Mammography Images Segmentation Based on Fuzzy Set and Thresholding

Salih, Ali Mohammed; Kamil, Mohammed Y.

doi:10.23851/mjs.v29i3.644

Cited by 2 publications

(1 citation statement)

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“…Lin et al [26] proposed a fuzzy-based novel quality control algorithm for the segmentation of medical images. Salih et al [27] presented an algorithm based on fuzzy set methods for segmenting the boundaries of breast masses. Chaira T. [28] refers to thresholding techniques in general and proposes a method where intuitionistic fuzzy sets can be used as a segmentation method for images that contain blood vessels and blood cells.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Intelligent Pattern Recognition Systems for Mass Segmentation and Classification: A Pilot Study on Full-Field Digital Mammograms

Dounis,

Avramopoulos,

Kallergi

2023

Applied Sciences

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Governments and health authorities emphasize the importance of early detection of breast cancer, usually through mammography, to improve prognosis, increase therapeutic options and achieve optimum outcomes. Despite technological advances and the advent of full-field digital mammography (FFDM), diagnosis of breast abnormalities on mammographic images remains a challenge due to qualitative variations in different tissue types and densities. Highly accurate computer-aided diagnosis (CADx) systems could assist in the differentiation between normal and abnormal tissue and the classification of abnormal tissue as benign or malignant. In this paper, classical, advanced fuzzy sets and fusion techniques for image enhancement were combined with three different thresholding methods (Global, Otsu and type-2 fuzzy sets threshold) and three different classifying techniques (K-means, FCM and ANFIS) for the classification of breast masses on FFDM. The aim of this paper is to identify the performance of the advanced fuzzy sets, fuzzy sets type-2 segmentation, decisions based on K-means and FCM, and the ANFIS classifier. Sixty-three combinations were evaluated on ninety-seven digital mammographic masses (sixty-five benign and thirty-two malignant). The performance of the sixty-three combinations was evaluated by estimating the accuracy, the F1 score, and the area under the curve (AUC). LH-XWW enhancement method with Otsu thresholding and FCM classifier outperformed all other combinations with an accuracy of 95.17%, F1 score of 89.42% and AUC of 0.91. This algorithm seems to offer a promising CADx system for breast cancer diagnosis on FFDM.

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Section: Related Workmentioning

confidence: 99%