Poor correlation between coronary artery calcification and obstructive coronary artery disease in an end-stage renal disease patient

Vascular calcification is highly prevalent and often severe in patients with chronic kidney disease. Arterial calcification in patients with chronic kidney disease can result from the deposition of mineral along the intimal layer of arteries in conjunction with atheromatous plaques or from calcium deposition in the medial wall of arteries, also known as Monckeberg's sclerosis. Whether coronary artery calcium scores as measured by electron beam computed tomography correlate with occlusive atherosclerotic disease in the dialysis population is uncertain. Here we report a case of an asymptomatic patient with diabetes mellitus and end-stage renal disease undergoing maintenance hemodialysis, who was found to have extremely elevated coronary artery calcium scores on electron beam computed tomography, but varied degrees of atherosclerotic plaque in her coronary arteries on coronary angiography. This suggests that in addition to the calcification anticipated in a remodeled intima, a proportion of the calcification is also likely to be in the arterial media. Thus, this case demonstrates that even an extremely high coronary calcium score may not be a satisfactory surrogate marker for obstructive atherosclerosis in elderly diabetic dialysis patients.     

Automatic liver tumor segmentation on multiphase computed tomography volume using SegNet deep neural network and K-means clustering

Liver and liver tumor segmentations are essential in computer-aided systems for diagnosing liver tumors. These systems must operate on multiphase computed tomography (CT) images instead of a single phase for accurate diagnosis for clinical applications. We have proposed a framework that can perform segmentation from quadriphasic CT data. The liver was segmented using a fine-tuned SegNet model and the liver tumor was segmented using the K-means clustering method coupled with a power-law transformation-based image enhancement technique. The best values for liver segmentation achieved were: Dice Coefficient = 96.46 ± 0.48%, Jaccard Index = 93.16 ± 0.89%, volumetric overlap error = 6.84 ± 0.89% and average symmetric surface distance = 0.59 ± 0.3 mm and the results for liver tumor delineation were Dice Coefficient = 85.07 ± 4.5%, Jaccard Index = 74.29 ± 6.8%, volumetric overlap error = 25.71 ± 6.8% and average symmetric surface distance = 1.14 ± 1.3 mm. The proposed liver segmentation method based on deep learning is fully automatic, robust, and effective for all phases. The image enhancement technique has shown promising results and aided in better liver tumor segmentation. The liver tumors were segmented satisfactorily; however, improvements concerning false positive reduction can further increase the accuracy.     

基于双参数颗粒分析的纹理分割

形态学颗粒分析是一种有效的颗粒纹理图像分割方法,但由于单一的颗粒度参数,使得其难以分割如颗粒度相同而空间排列不同的纹理图像,为此提出了一种基于双参数颗粒分析的纹理图像分割方法,该方法将传统的颗粒分析从单一参数扩展到以颗粒度和空间位置为参数的二维空间,使得扩展后的颗粒分析通过分布函数不仅能够提取纹理的颗粒度特征,而且能够获取纹理的空间排列特征,克服了传统颗粒分析难以区分颗粒度相同而空间排列不同纹理区域的问题.仿真实验表明,该方法在运算复杂度增加不大的情况下,纹理分割效果优于颗粒分析法,区域像素错分率低于颗粒分析法和Gabor滤波器法.     

冠状动脉血管阻抗估计系统及其临床应用

利用PC机和图像、信号采集设备，建立冠状动脉血管阻抗的估计系统。该系统从血管内超声和血压检测仪器采集冠状动脉内的超声图像、血流多普勒和血压信号，通过提取管腔的截面积曲线和流速信息，获取血流量曲线。结合血压曲线，计算冠状动脉的等效阻抗。系统对不同程度冠脉狭窄、心肌架桥和微循环障碍病人进行临床应用，结果表明：血管阻抗可以反映血管供血和扩张能力与不同类型、程度病症间的关系，有望用于医学临床的辅助诊断。     

