Towards Better Segmentation of Abnormal Part in Multimodal Images Using Kernel Possibilistic C Means Particle Swarm Optimization With Morphological Reconstruction Filters
نویسندگان
چکیده
The authors designed an automated framework to segment tumors with various image sequences like T1, T2, and post-processed MRI multimodal images. Contrast-limited adaptive histogram equalization method is used for preprocessing images enhance the intensity level view tumor part clearly. With combination of kernel possibilistic c means clustering particle swarm optimization technique, a segmented, morphological filters are applied remove unrelated outlier pixels in segmented detect accurate part. collected from online resources Harvard brain dataset, BRATS, RIDER, few clinical datasets. Efficiency ensured by computing performance metrics Jaccard Index MSE, PSNR, sensitivity, specificity, accuracy, computational time. proposed approach yields 97.06% segmentation accuracy 98.08% classification average 5s all
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ژورنال
عنوان ژورنال: International Journal of E-health and Medical Communications
سال: 2021
ISSN: ['1947-3168', '1947-315X']
DOI: https://doi.org/10.4018/ijehmc.20210501.oa4