Venkatesan Rajinikanth¹, Shabnam Mohamed Aslam², Seifedine Kadry³, Orawit Thinnukool^4,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 4087-4105, 2022, DOI:10.32604/cmc.2022.019786

Abstract The incident rate of the Gastrointestinal-Disease (GD) in humans is gradually rising due to a variety of reasons and the Endoscopic/Colonoscopic-Image (EI/CI) supported evaluation of the GD is an approved practice. Extraction and evaluation of the suspicious section of the EI/CI is essential to diagnose the disease and its severity. The proposed research aims to implement a joint thresholding and segmentation framework to extract the Gastric-Polyp (GP) with better accuracy. The proposed GP detection system consist; (i) Enhancement of GP region using Aquila-Optimization-Algorithm supported tri-level thresholding with entropy (Fuzzy/Shannon/Kapur) and between-class-variance (Otsu) technique, (ii) Automated (Watershed/Markov-Random-Field) and semi-automated (Chan-Vese/Level-Set/Active-Contour) segmentation… More >

P. Naga Srinivasu¹, Shakeel Ahmed^2,*, Abdulaziz Alhumam², Akash Bhoi Kumar³, Muhammad Fazal Ijaz⁴

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3303-3319, 2021, DOI:10.32604/cmc.2021.018472

Abstract In the digestion of amino acids, carbohydrates, and lipids, as well as protein synthesis from the consumed food, the liver has many diverse responsibilities and functions that are to be performed. Liver disease may impact the hormonal and nutritional balance in the human body. The earlier diagnosis of such critical conditions may help to treat the patient effectively. A computationally efficient AW-HARIS algorithm is used in this paper to perform automated segmentation of CT scan images to identify abnormalities in the human liver. The proposed approach can recognize the abnormalities with better accuracy without training, unlike in supervisory procedures requiring… More >

N. Shobha Rani^{1, *}, M. Chandrajith², B. R. Pushpa¹, B. J. Bipin Nair¹

CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 679-694, 2020, DOI:10.32604/cmc.2020.08552

Abstract Assessing the age of an individual via bones serves as a fool proof method in true determination of individual skills. Several attempts are reported in the past for assessment of chronological age of an individual based on variety of discriminative features found in wrist radiograph images. The permutation and combination of these features realized satisfactory accuracies for a set of limited groups. In this paper, assessment of gender for individuals of chronological age between 1-17 years is performed using left hand wrist radiograph images. A fully automated approach is proposed for removal of noise persisted due to non-uniform illumination during… More >

Xueying Huang¹, Chun Yang², Jie Zheng³, Pamela K. Woodard³, Dalin Tang¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.1, No.1, pp. 29-34, 2007, DOI:10.3970/icces.2007.001.029

Abstract Atherosclerotic plaques may rupture without warning and cause acute cardiovascular syndromes such as heart attack and stroke. Accurate identification of plaque components will improve the accuracy and reliability of computational models. In this article, we present a segmentation method using a cluster analysis technique to quantify and classify plaque components from magnetic resonance images (MRI). 3D in vivo and ex vivo multi-contrast (T1-, proton density-, and T2-weighted) MR Images were acquired from a patient of cardiovascular disease. Normal distribution Bayes classifier was performed on ex vivo and in vivo MR Images respectively. The resulting segmentation obtained from cluster analysis showed… More >