Robust implementation of foreground extraction and vessel segmentation for X-ray coronary angiography image sequence

Dynamic magnetic resonance imaging (dMRI) strikes a balance between reconstruction speed and image accuracy in medical imaging field. In this paper, an improved robust tensor principal component analysis (RTPCA) method is proposed to reconstruct the dynamic magnetic resonance imaging (MRI) from highly under-sampled K-space data. The MR reconstruction problem is formulated as a high-order low-rank tenor plus sparse tensor recovery problem, which is solved by robust tensor principal component analysis (RTPCA) with a new tensor nuclear norm (TNN). To further exploit the low-rank structures in multi-way data, the core matrix nuclear norm, extracted from the diagonal elements of the core tensor under tensor singular value decomposition (t-SVD) framework, is also integrated into TNN for enforcing the low-rank structure in MRI datasets. The experimental results show that the proposed method outperforms state-of-the-art methods in terms of both MR image reconstruction accuracy and computational efficiency on 3D and 4D experiment datasets, especially for 4D MR image reconstruction. Graphical abstract The flowchart of the proposed method to reconstruct the dynamic magnetic resonance imaging (MRI) from highly under-sampled K-space data in the kth iteration. To further exploit the low-rank structures in multi-way data, the core matrix nuclear norm, extracted from the diagonal elements of the core tensor under tensor singular value decomposition (t-SVD) framework, is also integrated into tensor nuclear norm (TNN) for enforcing the low-rank structure in MRI datasets. In each iteration, the first step is to get low-rank tensor ℓk - 1 by using soft thresholding on the singular values of ℓk - 1 = χk - 1 - ξk - 1, and an improved tensor nuclear norm method is proposed to process the low-rank tensor ℓk - 1 firstly. Then, the shrinkage operator is applied to ξk - 1 = χk - 1 - ℓk - 1 for sparse part ξk - 1. The final reconstructed d-MRI χk is obtained by enforcing data consistency that the residual in K-space is subtracted by the sum of the reconstructed low-rank tensor and sparse tensor.

The major challenges during the data acquisition process in an environmental wireless sensor network (EWSN) architecture are the presence of outliers and missing data. Outliers and missing data are ubiquitous in EWSN due to sensor failures, external noise, power dwindling, communication failures etc. Robust tensor principal component analysis (RTPCA) decomposes a noisy data tensor into a low-rank tensor and a sparse tensor, which can be exploited for the data recovery in EWSNs, where the low-rank component represents the intrinsic data tensor and the sparse component represents the gross outlier tensor. In this paper, a novel probabilistic outlier modelling scheme using multivariate Chebyshev's inequality hypothesis is proposed, which maps the sample population and the associated magnitudes of outliers with the spatio-temporal correlations in the acquired data. The inherent spatio-temporal and multi-attribute correlations in the EWSN data are established using singular value methods. A tensor nuclear norm (TNN) which extracts more temporal and multi-attribute correlations in the sensory data through block circulant matricization is used as the minimization function in RTPCA. In the forest surveillance scenario, usage of RTPCA results in a reconstruction accuracy of approximately 90% for a dataset with a high missing ratio of 0.95 and the outliers having largest possible magnitudes considered. For oceanographic data, in which the variance is lower, the simulations show similar performance for different outlier contaminations, although RPTCA performs better than other matrix and tensor data completion methods in terms of reconstruction accuracy.

Determination of abnormalities in coronary vessels and vessel segmentation in X-ray coronary angiography has been a challenging problem in recent years. This paper presents a new approach to increase segmentation success in angiography images. With the proposed method, the vessel location due to the change in brightness are detected in the angiography image sequences. In Angiography images Frangi Filter for vessel identification and the OTSU algorithm for automatic threshold detection in image thresholding that was tried to vessel segmentation. The contribution of the proposed method to the success of segmentation is shown wiht the experiments. Sensitivity, specifity and accuracy measures are used to analyze the segmentation success. The results are compared with the manual segmentation of the angio by the specialist. In the experiments, it is seen that the proposed method increases at least 3% of the segmantation success. In the best example, the accuracy has increased to 91,67 deni 93,81.