COVID-19 lung infection segmentation from chest CT images based on CAPA-ResUNet

Coronavirus disease 2019 (COVID-19) epidemic has devastating effects on personal health around the world. It is significant to achieve accurate segmentation of pulmonary infection regions, which is an early indicator of disease. To solve this problem, a deep learning model, namely, the content-aware pre-activated residual UNet (CAPA-ResUNet), was proposed for segmenting COVID-19 lesions from CT slices. In this network, the pre-activated residual block was used for down-sampling to solve the problems of complex foreground and large fluctuations of distribution in datasets during training and to avoid gradient disappearance. The area loss function based on the false segmentation regions was proposed to solve the problem of fuzzy boundary of the lesion area. This model was evaluated by the public dataset (COVID-19 Lung CT Lesion Segmentation Challenge—2020) and compared its performance with those of classical models. Our method gains an advantage over other models in multiple metrics. Such as the Dice coefficient, specificity (Spe), and intersection over union (IoU), our CAPA-ResUNet obtained 0.775 points, 0.972 points, and 0.646 points, respectively. The Dice coefficient of our model was 2.51% higher than Content-aware residual UNet (CARes-UNet). The code is available at https://github.com/malu108/LungInfectionSeg .     

Brain tumour segmentation from MRI image using genetic algorithm with fuzzy initialisation and seeded modified region growing (GFSMRG) method

Segmentation is an important aspect of medical image processing. For improving the accuracy in the detection of tumour and improving the speed of execution in segmentation, a new genetic-based genetic algorithm with fuzzy initialisation and seeded modified region growing (GFSMRG) method with back propagation neural network (BPNN) is proposed and presented in this paper. The proposed system consists of four steps: pre-processing, segmentation, feature extraction and classification. The GFSMRG method and its components, feature extraction and classification are explained in detail. The performance analysis of the GFSMRG method with respect to accuracy and time complexity are also discussed. The performance of this method has been validated both quantitatively and qualitatively by using the performance metrics such as Similarity Index, Jaccard Index, Sensitivity, Specificity and Accuracy.     

基于空间邻域信息的二维模糊聚类图像分割

传统模糊C均值聚类(FCM)算法进行图像分割时仅利用了像素的灰度信息,并且使用对噪声较敏感的欧氏距离作为像素与聚类中心距离度量的标准,因此抗噪性能较差.为了克服传统FCM算法的局限性,本文提出了一种基于空间邻域信息的二维模糊聚类图像分割方法(2DFCM).该方法利用二维直方图描述的像素邻域关系属性,一方面为聚类提供较准确的初始聚类中心,从而避免聚类中的死点问题;另一方面通过提出聚类中心同时在像素值、像素邻域值二维方向上进行更新的思想,建立了包含邻域信息的新的聚类目标函数,实现了图像的分割.实验结果表明,这种方法抗噪能力强、收敛速度快,是一种有效的模糊聚类图像分割方法.     

An integrated feature frame work for automated segmentation of COVID‐19 infection from lung CT images

The novel coronavirus disease (SARS‐CoV‐2 or COVID‐19) is spreading across the world and is affecting public health and the world economy. Artificial Intelligence (AI) can play a key role in enhancing COVID‐19 detection. However, lung infection by COVID‐19 is not quantifiable due to a lack of studies and the difficulty involved in the collection of large datasets. Segmentation is a preferred technique to quantify and contour the COVID‐19 region on the lungs using computed tomography (CT) scan images. To address the dataset problem, we propose a deep neural network (DNN) model trained on a limited dataset where features are selected using a region‐specific approach. Specifically, we apply the Zernike moment (ZM) and gray level co‐occurrence matrix (GLCM) to extract the unique shape and texture features. The feature vectors computed from these techniques enable segmentation that illustrates the severity of the COVID‐19 infection. The proposed algorithm was compared with other existing state‐of‐the‐art deep neural networks using the Radiopedia and COVID‐19 CT Segmentation datasets presented specificity, sensitivity, sensitivity, mean absolute error (MAE), enhance‐alignment measure (EM_φ), and structure measure (S_m) of 0.942, 0.701, 0.082, 0.867, and 0.783, respectively. The metrics demonstrate the performance of the model in quantifying the COVID‐19 infection with limited datasets.     

