Image enhancement and thresholding by optimization of fuzzy compactness
Pattern Recognition Letters
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Segmentation using fuzzy divergence
Pattern Recognition Letters
The fuzzy geometry of image subsets
Pattern Recognition Letters
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Segmenting suspicious regions in mammographic images that may contain tumours from the background parenchyma of the breast is a highly challenging task. This is made difficult by factors including the complicated structure of breast tissues, unclear boundaries between normal tissues and tumours, and the low contrast between masses and surrounding regions in the images. In recent years, many researchers have discovered that fuzzy-logic based techniques have a number of advantages over conventional crisp approaches in segmenting masses in mammographic images. To this end, we compare five representative fuzzy thresholding techniques for this task in this paper using the recall and precision metrics. Experimental results revealed that fuzzy similarity thresholding achieves higher segmentation accuracy over a test set of 54 mammographic images selected from the mini-MIAS database.