R. Shijitha^1,*, P. Karthigaikumar², A. Stanly Paul²

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5233-5249, 2022, DOI:10.32604/cmc.2022.020227 - 11 October 2021

Abstract Brain Hemorrhagic stroke is a serious malady that is caused by the drop in blood flow through the brain and causes the brain to malfunction. Precise segmentation of brain hemorrhage is crucial, so an enhanced segmentation is carried out in this research work. The brain image of various patients has taken using an MRI scanner by the utilization of T1, T2, and FLAIR sequence. This work aims to segment the Brain Hemorrhagic stroke using deep learning-based Multi-resolution UNet (multires UNet) through morphological operations. It is hard to precisely segment the brain lesions to extract the More >

Liqiong Ding¹, Pingsheng Li², Liu He¹, Fengnan Xu¹, Jieqiong Ding^3,*, Binhua Luo^1,*

Journal of Renewable Materials, Vol.10, No.3, pp. 767-780, 2022, DOI:10.32604/jrm.2022.016903 - 28 September 2021

Abstract The development of contrast agents that can be activated by multiple modes is of great significance for tumor diagnosis. In this study, the lactoferrin (Lf)-conjugated polylactic acid (PLLA) nanobubbles (Lf-PLLA NBs) were used to encapsulate liquid perfluoropentane (PFP) with the double emulsion method, creating PFP loaded (PFP/Lf-PLLA) NBs for the ultrasound/magnetic resonance dual-modality imaging of subcutaneous tumor. The particle diameter and stability of nanobubbles were investigated by photon correlation spectroscopy. The biocompatibility of nanobubbles was preliminarily evaluated by cell proliferation and migration assay, hemolysis rate, and blood biochemistry analysis. A B-mode clinical ultrasound real-time imaging… More >

Samuel Helrich¹, Wesley Pate¹, Nishant Garg¹, Philip Barbosa², Shaun Wason¹

Canadian Journal of Urology, Vol.28, No.6, pp. 10889-10899, 2021

Abstract Introduction: To compare pelvic ultrasound (PUS) and transrectal ultrasound (TRUS) to magnetic resonance imaging (MRI) in the estimation of prostate size.
Materials and methods: After IRB approval, we performed a single-center, retrospective study of 91 patients who had prostate sizing between August 2013 and June 2017. Correlation, reliability, and agreement between PUS, TRUS, and MRI were calculated through the Pearson coefficient, intraclass correlation coefficient, and Bland-Altman analysis, respectively. Data was stratified by prostate size, body mass index, and time between imaging acquisition.
Results: A total of 91 patients underwent all three imaging methods. Median age was 64, median… More >

Jing Peng^1,2,4, Chaoyang Xia², Yuanwei Xu³, Xiaojie Li², Xi Wu², Xiao Han^1,4, Xinlai Chen⁵, Yucheng Chen³, Zhe Cui^1,4,*

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 259-272, 2021, DOI:10.32604/iasc.2021.016749 - 26 July 2021

Abstract Cardiomyopathy is a group of diseases that affect the heart and can cause serious health problems. Segmentation and classification are important for automating the clinical diagnosis and treatment planning for cardiomyopathy. However, this automation is difficult because of the poor quality of cardiac magnetic resonance (CMR) imaging data and varying dimensions caused by movement of the ventricle. To address these problems, a deep multi-task framework based on a convolutional neural network (CNN) is proposed to segment the left ventricle (LV) myocardium and classify cardiopathy simultaneously. The proposed model consists of a longitudinal encoder–decoder structure that… More >

Mohamed Abdel-Basset¹, Reda Mohamed¹, Mohamed Abouhawwash^2,3, Ripon K. Chakrabortty⁴, Michael J. Ryan⁴, Yunyoung Nam^5,*

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2961-2977, 2021, DOI:10.32604/cmc.2021.016956 - 06 May 2021

Abstract Image segmentation is vital when analyzing medical images, especially magnetic resonance (MR) images of the brain. Recently, several image segmentation techniques based on multilevel thresholding have been proposed for medical image segmentation; however, the algorithms become trapped in local minima and have low convergence speeds, particularly as the number of threshold levels increases. Consequently, in this paper, we develop a new multilevel thresholding image segmentation technique based on the jellyfish search algorithm (JSA) (an optimizer). We modify the JSA to prevent descents into local minima, and we accelerate convergence toward optimal solutions. The improvement is… More >

