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Fuzzy clustering using gravitational search algorithm for brain image segmentation
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AI
DOI：10.1007/s11042-022-12336-x.png)
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Abstract
En 中文
Clustering is a key activity in numerous data mining applications such as information retrieval, text mining, image segmentation. Clustering also plays a major role in medical image processing. Manual image segmentation is very tedious and time consuming task and the results of manual segmentation are subjected to errors due to huge and varying data. Therefore, automated segmentation systems are gaining enormous importance nowadays. This paper presents an automated system for segmentation of brain tissues namely white matter, gray matter and cerebrospinal fluid from brain MRI images. In this work, we propose a novel clustering approach, Fuzzy-Gravitational Search Algorithm(GSA) for MRI brain image segmentation. The proposed approach is based on GSA, and uses fuzzy inference rules for controlling the parameter alpha as search progresses. The results of the system are compared with GSA and recent work on brain image segmentation algorithms for both real and simulated database on the basis of Dice Coefficient values. The performance of the Fuzzy-GSA algorithm is also evaluated against four benchmark datasets from the UC Irvine repository. The results illustrate that the Fuzzy-GSA approach attains the highest quality clustering over the selected datasets when compared with several other clustering algorithms.
Keywords:
Magnetic resonance imaging
Brain image segmentation
Gravitational search algorithm
Fuzzy clustering
Fuzzy inference rules
Journal
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3
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1.9W
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