A. Renugambal^{1, *}, K. Selva Bhuvaneswari²

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 681-700, 2020, DOI:10.32604/cmc.2020.09519

Abstract In this study, a novel hybrid Water Cycle Moth-Flame Optimization (WCMFO) algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance (MR) image slices. WCMFO constitutes a hybrid between the two techniques, comprising the water cycle and moth-flame optimization algorithms. The optimal thresholds are obtained by maximizing the between class variance (Otsu’s function) of the image. To test the performance of threshold searching process, the proposed algorithm has been evaluated on standard benchmark of ten axial T₂-weighted brain MR images for image segmentation. The experimental outcomes infer that it produces better optimal threshold values at a greater and… More >

Caining Zhang¹, Xiaopeng Guo², Xiaoya Guo³, David Molony⁴, Huaguang Li², Habib Samady⁴, Don P. Giddens^4,5, Lambros Athanasiou⁶, Dalin Tang^1*,7, Rencan Nie^2,*, Jinde Cao⁸

CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.2, pp. 631-646, 2020, DOI:10.32604/cmes.2020.09718

Abstract Optical coherence tomography (OCT) is a new intravascular imaging technique with high resolution and could provide accurate morphological infor￾mation for plaques in coronary arteries. However, its segmentation is still com￾monly performed manually by experts which is time-consuming. The aim of this study was to develop automatic techniques to characterize plaque components and quantify plaque cap thickness using 3 machine learning methods including convolutional neural network (CNN) with U-Net architecture, CNN with Fully convolutional DenseNet (FC-DenseNet) architecture and support vector machine (SVM). In vivo OCT and intravascular ultrasound (IVUS) images were acquired from two patients at Emory University with informed consent… More >

Fangming Bi^{1, 2}, Xuanyi Fu^{1, 2}, Wei Chen^{1, 2, 3, *}, Weidong Fang⁴, Xuzhi Miao^{1, 2}, Biruk Assefa^{1, 5}

CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 199-216, 2020, DOI:10.32604/cmc.2020.06258

Abstract Aiming at the defects of the traditional fire detection methods, which are caused by false positives and false negatives in large space buildings, a fire identification detection method based on video images is proposed. The algorithm first uses the hybrid Gaussian background modeling method and the RGB color model to perform fire prejudgment on the video image, which can eliminate most non-fire interferences. Secondly, the traditional regional growth algorithm is improved and the fire image segmentation effect is effectively improved. Then, based on the segmented image, the dynamic and static features of the fire flame are further analyzed and extracted… More >

Caining Zhang¹, Xiaoya Guo², Dalin Tang^1,3,*, David Molony⁴, Chun Yang³, Habib Samady⁴, Jie Zheng⁵, Gary S. Mintz⁶, Akiko Maehara⁶, Mitsuaki Matsumura⁶, Don P. Giddens^4,7

Molecular & Cellular Biomechanics, Vol.16, Suppl.1, pp. 79-80, 2019, DOI:10.32604/mcb.2019.05747

Abstract Cardiovascular diseases are closely associated with sudden rupture of atherosclerotic plaques. Previous image modalities such as magnetic resonance imaging (MRI) and intravascular ultrasound (IVUS) were unable to identify vulnerable plaques due to their limited resolution. Optical coherence tomography (OCT) is an advanced intravascular imaging technique developed in recent years which has high resolution approximately 10 microns and could provide more accurate morphology of coronary plaque. In particular, it is now possible to identify plaques with fibrous cap thickness <65 μm, an accepted threshold value for vulnerable plaques. However, the current segmentation of OCT images are still performed manually by physicians… More >

Yuxiang Huang¹, Chuliu He¹, Jiaqiu Wang², Yuehong Miao¹, Tongjin Zhu¹, Ping Zhou¹, Zhiyong Li^1,2,*

Molecular & Cellular Biomechanics, Vol.15, No.2, pp. 117-125, 2018, DOI: 10.3970/mcb.2018.02478

Abstract Intravascular optical coherence tomography (IVOCT) is becoming more and more popular in clinical diagnosis of coronary atherosclerotic. However, reading IVOCT images is of large amount of work. This article describes a method based on image feature extraction and support vector machine (SVM) to achieve semi-automatic segmentation of IVOCT images. The image features utilized in this work including light attenuation coefficients and image textures based on gray level co-occurrence matrix. Different sets of hyper-parameters and image features were tested. This method achieved an accuracy of 83% on the test images. Single class accuracy of 89% for fibrous, 79.3% for calcification and… More >

Jia Dongyao^1,2, Zhu Huaihua¹, Ai Yanke¹, Zou Shengxiong¹

CMES-Computer Modeling in Engineering & Sciences, Vol.103, No.6, pp. 411-427, 2014, DOI:10.3970/cmes.2014.103.411

Abstract The existing study on the image segmentation methods based on the image of vehicle lights is insufficient both at home and abroad, and its segmentation efficiency and accuracy is low as well. On the basis of the analysis of the regional characteristics of vehicle lights and a level set model, an image segmentation method for complex vehicle lights based on adaptive significance level set contour model is proposed in this paper. Adaptive positioning algorithm of the significant initial contour curve based on two-dimensional convex hull is designed to obtain the initial position of evolution curve, thus the adaptive ability of… More >

Yanpeng Wu¹, Xiaoqi Peng^1,*, Mohammad Nur², Hengfu Yang¹

CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1155-1170, 2019, DOI:10.32604/cmc.2019.06123

Abstract Foam image segmentation, represented by watershed algorithm, is wildly used in the extraction of bubble morphology features. H-minima transformation was proved to be effective in locating the catchment basins in the traditional watershed segmentation method. To further improve the accuracy of watershed segmentation, method of top-bottom-cap filters and method of morphological reconstruction were implied to marking the catchment basins. In this paper, instead of H-minima transformation, a method of contour lines is specially proposed to obtain the catchment basins for foam image segmentation by using top-bottom-cap filters and less morphological reconstruction. Experimental results in foam segmentation show that the proposed… More >

Zhe Liu^1,2,*, Bao Xiang^1,3, Yuqing Song¹, Hu Lu¹, Qingfeng Liu¹

CMC-Computers, Materials & Continua, Vol.58, No.2, pp. 451-461, 2019, DOI:10.32604/cmc.2019.04069

Abstract Most image segmentation methods based on clustering algorithms use single-objective function to implement image segmentation. To avoid the defect, this paper proposes a new image segmentation method based on a multi-objective particle swarm optimization (PSO) clustering algorithm. This unsupervised algorithm not only offers a new similarity computing approach based on electromagnetic forces, but also obtains the proper number of clusters which is determined by scale-space theory. It is experimentally demonstrated that the applicability and effectiveness of the proposed multi-objective PSO clustering algorithm. More >