Fuzzy clustering‐based image segmentation techniques used to segment magnetic resonance imaging/computed tomography scan brain tissues: Comparative analysis

Abstract

Medical images are obtained with computer‐aided diagnosis using electronic devices such as CT scanners and MRI machines. The captured computed tomography (CT)/magnetic resonance imaging (MRI) images typically have limited spatial resolution, low contrast, noise and nonuniform variability in intensity due to environmental effects. Therefore, the distinctions of the objects are blurred, distorted and the meanings of the objects are not quite precise. Fuzzy sets and fuzzy logic are best suited for addressing vagueness and ambiguity. Fuzzy clustering technique has been commonly used for segmentation of images throughout the last decade. This study presents a comparative study of 14 fuzzy‐clustered image segmentation algorithms used in the CT scan and MRI brain image segments. This study used 17 data sets including 4 synthetic data sets, namely, Bensaid, Diamond, Square, and its noisy version, 5 real‐world digital images, and 8 CT scan/MRI brain images to analyze the algorithms. Ground truth images are used for qualitative analysis. Apart from the qualitative analysis, the study also quantitatively evaluated the methods using three validity metrics, namely, partition coefficient, partition entropy, and Fukuyama‐Sugeno. After a thorough and careful review of the results, it is observed that extension of the fuzzy C‐means (EFCM) outperformed every other image segmentation algorithm, even in a noisy environment, followed by kernel‐based FCM σ, the output of which is also very good after EFCM.

Brain stroke computed tomography images analysis using image processing: A Review

“…The proposed technique has higher accuracy as compared to FCM clustering and manual fuzzy based active contour. Kaur et al [12] estimated that FCM could cater vague and ambiguous CT images. In [12] concluded that the extended FCM (EFCM) has the highest efficiency compared to other image segmentation technique.…”

“…Kaur et al [12] estimated that FCM could cater vague and ambiguous CT images. In [12] concluded that the extended FCM (EFCM) has the highest efficiency compared to other image segmentation technique.…”

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Deep Learning based Approach for Bone Diagnosis Classification in Ultrasonic Computed Tomographic Images

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Efficient DWT based Fusion Algorithm for Improving Contrast and Edge Preservation

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