A new images segmentation method based on modified particle swarm optimization algorithm

The partitioning of an image into several constituent components is called image segmentation. Many approaches have been developed; one of them is the particle swarm optimization (PSO) algorithm, which is widely used. PSO algorithm is one of the most recent stochastic optimization strategies. In this article, a new efficient technique for the magnetic resonance imaging (MRI) brain images segmentation thematic based on PSO is proposed. The proposed algorithm presents an improved variant of PSO, which is particularly designed for optimal segmentation and it is called modified particle swarm optimization. The fitness function is used to evaluate all the particle swarm in order to arrange them in a descending order. The algorithm is evaluated by performance measures such as run time execution and the quality of the image after segmentation. The performance of the segmentation process is demonstrated by using a defined set of benchmark images and compared against conventional PSO, genetic algorithm, and PSO with Mahalanobis distance based segmentation methods. Then we applied our method on MRI brain image to determinate normal and pathological tissues. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 265–271, 2013     

3D brain tumor segmentation in MRI images based on a modified PSO technique

Three-dimensional (3D) brain tumor segmentation is a clinical requirement for brain tumor diagnosis and radiotherapy planning. This is a challenging task due to variation in type, size, location, and shape of tumors. Several methods such as particle swarm optimization (PSO) algorithm formed a topological relationship for the slices that converts 2D images into 3D magnetic resonance imaging (MRI) images which does not provide accurate results and they depend on the number of input sections, positions, and the shape of the MRI images. In this article, we propose an efficient 3D brain tumor segmentation technique called modified particle swarm optimization. Also, segmentation results are compared with Darwinian particle swarm optimization (DPSO) and fractional-order Darwinian particle swarm optimization (FODPSO) approaches. The experimental results show that our method succeeded 3D segmentation with 97.6% of accuracy rate more efficient if compared with the DPSO and FODPSO methods with 78.1% and 70.21% for the case of T1-C modality.     

Supervised method for blood vessel segmentation from coronary angiogram images using 7-D feature vector

With the recent advancement in medical image processing field and sophisticated simulation tools it has been possible to acquire useful information from raw images for different parts of the body. Coronary artery segmentation is the fundamental component which extract significant features from angiogram images. Cardiac catheterization is an invasive diagnostic procedure that provides important information about the structure and function of heart. The procedure usually involves X-ray images of heart, arteries using coronary angiography. The resultant images (coronary angiogram) are considered as best of way to diagnose cardiac heart disease. The main focus of coronary angiography is to find the blockage in major blood vessels, however if the blockage is not found in large blood vessels and patient persists to have pain (angina) then it is concluded that the patient is having micro vascular disease (MVD). MVD is caused by blockage or narrowing of small blood vessels in heart, unfortunately there is no specific test to diagnose MVD but it is common in people having diabetes and blood pressure. This paper proposes an automated method of vessel segmentation from coronary angiogram images using radial basis function and moment invariant-based features to extract the small blood vessel for diagnosis of MVD. Experimental results show that the proposed method is capable of extracting small blood vessels from coronary artery and can be a basis to identify key characteristics for MVD. The dataset of angiogram images have been provided by ISRA University Hospital and MATLAB is used for implementing the proposed method.     

Lung computed axial tomography image segmentation using possibilistic fuzzy C-means approach for computer aided diagnosis system

Soft computing is an associate rising field that plays a crucial half in the area of engineering and science. One of the most significant applications of soft computing is image segmentation. It focuses on an exploiting tolerance of imprecision and uncertainty. Segmentation supported soft computing remains a difficult task within the medical field. Medical images are habitually used in the segmentation process to extract the meaningful portions and to know and clarify the condition of the particular patient. In this article, we implement an efficient possibilistic fuzzy C-means (PFCM) approach to segment the lung portion in the computed tomography (CT) image and the result shows that it improves the segmentation accuracy upto 98.5012% and results are compared with existing segmenting approaches like fuzzy possibilistic C-means method, fuzzy bitplane method and so forth. Also, the PFCM approach increases the diagnostic accuracy of the computer aided diagnosis system using CT images. The radiologist may utilize this computer aided diagnosis system results as a second opinion of their diagnosed results.     

