The results demonstrate a utility and potential of this transformer-based observer model in developing deep low-dose CT denoising methods in the sinogram domain., Comment: 14 pages, 9 figures

Accurately identifying patient's status through medical images plays an important role in diagnosis and treatment. Artificial intelligence (AI), especially the deep learning, has achieved great success in many fields. However, more reliable AI model is needed in image guided diagnosis and therapy. To achieve this goal, developing a balanced, safe and robust model with a unified framework is desirable. In this study, a new unified model termed as automated multi-objective Mixer (AutoMO-Mixer) model was developed, which utilized a recent developed multiple layer perceptron Mixer (MLP-Mixer) as base. To build a balanced model, sensitivity and specificity were considered as the objective functions simultaneously in training stage. Meanwhile, a new evidential reasoning based on entropy was developed to achieve a safe and robust model in testing stage. The experiment on an optical coherence tomography dataset demonstrated that AutoMO-Mixer can obtain safer, more balanced, and robust results compared with MLP-Mixer and other available models.

Image quality assessment that aims at estimating the subject quality of images, builds models to evaluate the perceptual quality of the image in different applications. Based on the fact that the human visual system (HVS) is highly sensitive to structural information, the edge information extraction is widely applied in different IQA metrics. According to previous studies, the image gradient magnitude (GM) and the Laplacian of Gaussian (LOG) operator are two efficient structural features in IQA tasks. However, most of the IQA metrics achieve good performance only when the distorted image is totally registered with the reference image, but fail to perform on images with small translations. In this paper, we propose an FR-IQA model with the quadratic sum of the GM and the LOG signals, which obtains good performance in image quality estimation considering shift-insensitive property for not well-registered reference and distortion image pairs. Experimental results show that the proposed model works robustly on three large scale subjective IQA databases which contain a variety of distortion types and levels, and stays in the state-of-the-art FR-IQA models no matter for single distortion type or across whole database. Furthermore, we validated that the proposed metric performs better with shift-insensitive property compared with the CW-SSIM metric that is considered to be shift-insensitive IQA so far. Meanwhile, the proposed model is much simple than the CW-SSIM, which is efficient for applications., Comment: 10 pages, 7 figures

Structural similarity (SSIM)-based distortion $D_\text{SSIM}$ is more consistent with human perception than the traditional mean squared error $D_\text{MSE}$. To achieve better video quality, many studies on optimal bit allocation (OBA) and rate-distortion optimization (RDO) used $D_\text{SSIM}$ as the distortion metric. However, many of them failed to optimize OBA and RDO jointly based on SSIM, thus causing a non-optimal R-$D_\text{SSIM}$ performance. This problem is due to the lack of an accurate R-$D_\text{SSIM}$ model that can be used uniformly in both OBA and RDO. To solve this problem, we propose a $D_\text{SSIM}$-$D_\text{MSE}$ model first. Based on this model, the complex R-$D_\text{SSIM}$ cost in RDO can be calculated as simpler R-$D_\text{MSE}$ cost with a new SSIM-related Lagrange multiplier. This not only reduces the computation burden of SSIM-based RDO, but also enables the R-$D_\text{SSIM}$ model to be uniformly used in OBA and RDO. Moreover, with the new SSIM-related Lagrange multiplier in hand, the joint relationship of R-$D_\text{SSIM}$-$\lambda_\text{SSIM}$ (the negative derivative of R-$D_\text{SSIM}$) can be built, based on which the R-$D_\text{SSIM}$ model parameters can be calculated accurately. With accurate and unified R-$D_\text{SSIM}$ model, SSIM-based OBA and SSIM-based RDO are unified together in our scheme, called SOSR. Compared with the HEVC reference encoder HM16.20, SOSR saves 4%, 10%, and 14% bitrate under the same SSIM in all-intra, hierarchical and non-hierarchical low-delay-B configurations, which is superior to other state-of-the-art schemes., Comment: An improved version of this manuscript has been accepted by IEEE Transactions on Broadcasting DOI 10.1109/TBC.2021.3068871. The project page is located at http://gr.xjtu.edu.cn/web/xqmou/sosr

The distinctiveness of image regions is widely used as the cue of saliency. Generally, the distinctiveness is computed according to the absolute difference of features. However, according to the image quality assessment (IQA) studies, the human visual system is highly sensitive to structural changes rather than absolute difference. Accordingly, we propose the computation of the structural dissimilarity between image patches as the distinctiveness measure for saliency detection. Similar to IQA models, the structural dissimilarity is computed based on the correlation of the structural features. The global structural dissimilarity of a patch to all the other patches represents saliency of the patch. We adopt two widely used structural features, namely the local contrast and gradient magnitude, into the structural dissimilarity computation in the proposed model. Without any postprocessing, the proposed model based on the correlation of either of the two structural features outperforms 11 state-of-the-art saliency models on three saliency databases., Comment: For associated source code, see https://github.com/yangli-xjtu/SDS

