Modified Bias Field Fuzzy C-Means for Effective Segmentation of Brain MRI

Image segmentation still remains an important task in image processing and analysis. Sequel to any segmentation process, preprocessing activities carried out on the images have a great effect on the accuracy of the segmentation task. This paper therefore laid emphasis on the preprocessing stage of brain Magnetic Resonance Imaging (MRI) images Smallest Univalue Segment Assimilating Nucleus (SUSAN) and bias field correction algorithms. Subsequently, brain tissue extraction tool was employed in extracting non-brain tissues from the brain image. Afterwards, Fuzzy K-Means (FKM) and Fuzzy C-Means (FCM) segmentation algorithms were employed for segmenting brain MRI images acquired from four different MRI databases into their White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF) constituents. Evaluation metrics such as cluster validity functions using partition coefficients and partition entropy; area error metrics such as false positive, true positive, true negative and false negative (FN); similarity index, sensitivity and specificity were used to evaluate the performance of both techniques. A comparative analysis of the experimental results revealed that in most instances, FKM segmentation technique is preferable to FCM segmentation technique for brain MRI segmentation task.

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Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405616666210104111218 ◽

2021 ◽

Vol 16 ◽

Author(s):

Ghazanfar Latif ◽

Jaafar Alghazo ◽

Fadi N. Sibai ◽

D.N.F. Awang Iskandar ◽

Adil H. Khan

Keyword(s):

Brain Tumor ◽

Brain Tumors ◽

Brain Mri ◽

Tumor Segmentation ◽

Brain Anatomy ◽

Mri Segmentation ◽

Brain Tumor Segmentation ◽

Brain Images ◽

Low Contrast ◽

Fuzzy C Means

Background: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. Objective: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. Results: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

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