Fuzzy entropy‐based MR brain image segmentation using modified particle swarm optimization

This article presents an image segmentation technique based on fuzzy entropy, which is applied to magnetic resonance (MR) brain images in order to detect brain tumors. The proposed method performs image segmentation based on adaptive thresholding of the input MR images. The image is classified into two membership functions (MFs) of the fuzzy region: Z‐function and S‐function. The optimal parameters of these fuzzy MFs are obtained using modified particle swarm optimization (MPSO) algorithm. The objective function for obtaining the optimal fuzzy MF parameters is considered to be the maximum fuzzy entropy. Through a number of examples, The performance is compared with existing entropy based object segmentation approaches and the superiority of the proposed method is demonstrated. The experimental results are compared with the exhaustive search method and Otsu's segmentation technique. The result shows the proposed fuzzy entropy‐based segmentation method optimized using MPSO achieves maximum entropy with proper segmentation of infected areas and with minimum computational time. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 281–288, 2013     

Leakage suppression in human airway tree segmentation using shape optimization based on fuzzy connectivity method

Human airway tree segmentation from computed tomography (CT) images is a very important step for virtual bronchoscopic applications. Imaging artifacts or thin airway walls decrease the contrast between the air and airway wall and make the segmented region to leak from inside of the airway to the parenchyma. This in turn begins the leakage phenomenon to build and then large parts of the lung parenchyma might be erroneously marked as the airway tree instead. Unfortunately, existing methods typically do not sufficiently extract the necessary peripheral airways needed to plan a procedure. In this article, we propose a new shape based human airway segmentation scheme to suppress the leakage into surrounding area which is based on fuzzy connectivity (FC) method. Complex medical image features such as weak boundary edges in the CT images of the lung parenchyma have fuzzy properties and can be described by FC in many extents. Our method aims to embed a mathematical shape optimization approach in a FC algorithm. Using the partial derivatives of the image data that is minimized with respect to the polar angle and cylindrical axis direction, a proper cost function based on cylindrical features of the airway branches is proposed. This approach retains the cylindrical properties of the airway branches during the segmentation process. The proposed cost function includes two parts named cylindrical‐shape feature and smoothed final error term. The former term arranges the underlying voxels on a cylindrical shape and the latter term controls and smoothes the final error considering the local minima's problem. To evaluate the efficiency of our proposed optimization technique in term of segmentation accuracy, the cost function is first applied to the simulated data with the spongy shape of leakage and the leakage origin. The impact of each term of the proposed cost function on the final error and the convergence of the algorithm are also evaluated. Then, the cost function with best proper parameters is applied to real image dataset. Comparisons of the results on multidetector CT chest scans show that our segmentation algorithm outperforms the fuzzy region growing algorithm. Quantitative comparisons with manually segmented airway trees also indicate high sensitivity of our segmentation algorithm on peripheral airways. On the basis of the results, it is concluded that the proposed method is able to detect more branches up to the sixth generation with no leakage which provides 2–3 more generations of airways than several other methods do. The extracted airway trees enable image‐guided bronchoscopy to go deeper into the human lung periphery than past studies. The novelty of our proposed method is to apply a shape optimization approach embedded in an efficient FC segmentation algorithm. Hence, our method prevents leakage from its origination in contrast to most previously published works that just set their algorithms to repeat the segmentation steps to reduce leakage. As our results indicate leakage suppression in human airway segmentation instead of readjusting the segmentation parameters, more airway branches can be extracted with correct shape. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 71–84, 2013     

A snake‐based approach to automated segmentation of tongue image using polar edge detector