Genetic studies have identified associations between gene mutations and clear cell renal cell carcinoma (ccRCC). Because the complete gene mutational landscape cannot be characterized through biopsy and sequencing assays for each patient, non-invasive tools are needed to determine the mutation status for tumors. Radiogenomics may be an attractive alternative tool to identify disease genomics by analyzing amounts of features extracted from medical images. Most current radiogenomics predictive models are built based on a single classifier and trained through a single objective. However, since many classifiers are available, selecting an optimal model is difficult. On the other hand, a single objective may not be a good measure to guide model training. We proposed a new multi-classifier multi-objective (MCMO) radiogenomics predictive model. To obtain more reliable prediction results, similarity-based sensitivity and specificity were defined and considered as the two objective functions simultaneously during training. To take advantage of different classifiers, the evidential reasoning (ER) approach was used for fusing the output of each classifier. Additionally, a new similarity-based multi-objective optimization algorithm (SMO) was developed for training the MCMO to predict ccRCC related gene mutations (VHL, PBRM1 and BAP1). Preliminary results revealed an association between some quantitative computed tomography (CT) features and underlying mutations. Using the proposed MCMO model, we achieved a predictive area under the receiver operating characteristic curve (AUC) over 0.86 for VHL, PBRM1, and BAP1 genes with balanced sensitivity and specificity. Furthermore, MCMO outperformed all the individual classifiers and yielded more reliable results than other optimization algorithms and commonly used fusion strategies.

In this paper, we introduce a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity. The Wasserstein distance is a key concept of the optimal transform theory, and promises to improve the performance of the GAN. The perceptual loss compares the perceptual features of a denoised output against those of the ground truth in an established feature space, while the GAN helps migrate the data noise distribution from strong to weak. Therefore, our proposed method transfers our knowledge of visual perception to the image denoising task, is capable of not only reducing the image noise level but also keeping the critical information at the same time. Promising results have been obtained in our experiments with clinical CT images.

In video coding, it is expected that the encoder could adaptively select the encoding parameters (e.g., quantization parameter) to optimize the bit allocation to different sources under the given constraint. However, in hybrid video coding, the dependency between sources brings high complexity for the bit allocation optimization, especially in the block-level, and existing optimization methods mostly focus on frame-level bit allocation. In this paper, we propose a macroblock (MB) level bit allocation method based on the minimum maximum (MINMAX) criterion, which has acceptable encoding complexity for offline applications. An iterative-based algorithm, namely maximum distortion descend (MDD), is developed to reduce quality fluctuation among MBs within a frame, where the Structure SIMilarity (SSIM) index is used to measure the perceptual distortion of MBs. Our extensive experimental results on benchmark video sequences show that the proposed method can greatly enhance the encoding performance in terms of both bits saving and perceptual quality improvement., Comment: 11 pages, 17 figures

Among the various image quality assessment (IQA) tasks, blind IQA (BIQA) is particularly challenging due to the absence of knowledge about the reference image and distortion type. Features based on natural scene statistics (NSS) have been successfully used in BIQA, while the quality relevance of the feature plays an essential role to the quality prediction performance. Motivated by the fact that the early processing stage in human visual system aims to remove the signal redundancies for efficient visual coding, we propose a simple but very effective BIQA method by computing the statistics of self-similarity (SOS) in an image. Specifically, we calculate the inter-scale similarity and intra-scale similarity of the distorted image, extract the SOS features from these similarities, and learn a regression model to map the SOS features to the subjective quality score. Extensive experiments demonstrate very competitive quality prediction performance and generalization ability of the proposed SOS based BIQA method.

In this paper, an improved scatter correction with moving beam stop array (BSA) for x-ray cone beam (CB) CT is proposed. Firstly, correlation between neighboring CB views is deduced based on John's Equation. Then, correlation-based algorithm is presented to complement the incomplete views by using the redundancy (over-determined information) in CB projections. Finally, combining the algorithm with scatter correction method using moving BSA, where part of primary radiation is blocked and incomplete projections are acquired, an improved correction method is proposed. Effectiveness and robustness is validated by Monte Carlo (MC) simulation with EGSnrc on humanoid phantom., Comment: 4 pages, pp98-101, Proceedings of the 10th International Meeting on Fully Three-dimensional Image Reconstruction in Radiology and Nuclear Medicine, Beijing, China, 2009

