期刊名称：Journal of King Saud University @?C Computer and Information Sciences
印刷版ISSN：1319-1578
出版年度：2022
卷号：34
期号：6
页码：2589-2600
DOI：10.1016/j.jksuci.2020.01.003
语种：English
出版社：Elsevier
摘要：The present work proposes entropy based automatic unsupervised brain intracranial hemorrhage segmentation using CT images. The proposed work is consisting of fuzzy c-mean (FCM), automatic selection of cluster, skull removal, thresholding and edge-based active contour methods. The FCM is used to divide the image into different cluster and among these clusters one is automatically selected for the segmentation process which have skull and hemorrhage region. Due to major benefits of level set method i.e. use large time step which reduce the number of iterations and does not need reinitialization process thus it has fast convergence speed is used for smoothness of hemorrhage region. The seed point for level set method is initialized by entropy based thresholding techniques.The exhaustive experimentations have been carried out on 35 different patients CT images collected from SGRR Institute of Medical & Health Sciences and SMI Hospital, Dehradun, Uttarakhand, India. The performance of developed automatic segmentation techniques for hemorrhage detection is analyzed by using sensitivity, specificity, Dice coefficient, Jaccard index, Precision and Accuracy. After the evaluation of proposed method it can be observed that hemorrhagic regions segmented by the proposed method, has high accuracy comparison to FCM method and manual fuzzy based active contour method.
关键词：Fuzzy c-mean;Distance regularized level set evolution;Computed tomography imaging;Intracranial hemorrhage
