Improvement of MR brain images segmentation based on interval type-2 fuzzy C-Means

Ouarda, Assas; Benmedour, Fadila

doi:10.1109/icocs.2015.7483275

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…Ouarda and Fadila used type‐2 Fuzzy C‐Means (FCM) to delineate the different tissues present in MR brain images. FCM algorithm allows partial membership assignment in different tissue classes.…”

Section: Related Workmentioning

confidence: 99%

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A fully automated hybrid methodology using Cuckoo‐based fuzzy clustering technique for magnetic resonance brain image segmentation

Alagarsamy

Kamatchi

Govindaraj

et al. 2017

Int J Imaging Syst Tech

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This article aims at developing an automated hybrid algorithm using Cuckoo Based Search (CBS) and interval type‐2 fuzzy based clustering, so as to exhibit efficient magnetic resonance (MR) brain image segmentation. An automatic MR brain image segmentation facilitates and enables a radiologist to have a brief review and easy analysis of complicated tumor regions of imprecise gray level regions with minimal user interface. The tumor region having severe intensity variations and suffering from poor boundaries are to be detected by the proposed hybrid technique that could ease the process of clinical diagnosis and this tends to be the core subject of this article. The ability of the proposed technique is compared using standard comparison parameters such as mean squared error, peak signal to noise ratio, computational time, Dice Overlap Index, and Jaccard Tanimoto Coefficient Index. The proposed CBS combined with interval type‐2 fuzzy based clustering produces a sensitivity of 0.7143 and specificity of 0.9375, which are far better than the conventional techniques such as kernel based, entropy based, graph‐cut based, and self‐organizing maps based clustering. Appreciable segmentation results of tumor region that enhances clinical diagnosis is made available through this article and two of the radiologists who have hands on experience in the field of radiology have extended their support in validating the efficiency of the proposed methodology and have given their consent in utilizing the proposed methodology in the processes of clinical oncology.

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Section: Related Workmentioning

confidence: 99%

“…The classes are obtained by comparing the segmented results with the gold standard images or Ground Truth (GT) images. [36][37][38] S:I:5 23TP 23TP1FP1FN (13)…”

Section: Similarity Indexmentioning

confidence: 99%