In the scatter correction for x-ray Cone Beam (CB) CT, the single-scan scheme with moving Beam Stop Array (BSA) offers reliable scatter measurement with low dose, and by using Projection Correlation based View Interpolation (PC-VI), the primary fluence shaded by the moving BSA (during scatter measurement) could be recovered with high accuracy. However, the moving BSA may increase the mechanical burden in real applications. For better practicability, in this paper we proposed a PC-VI based single-scan scheme with a ring-shaped stationary BSA, which serves as a virtual moving BSA during CB scan, so the shaded primary fluence by this stationary BSA can be also well recovered by PC-VI. The principle in designing the whole system is deduced and evaluated. The proposed scheme greatly enhances the practicability of the single-scan scatter correction scheme., Comment: 4 pages, Proceedings of the 11th International Meeting on Fully Three-dimensional Image Reconstruction in Radiology and Nuclear Medicine, Potsdam, Germany, 2011

It is an important task to faithfully evaluate the perceptual quality of output images in many applications such as image compression, image restoration and multimedia streaming. A good image quality assessment (IQA) model should not only deliver high quality prediction accuracy but also be computationally efficient. The efficiency of IQA metrics is becoming particularly important due to the increasing proliferation of high-volume visual data in high-speed networks. We present a new effective and efficient IQA model, called gradient magnitude similarity deviation (GMSD). The image gradients are sensitive to image distortions, while different local structures in a distorted image suffer different degrees of degradations. This motivates us to explore the use of global variation of gradient based local quality map for overall image quality prediction. We find that the pixel-wise gradient magnitude similarity (GMS) between the reference and distorted images combined with a novel pooling strategy the standard deviation of the GMS map can predict accurately perceptual image quality. The resulting GMSD algorithm is much faster than most state-of-the-art IQA methods, and delivers highly competitive prediction accuracy.

Patient respiratory signal associated with the cone beam CT (CBCT) projections is important for lung cancer radiotherapy. In contrast to monitoring an external surrogate of respiration, such signal can be extracted directly from the CBCT projections. In this paper, we propose a novel local principle component analysis (LPCA) method to extract the respiratory signal by distinguishing the respiration motion-induced content change from the gantry rotation-induced content change in the CBCT projections. The LPCA method is evaluated by comparing with three state-of-the-art projection-based methods, namely, the Amsterdam Shroud (AS) method, the intensity analysis (IA) method, and the Fourier-transform based phase analysis (FT-p) method. The clinical CBCT projection data of eight patients, acquired under various clinical scenarios, were used to investigate the performance of each method. We found that the proposed LPCA method has demonstrated the best overall performance for cases tested and thus is a promising technique for extracting respiratory signal. We also identified the applicability of each existing method., Comment: 21 pages, 11 figures, submitted to Phys. Med. Biol

This paper studies the security of a secure communication scheme based on two discrete-time intermittently-chaotic systems synchronized via a common random driving signal. Some security defects of the scheme are revealed: 1) the key space can be remarkably reduced; 2) the decryption is insensitive to the mismatch of the secret key; 3) the key-generation process is insecure against known/chosen-plaintext attacks. The first two defects mean that the scheme is not secure enough against brute-force attacks, and the third one means that an attacker can easily break the cryptosystem by approximately estimating the secret key once he has a chance to access a fragment of the generated keystream. Yet it remains to be clarified if intermittent chaos could be used for designing secure chaotic cryptosystems., Comment: RevTeX4, 11 pages, 15 figures

This paper presents a comprehensive analysis on the security of the Yi-Tan-Siew chaotic cipher proposed in [IEEE TCAS-I 49(12):1826-1829 (2002)]. A differential chosen-plaintext attack and a differential chosen-ciphertext attack are suggested to break the sub-key K, under the assumption that the time stamp can be altered by the attacker, which is reasonable in such attacks. Also, some security Problems about the sub-keys $\alpha$ and $\beta$ are clarified, from both theoretical and experimental points of view. Further analysis shows that the security of this cipher is independent of the use of the chaotic tent map, once the sub-key $K$ is removed via the proposed suggested differential chosen-plaintext attack., Comment: 5 pages, 3 figures, IEEEtrans.cls v 1.6

In 1998, M. S. Baptista proposed a chaotic cryptosystem, which has attracted much attention from the chaotic cryptography community: some of its modifications and also attacks have been reported in recent years. In [Phys. Lett. A 307 (2003) 22], we suggested a method to enhance the security of Baptista-type cryptosystem, which can successfully resist all proposed attacks. However, the enhanced Baptista-type cryptosystem has a nontrivial defect, which produces errors in the decrypted data with a generally small but nonzero probability, and the consequent error propagation exists. In this Letter, we analyze this defect and discuss how to rectify it. In addition, we point out some newly-found problems existing in all Baptista-type cryptosystems and consequently propose corresponding countermeasures., Comment: 13 pages, 2 figures