Precise segmentation of continuous vessels in X-ray coronary angiography (XCA) image sequences is pivotal for improving the diagnosis and treatment of coronary artery disease. However, motion artifacts and shadowing in XCA images significantly complicate the segmentation of accurate vessel segmentation. To address these challenges, we propose FlowVM-Net, a dynamic information-enhanced encoder-decoder architecture. The model incorporates an optical flow generation module to create temporal information across image sequences. Additionally, we introduce a wavelet dilated convolution visual state space model block based on VMamba as a fundamental component of the encoder-decoder structure. An attention-based optical flow feature fusion module is designed to effectively integrate sequential spatial features and temporal information. Furthermore, a composite loss function, including boundary difference over union loss, is employed to enhance the accuracy of vessel edge and thin vessel segmentation. We evaluate FlowVM-Net on a dataset of 542 samples, achieving a DSC of 85.17%, a sensitivity of 85.15%, and a quality score of 90.49% on the test set. The proposed network effectively preserves vessel continuity, accurately segments thin vessels, and demonstrates the potential of leveraging dynamic context from XCA sequences for improved coronary artery segmentation. The code for this project is available at: https://github.com/wgyhhhh/FlowVM-Net .

In the big data era, sequencing technology has produced a large number of biological sequencing data. Different views of the cancer genome data provide sufficient complementary information to explore genetic activity. The identification of differentially expressed genes from multiview cancer gene data is of great importance in cancer diagnosis and treatment. In this paper, we propose a novel method for identifying differentially expressed genes based on tensor robust principal component analysis (TRPCA), which extends the matrix method to the processing of multiway data. To identify differentially expressed genes, the plan is carried out as follows. First, multiview data containing cancer gene expression data from different sources are prepared. Second, the original tensor is decomposed into a sum of a low‐rank tensor and a sparse tensor using TRPCA. Third, the differentially expressed genes are considered to be sparse perturbed signals and then identified based on the sparse tensor. Fourth, the differentially expressed genes are evaluated using Gene Ontology and Gene Cards tools. The validity of the TRPCA method was tested using two sets of multiview data. The experimental results showed that our method is superior to the representative methods in efficiency and accuracy aspects.

Infrared small target detection is widely used for early warning, aircraft monitoring, ship monitoring, and so on, which requires the small target and its background to be represented and modeled effectively to achieve their complete separation. Low-rank sparse decomposition based on the structural features of infrared images has attracted much attention among many algorithms because of its good interpretability. Based on our study, we found some shortcomings in existing baseline methods, such as redundancy of constructing tensors and fixed compromising factors. A self-adaptive low-rank sparse tensor decomposition model for infrared dim small target detection is proposed in this paper. In this model, the entropy of image block is used for fast matching of non-local similar blocks to construct a better sparse tensor for small targets. An adaptive strategy of low-rank sparse tensor decomposition is proposed for different background environments, which adaptively determines the weight coefficient to achieve effective separation of background and small targets in different background environments. Tensor robust principal component analysis (TRPCA) was applied to achieve low-rank sparse tensor decomposition to reconstruct small targets and their backgrounds separately. Sufficient experiments on the various types data sets show that the proposed method is competitive.

Background: An organizational report indicates that heart attacks lead to seventy percent of human fatalities. Heart-related diseases strike people in India who range between the ages of 30-60 years. X-ray coronary angiography functions as the key procedure for detecting these conditions. The manual process of heart vessel segmentation by cardiologists becomes slow and needs significant effort because different professional skill levels affect the consistency of their output results. Objective: A study proposes automatic coronary angiography segmentation through artificial intelligence analysis of edge features to accurately detect the main cardiovascular artery system edges. Materials and methods: The Mendeley public database contained 100 patient images for training purposes and 34 images for validation purposes. The VGG Image Annotator tool served to create binary masks for annotation purposes. The analysis incorporated traditional edge detection methods that included Sobel, Prewitt, and Roberts along with Canny. Results: The tested model obtained 99% accuracy alongside a positive predictive value (PPV) of 96% and Sensitivity of 94% and Dice Coefficient of 95%. The upcoming research will focus on developing soft computing approaches for detecting stenosis in segmented images. Conclusion: The method demonstrates better performance metrics that show superior capability to previous techniques implemented in this field. New studies are needed to analyze soft computing techniques to identify vascular structures in coronary angiographic images.

In this paper we propose novel methods for completion (from limited samples) and de-noising of multilinear (tensor) data and as an application consider 3-D and 4- D (color) video data completion and de-noising. We exploit the recently proposed tensor-Singular Value Decomposition (t-SVD)[11]. Based on t-SVD, the notion of multilinear rank and a related tensor nuclear norm was proposed in [11] to characterize informational and structural complexity of multilinear data. We first show that videos with linear camera motion can be represented more efficiently using t-SVD compared to the approaches based on vectorizing or flattening of the tensors. Since efficiency in representation implies efficiency in recovery, we outline a tensor nuclear norm penalized algorithm for video completion from missing entries. Application of the proposed algorithm for video recovery from missing entries is shown to yield a superior performance over existing methods. We also consider the problem of tensor robust Principal Component Analysis (PCA) for de-noising 3-D video data from sparse random corruptions. We show superior performance of our method compared to the matrix robust PCA adapted to this setting as proposed in [4].

