Xuelong Wu¹, Junsheng Wang^1,*, Zhongyao Li¹, Yisheng Miao¹, Chengpeng Xue¹, Yuling Lang², Decai Kong², Xiaoying Ma², Haibao Qiao²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3703-3720, 2024, DOI:10.32604/cmc.2024.047027

Abstract X-ray Computed Tomography (XCT) enables non-destructive acquisition of the internal structure of materials, and image segmentation plays a crucial role in analyzing material XCT images. This paper proposes an image segmentation method based on the Segment Anything model (SAM). We constructed a dataset of carbide in nickel-based single crystal superalloys XCT images and preprocessed the images using median filtering, histogram equalization, and gamma correction. Subsequently, SAM was fine-tuned to adapt to the task of material XCT image segmentation, resulting in Material-SAM. We compared the performance of threshold segmentation, SAM, U-Net model, and Material-SAM. Our method achieved 88.45% Class Pixel Accuracy… More >

Huan Wang¹, Shi Qiu^1,2,*, Benyue Zhang¹, Lixuan Xiao³

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1569-1589, 2024, DOI:10.32604/cmc.2023.046821

Abstract Lung cancer is a malady of the lungs that gravely jeopardizes human health. Therefore, early detection and treatment are paramount for the preservation of human life. Lung computed tomography (CT) image sequences can explicitly delineate the pathological condition of the lungs. To meet the imperative for accurate diagnosis by physicians, expeditious segmentation of the region harboring lung cancer is of utmost significance. We utilize computer-aided methods to emulate the diagnostic process in which physicians concentrate on lung cancer in a sequential manner, erect an interpretable model, and attain segmentation of lung cancer. The specific advancements can be encapsulated as follows:… More >

Pier Paolo Bassareo^1,2,3,*, Aurelio Secinaro⁴, Paolo Ciliberti⁵, Massimo Chessa^6,7, Marco Alfonso Perrone^5,8, Kevin Patrick Walsh^1,2,3, Colin Joseph Mcmahon^2,3

Congenital Heart Disease, Vol.18, No.6, pp. 595-610, 2023, DOI:10.32604/chd.2023.046229

Abstract Background: Congenital absence of pericardium (CAP), also known as pericardial agenesis, represents an uncommon cardiac abnormality and mostly incidental finding. It can be subdivided into complete and partial (left or right-sided) forms. Because of its infrequency, just case reports and a few case series have been released so far. This paper represents the largest systematic review in the field. Nine features (age at diagnosis, type, gender, clinical presentation, electrocardiography, imaging (ultrasounds, CT/MRI), concomitant cardiac defects, and outcome) were analysed. Methods: The electronic database PubMed was investigated from its establishment up to July 15^th, 2023. Just case reports and case series… More >

Muhammad Naeem Akram¹, Muhammad Usman Yaseen¹, Muhammad Waqar¹, Muhammad Imran^1,*, Aftab Hussain²

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3727-3744, 2023, DOI:10.32604/cmc.2023.041855

Abstract This study presents a deep learning model for efficient intracranial hemorrhage (ICH) detection and subtype classification on non-contrast head computed tomography (CT) images. ICH refers to bleeding in the skull, leading to the most critical life-threatening health condition requiring rapid and accurate diagnosis. It is classified as intra-axial hemorrhage (intraventricular, intraparenchymal) and extra-axial hemorrhage (subdural, epidural, subarachnoid) based on the bleeding location inside the skull. Many computer-aided diagnoses (CAD)-based schemes have been proposed for ICH detection and classification at both slice and scan levels. However, these approaches perform only binary classification and suffer from a large number of parameters, which… More >

Fangzhou Wang^a,* , Gennifer A. Riley^b, Munonyedi Egbo^c, Melanie M. Derby^b, Gisuk Hwang^c, Xianglin Li^a,†

Frontiers in Heat and Mass Transfer, Vol.15, pp. 1-8, 2020, DOI:10.5098/hmt.15.1

Abstract This study conducts pore-scale simulations and experiments to estimate the permeability of two different types of porous materials: metal foams and sintered copper particles with porosities of approximately 0.9 and 0.4, respectively. The integration of micro X-ray computed tomography with pore-scale computational fluid dynamics simulations develops a unique tool to capture the pore-scale geometry of porous media and accurately predict non-isotropic permeability of porous media. The pore-scale simulation not only results in improved prediction accuracy but also has the capability to capture non-isotropic properties of heterogeneous materials, which is a huge challenge for empirical correlations, volume averaged simulations, and simulations… More >

