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  • Open Access


    An Improved Soft Subspace Clustering Algorithm for Brain MR Image Segmentation

    Lei Ling1, Lijun Huang2, Jie Wang2, Li Zhang2, Yue Wu2, Yizhang Jiang1, Kaijian Xia2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2353-2379, 2023, DOI:10.32604/cmes.2023.028828

    Abstract In recent years, the soft subspace clustering algorithm has shown good results for high-dimensional data, which can assign different weights to each cluster class and use weights to measure the contribution of each dimension in various features. The enhanced soft subspace clustering algorithm combines interclass separation and intraclass tightness information, which has strong results for image segmentation, but the clustering algorithm is vulnerable to noisy data and dependence on the initialized clustering center. However, the clustering algorithm is susceptible to the influence of noisy data and reliance on initialized clustering centers and falls into a local optimum; the clustering effect… More >

  • Open Access


    Early Tumor Diagnosis in Brain MR Images via Deep Convolutional Neural Network Model

    Tapan Kumar Das1, Pradeep Kumar Roy2, Mohy Uddin3, Kathiravan Srinivasan1, Chuan-Yu Chang4,*, Shabbir Syed-Abdul5

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

    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 abnormality detection over MRI data… More >

  • Open Access


    Image Segmentation of Brain MR Images Using Otsu’s Based Hybrid WCMFO Algorithm

    A. Renugambal1, *, 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 >

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