Tongue diagnosis, one of the most important diagnosis methods of Traditional Chinese Medicine, is very competitive as a candidate of remote diagnosis method because of its simplicity and noninvasiveness. Recently, considerable research interests have been given to the development of automated tongue segmentation technologies, which is difficult due to the complexity of pathological tongue, variance of tongue shape, and interference of the lips. In this paper, we propose a novel automated tongue segmentation method via combining polar edge detector and active contour model (ACM) technique. First, a polar edge detector is presented to effectively extract the edge of the tongue body. Then we design an edge filtering scheme to avoid the adverse interference from the nontongue boundary. After edge filtering, a local adaptive edge bi‐thresholding algorithm is introduced to perform the edge binarization. Finally, a heuristic initialization and an ACM are proposed to segment the tongue body from the image. The experimental results demonstrate that the proposed method can segment the tongue body accurately and effectively. A quantitative evaluation on 200 images indicates that the normalized mean distance to the closest point is 0.48%, and the average true positive percent of our method is 97.1%. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 16, 103–112, 2006.     

Coronary artery bypass grafting versus percutaneous coronary intervention in end-stage kidney disease: A systematic review and meta-analysis of clinical studies

The most significant complication of end-stage kidney disease (ESKD) is cardiovascular disease, mainly coronary artery disease (CAD). Although the effective treatment of CAD is an important prognostic factor, whether percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG) is better for treating CAD in this group of patients is still controversial. We searched Pubmed/Medline, Web of Science, Embase, the Cochrane Central Register of Controlled Trials articles that compared the outcomes of CABG versus PCI in patients with ESKD requiring dialysis. A total of 10 observational studies with 39,666 patients were included. Our analysis showed that when compared to PCI, CABG had lower risk of need for repeat revascularization (relative risk [RR] = 2.25, 95% confidence interval [CI] 2.1–2.42, p < 0.00001) and cardiovascular death (RR = 1.19, 95% CI 1.14–1.23, p < 0.00001) and higher risk for short-term mortality (RR = 0.43, 95% CI 0.38–0.48, p < 0.00001). There was no statistically significant difference between the PCI and CABG groups in the risk for late mortality (RR = 1.05, 95% CI 0.97–1.14, p = 0.25), myocardial infarction (RR = 1.05, 95% CI 0.46–2.36, p = 0.91) or stroke (RR = 1.02, 95% CI 0.64–1.61, p = 0.95). This meta-analysis showed that in ESKD patients requiring dialysis, CABG was superior to PCI in regard to cardiovascular death and need for repeat revascularization and inferior to PCI in regard to short term mortality. However, this meta-analysis has limitations and needs confirmation with large randomized controlled trials.     

Brain tumor severity analysis using modified multi‐texton histogram and hybrid kernel SVM

Magnetic resonance image (MRI) segmentation refers to a process of assigning labels to set of pixels or multiple regions. It plays a major role in the field of biomedical applications as it is widely used by the radiologists to segment the medical images input into meaningful regions. In recent years, various brain tumor detection techniques are presented in the literature. In this article, we have developed an approach to brain tumor detection and severity analysis is done using the various measures. The proposed approach comprises of preprocessing, segmentation, feature extraction, and classification. In preprocessing steps, we need to perform skull stripping and then, anisotropic filtering is applied to make image suitable for extracting features. In feature extraction, we have modified the multi‐texton histogram (MTH) technique to improve the feature extraction. In the classification stage, the hybrid kernel is designed and applied to training of support vector machine to perform automatic detection of tumor region in MRI images. For comparison analysis, our proposed approach is compared with the existing works using K‐cross fold validation method. From the results, we can conclude that the modified multi‐texton histogram with non‐linear kernels has shown the accuracy of 86% but the MTH with non‐linear kernels shows the accuracy of 83.8%.     