Liwei Deng¹, Yuanzhi Zhang¹, Xin Yang^2,*, Sijuan Huang², Jing Wang^3,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2671-2684, 2023, DOI:10.32604/cmc.2023.036642

Abstract High-resolution medical images have important medical value, but are difficult to obtain directly. Limited by hardware equipment and patient’s physical condition, the resolution of directly acquired medical images is often not high. Therefore, many researchers have thought of using super-resolution algorithms for secondary processing to obtain high-resolution medical images. However, current super-resolution algorithms only work on a single scale, and multiple networks need to be trained when super-resolution images of different scales are needed. This definitely raises the cost of acquiring high-resolution medical images. Thus, we propose a multi-scale super-resolution algorithm using meta-learning. The algorithm combines a meta-learning approach with… More >

Quan Yan¹, Yunfan Ye¹, Jing Xia¹, Zhiping Cai^1,*, Zhilin Wang², Qiang Ni³

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2545-2558, 2023, DOI:10.32604/iasc.2023.029857

Abstract Computed tomography has made significant advances since its introduction in the early 1970s, where researchers have mainly focused on the quality of image reconstruction in the early stage. However, radiation exposure poses a health risk, prompting the demand of the lowest possible dose when carrying out CT examinations. To acquire high-quality reconstruction images with low dose radiation, CT reconstruction techniques have evolved from conventional reconstruction such as analytical and iterative reconstruction, to reconstruction methods based on artificial intelligence (AI). All these efforts are devoted to constructing high-quality images using only low doses with fast reconstruction speed. In particular, conventional reconstruction… More >

Azhar Tursynova¹, Batyrkhan Omarov^1,2, Natalya Tukenova^3,*, Indira Salgozha⁴, Onergul Khaaval³, Rinat Ramazanov⁵, Bagdat Ospanov⁵

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1431-1446, 2023, DOI:10.32604/cmc.2023.034400

Abstract In the field of stroke imaging, deep learning (DL) has enormous untapped potential. When clinically significant symptoms of a cerebral stroke are detected, it is crucial to make an urgent diagnosis using available imaging techniques such as computed tomography (CT) scans. The purpose of this work is to classify brain CT images as normal, surviving ischemia or cerebral hemorrhage based on the convolutional neural network (CNN) model. In this study, we propose a computer-aided diagnostic system (CAD) for categorizing cerebral strokes using computed tomography images. Horizontal flip data magnification techniques were used to obtain more accurate categorization. Image Data Generator… More >

Maryam Wardah¹, Muhammad Mateen^1,*, Tauqeer Safdar Malik², Mohammad Eid Alzahrani³, Adil Fahad³, Abdulmohsen Almalawi⁴, Rizwan Ali Naqvi⁵

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2283-2299, 2023, DOI:10.32604/cmc.2023.030706

Abstract Brain hemorrhage is a serious and life-threatening condition. It can cause permanent and lifelong disability even when it is not fatal. The word hemorrhage denotes leakage of blood within the brain and this leakage of blood from capillaries causes stroke and adequate supply of oxygen to the brain is hindered. Modern imaging methods such as computed tomography (CT) and magnetic resonance imaging (MRI) are employed to get an idea regarding the extent of the damage. An early diagnosis and treatment can save lives and limit the adverse effects of a brain hemorrhage. In this case, a deep neural network (DNN)… More >

Yudong Zhang¹, Muhammad Attique Khan², Ziquan Zhu¹, Shuihua Wang^1,*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 13-26, 2023, DOI:10.32604/csse.2023.034172

Abstract (Aim) The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022. Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients. (Method) Two datasets are chosen for this study. The multiple-way data augmentation, including speckle noise, random translation, scaling, salt-and-pepper noise, vertical shear, Gamma correction, rotation, Gaussian noise, and horizontal shear, is harnessed to increase the size of the training set. Then, the SqueezeNet (SN) with complex bypass is used to generate SN features. Finally, the extreme learning machine (ELM) is used to serve as the classifier due to its simplicity of usage,… More >