聚类分析
计算机科学
人工智能
初始化
模糊聚类
模糊逻辑
模式识别(心理学)
数据挖掘
分割
程序设计语言
作者
Arunita Das,Amrita Namtirtha,Animesh Dutta
标识
DOI:10.1016/j.knosys.2021.108008
摘要
Patients with Acute Lymphoblastic Leukemia (ALL) require prompt diagnosis since it has the chance to become fatal if neglected for a few weeks. The microscopic image of lymphocyte cells creates doubts even for expert pathologists because normal lymphocytes cells and ALL blast cells are both very smooth. Therefore, proper segmentation of White Blood Cells (WBC) is a very crucial aspect of the task. Consequently, the focus of the study is on segmenting the WBCs in Acute Lymphoblastic Leukemia (ALL) images utilizing classical crisp and fuzzy clustering approaches like K-means (KM) and Fuzzy C-means (FCM). But these classical clustering approaches are very sensitive to noise and initial cluster center initialization and hence trapped in local optima. As a result, these techniques may produce incorrect cluster centers. Firstly, researchers are employing Nature-Inspired Optimization Algorithms (NIOAs) as an alternate methodology for both crisp and fuzzy clustering problems to solve the initial cluster center initialization issue. Therefore, using a two-stage Eagle Strategy based on Stochastic Fractal Search (SFS) method, this research proposes a fuzzy clustering methodology. Secondly, morphological reconstruction has been employed for filtering the membership matrix to guarantee noise-immunity. A scrupulous parallel study is performed among the proposed eagle strategy based fuzzy clustering depending on morphological reconstruction with some well-known NIOA based fuzzy clustering and crisp clustering approaches, and classical clustering methodologies like KM and FCM in view of a collection of color ALL images and regular performance metrics. Experimental results demonstrate that recommended ES-SFS based fuzzy clustering technique with morphological reconstruction surpasses the most of utilized approaches in words of computation effort, quality metrics, and robustness. Additionally, to get rid of the random effect in the achieved numerical results, a non-parametric strategy is used for statistical validation.
科研通智能强力驱动
Strongly Powered by AbleSci AI