Probability-weighted tensor robust PCA with CP decomposition for hyperspectral image restoration

Nowadays, some tensor-based hyperspectral anomaly detection (HAD) approaches are still insufficient in utilizing the spatial-spectral structure information of hyperspectral images (HSIs), resulting in the inability to isolate the background and abnormal targets well. In this letter, an enhanced tensor low-rank representation learning (ETLR) model is proposed for HAD. Specifically, the original 3-D hyperspectral image (HSI) data is firstly decomposed into a structural background component, an anomaly component and a noise component. Among them, with the help of multi-subspace learning technology, the structural background component is reformulated by the t-product of the background dictionary tensor and the corresponding coefficient tensor. Then, tensor nuclear norm (TNN) is adopted to preserve the global low-rank property of the background component in both spatial and spectral dimensions. For the abnormal component, an ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1,1</sub> -norm is designed to enhance the group sparsity of abnormal pixels. For the noise component, a tensor <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> -norm constraint is imposed to suppress the confusion of noise and anomalies. Meanwhile, a robust dictionary tensor that can adequately characterize the background is constructed by using tensor robust principal component analysis (TRPCA). Furthermore, to reduce the interference of redundant information on detection accuracy, the optimal clustering framework (OCF) method is utilized for band selection. Finally, extensive experiments on one simulated and three real HSI datasets confirm that our algorithm is superior than current HAD algorithms.

Chapter 6 - Tensor principal component analysis

Heart disease is the leading cause of death worldwide, responsible for 16% of the world’s total deaths. Coronary artery disease is the most common type of heart disease and is caused by plaque buildup in the wall of the arteries. X-ray coronary angiography is the gold standard for assessing coronary artery disease via estimation of the percentage of narrowing, also known as stenosis. However, errors in physicians’ interpretation of stenosis severity could lead to overuse and underuse of revascularization. Automated techniques can help clinicians in their decision-making. In this paper, we review state-of-the-art techniques developed for coronary artery segmentation, vessel modeling, and stenosis detection. The methods are categorized using criteria such as segmentation methods, evaluation metrics, and validation approaches.

Retinal vessel tree extraction is a complex and computationally intensive task used in several medical and biometric applications. The emergence of portable biometric authentication applications, as well as on-site biomedical diagnostics, raises the need for hardware-accelerated, power-efficient architectures that can satisfy the performance and accuracy requirements of retinal vessel tree extraction. As such, this paper presents a VLSI implementation of a retina vessel segmentation system, in an attempt to illustrate the advantages and performance benefits that result from a dedicated VLSI solution. The proposed design implements an unsupervised, vessel segmentation algorithm, which utilizes match filtering with signed integers to enhance the difference between the blood vessels and the rest of the retina. The design simplifies the process of obtaining a binary map of the vessel tree by using parallel processing and efficient resource sharing, thus offering real-time performance. FPGA-based simulation results indicate significant performance improvements (up to 90x) when compared to existing hardware and software implementations.

Diabetic Retinopathy (DR) is a complication of long-standing, unchecked diabetes and one of the leading causes of blindness in the world. This paper focuses on improved and robust methods to extract some of the features of DR, viz. Blood Vessels and Exudates. Blood vessels are segmented using multiple morphological and thresholding operations. For the segmentation of exudates, k-means clustering and contour detection on the original images are used. Extensive noise reduction is performed to remove false positives from the vessel segmentation algorithm's results. The localization of Optic Disc using k-means clustering and template matching is also performed. Lastly, this paper presents a Deep Convolutional Neural Network (DCNN) model with 14 Convolutional Layers and 2 Fully Connected Layers, for the automatic, binary diagnosis of DR. The vessel segmentation, optic disc localization and DCNN achieve accuracies of 95.93%, 98.77% and 75.73% respectively. The source code and pre-trained model are available https://github.com/Sohambasu07/DR_2021

Tensor Robust Principal Component Analysis (TRPCA) aims at recovering the low-rank and sparse components from target tensor, which has extensive applications in multi-dimensional data recovery. However, most of the existing methods only exploit the global low-rank of image data, which result in missing local details in the recovered data. To restore the data more accurately, we propose a new TRPCA method which simultaneously combines the model-based method and data-driven approaches to preserve the global structure and fine local information. Specially, we pick the tensor nuclear norm to characterize the global correlation and a convolutional neural network(CNN) denoiser which reserves the local detail. Then, a flexible alternating direction method of multipliers (ADMM) is designed to deal with the proposed optimization model. Extensive experiments on various types of tensor data illustrate that our model enhances performance compared to state-of-the-art methods.