A fully automated hybrid methodology using Cuckoo‐based fuzzy clustering technique for magnetic resonance brain image segmentation

This article aims at developing an automated hybrid algorithm using Cuckoo Based Search (CBS) and interval type‐2 fuzzy based clustering, so as to exhibit efficient magnetic resonance (MR) brain image segmentation. An automatic MR brain image segmentation facilitates and enables a radiologist to have a brief review and easy analysis of complicated tumor regions of imprecise gray level regions with minimal user interface. The tumor region having severe intensity variations and suffering from poor boundaries are to be detected by the proposed hybrid technique that could ease the process of clinical diagnosis and this tends to be the core subject of this article. The ability of the proposed technique is compared using standard comparison parameters such as mean squared error, peak signal to noise ratio, computational time, Dice Overlap Index, and Jaccard T animoto C oefficient Index. The proposed CBS combined with interval type‐2 fuzzy based clustering produces a sensitivity of 0.7143 and specificity of 0.9375, which are far better than the conventional techniques such as kernel based, entropy based, graph‐cut based, and self‐organizing maps based clustering. Appreciable segmentation results of tumor region that enhances clinical diagnosis is made available through this article and two of the radiologists who have hands on experience in the field of radiology have extended their support in validating the efficiency of the proposed methodology and have given their consent in utilizing the proposed methodology in the processes of clinical oncology.     

Long-term viability of coronary artery smooth muscle cells on poly(L-lactide-co-epsilon-caprolactone) nanofibrous scaffold indicates its potential for blood vessel tissue engineering.

Biodegradable polymer nanofibres have been extensively studied as cell culture scaffolds in tissue engineering. However, long-term in vitro studies of cell-nanofibre interactions were rarely reported and successful organ regeneration using tissue engineering techniques may take months (e.g. blood vessel tissue engineering). Understanding the long-term interaction between cells and nanofibrous scaffolds (NFS) is crucial in material selection, design and processing of the tissue engineering scaffolds. In this study, poly(L-lactide-co-epsilon-caprolactone) [P(LLA-CL)] (70:30) copolymer NFS were produced by electrospinning. Porcine coronary artery smooth muscle cells (PCASMCs) were seeded and cultured on the scaffold to evaluate cell-nanofibre interactions for up to 105 days. A favourable interaction between this scaffold and PCASMCs was demonstrated by cell viability assay, scanning electron microscopy, histological staining and extracellular matrix (ECM) secretion. Degradation behaviours of the scaffolds with or without PCASMC culture were determined by mechanical testing and gel permeation chromatography (GPC). The results showed that the PCASMCs attached and proliferated well on the P(LLA-CL) NFS. Large amount of ECM protein secretion was observed after 50 days of culture. Multilayers of aligned oriented PCASMCs were formed on the scaffold after two months of in vitro culture. In the degradation study, the PCASMCs were not shown to significantly increase the degradation rate of the scaffolds for up to 105 days of culture. The in vitro degradation time of the scaffold could be as long as eight months by extrapolating the results from GPC. These observations further supported the potential use of the P(LLA-CL) nanofibre in blood vessel tissue engineering.     

Lossless color medical image compression using adaptive block‐based encoding for human computed tomographic images

The advancement in medical imaging systems such as computed tomography (CT), magnetic resonance imaging (MRI), positron emitted tomography (PET), and computed radiography (CR) produces huge amount of volumetric images about various anatomical structure of human body. There exists a need for lossless compression of these images for storage and communication purposes. The major issue in medical image is the sequence of operations to be performed for compression and decompression should not degrade the original quality of the image, it should be compressed loss lessly. In this article, we proposed a lossless method of volumetric medical image compression and decompression using adaptive block‐based encoding technique. The algorithm is tested for different sets of CT color images using Matlab. The Digital Imaging and Communications in Medicine (DICOM) images are compressed using the proposed algorithm and stored as DICOM formatted images. The inverse process of adaptive block‐based algorithm is used to reconstruct the original image information loss lessly from the compressed DICOM files. We present the simulation results for large set of human color CT images to produce a comparative analysis of the proposed methodology with block‐based compression, and JPEG2000 lossless image compression technique. This article finally proves the proposed methodology gives better compression ratio than block‐based coding and computationally better than JPEG 2000 coding. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 227–234, 2013