Tapan Kumar Das¹, Pradeep Kumar Roy², Mohy Uddin³, Kathiravan Srinivasan¹, Chuan-Yu Chang^4,*, Shabbir Syed-Abdul⁵

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2413-2429, 2021, DOI:10.32604/cmc.2021.016698 - 13 April 2021

Abstract Machine learning based image analysis for predicting and diagnosing certain diseases has been entirely trustworthy and even as efficient as a domain expert’s inspection. However, the style of non-transparency functioning by a trained machine learning system poses a more significant impediment for seamless knowledge trajectory, clinical mapping, and delusion tracing. In this proposed study, a deep learning based framework that employs deep convolution neural network (Deep-CNN), by utilizing both clinical presentations and conventional magnetic resonance imaging (MRI) investigations, for diagnosing tumors is explored. This research aims to develop a model that can be used for… More >

Gurparsad Singh Suri¹, Gurleen Kaur¹, Sara Moein^2,*

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 723-735, 2021, DOI:10.32604/iasc.2021.015049 - 01 March 2021

Abstract Schizophrenia (SZ) is a mental heterogeneous psychiatric disorder with unknown cause. Neuroscientists postulate that it is related to brain networks. Recently, scientists applied machine learning (ML) and artificial intelligence for the detection, monitoring, and prognosis of a range of diseases, including SZ, because these techniques show a high performance in discovering an association between disease symptoms and disease. Regions of the brain have significant connections to the symptoms of SZ. ML has the power to detect these associations. ML interests researchers because of its ability to reduce the number of input features when the data More >

Kathrine Rydén Suther^1,*, Charlotte de Lange^1,2, Henrik Brun³, Rolf Svendsmark¹, Bac Nguyen¹, Stig Larsen⁴, Bjarne Smevik¹, Arnt Eltvedt Fiane^5,6, Harald Lauritz Lindberg⁶, Einar Hopp¹

Congenital Heart Disease, Vol.16, No.2, pp. 107-121, 2021, DOI:10.32604/CHD.2021.014164 - 26 January 2021

Abstract Background: Patency of the coronary arteries is an issue after reports of sudden cardiac death in patients with transposition of the great arteries (TGA) operated with arterial switch (ASO). Recent studies give rise to concern regarding the use of ionising radiation in congenital heart disease, and assessment of the coronary arteries with coronary MR angiography (CMRA) might be an attractive non-invasive, non-ionising imaging alternative in these patients. Theoretically, the use of 3.0T CMRA should improve the visualisation of the coronary arteries. The objective of this study was to assess feasibility of 3.0T CMRA at the coronary… More >

Marrium Anam¹, Vasaki a/p Ponnusamy^2,*, Muzammil Hussain³, Muhammad Waqas Nadeem^2,4, Mazhar Javed³, Hock Guan Goh², Sadia Qadeer³

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 89-105, 2021, DOI:10.32604/cmc.2021.013159 - 12 January 2021

Abstract Trabecular bone holds the utmost importance due to its significance regarding early bone loss. Diseases like osteoporosis greatly affect the structure of the Trabecular bone which results in different outcomes like high risk of fracture. The objective of this paper is to inspect the characteristics of the Trabecular Bone by using the Magnetic Resonance Imaging (MRI) technique. These characteristics prove to be quite helpful in studying different studies related to Trabecular bone such as osteoporosis. The things that were considered before the selection of the articles for the systematic review were language, research field, and… More >

Nathan Perlis^1,2, Bishoy Lawendy¹, Jack Barkin^1,3

Canadian Journal of Urology, Vol.27, No.2, pp. 10185-10191, 2020

Abstract There is increasing evidence to support the use of multiparametric magnetic resonance imaging (MRI) in men at risk for clinically significant prostate cancer to help identify lesions and inform biopsy. Randomized, level 1 evidence demonstrates that men who are managed with MRI and MRI-ultrasound fusion targeted biopsy (MRF-TB) have more clinically significant prostate cancer and less clinically insignificant prostate cancer detected and avoid biopsy altogether more often than men who undergo systematic, whole-gland prostate biopsy (SPB). Furthermore, strategies that incorporate MRF-TB have lower rates of upgrading on radical prostatectomy compared to SPB. However, generalizing this… More >