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Brain MR image segmentation based on Gaussian filtering and improved FCM clustering algorithm

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Abstract:

Traditional fuzzy C-means (FCM) clustering method is not able to get the desired results of brain segmentation in the brain magnetic resonance (MR) image. In this paper, ...Show More

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Abstract:

Traditional fuzzy C-means (FCM) clustering method is not able to get the desired results of brain segmentation in the brain magnetic resonance (MR) image. In this paper, a new method of brain image segmentation based on Gaussian filtering and FCM clustering algorithm is proposed. The method adopted Gaussian filtering to remove noise and the initial cluster center was determined by the gray histograms to obtain. Using this method, 20 samples of brain MR images with 9% and 5% noise interference provided by Brain Web were segmented. The experimental results showed the proposed method was accuracy and efficiency.

Published in: 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

Date of Conference: 14-16 October 2017

Date Added to IEEE Xplore: 26 February 2018

ISBN Information:

DOI: 10.1109/CISP-BMEI.2017.8301978

Conference Location: Shanghai, China

References is not available for this document.

I. Introduction

Magnetic Resonance Imaging (MRI) is widely applied to diagnose a variety of diseases. MRI has several advantages which include the high contrast among different soft tissues and relatively high spatial resolution [1]. The brain MR image segmentation is a precondition for analyzing related brain diseases. As the manual segmentation is easily influenced by the subjective factors, the automatic segmentation is a key problem in the field of biomedical image processing research.

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