A Brief Review on Clustering Based Medical Image Segmentation Algorithms with Issues and Challenges
Chetna Kaushal, Anshu Singla, Poonam Panwar · 2021
In these days, research on the medical healthcare system is an emerging area and mainly focused on the designing of an efficient segmentation approach with the concept of Artificial Intelligence (AI) techniques for the appropriate and accurate region detection. A lot of clustering, as well as traditional segmentation approaches, are available for medical images, but most of them are depended on the data types. In this survey, various types of medical diagnosis systems are analyzed for segmentation such as segmentation of brain tumor, skin lesion, leukemia blood cell, and lungs nodules segmentation. In this paper, we presented a brief review on clustering-based medical image segmentation with their challenging factors faced by researchers. Owing to the great success rate of AI, Deep Learning (DL or), Machine Learning (ML) algorithms, there are many segmentation techniques aimed by researchers for medical image segmentation to overcome the existing challenges. Here, a survey of existing clustering-based segmentation and their hybridization with swarm-based optimization techniques are also discussed with a tabular representation. At the last, we compare the existing work based on the quantitative performance analyses in terms of Accuracy, Dice Coefficient, Jaccard Index, Sensitivity, and Specificity, etc. on some popular benchmark datasets.