IEEE transactions on bio-medical engineering · 2016
Mammogram Enhancement Using Intuitionistic Fuzzy Sets.
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Deng He, Deng Wankai, Sun Xianping, Liu Maili, Ye Chaohui, Zhou Xin
Аннотация
OBJECTIVE: Conventional mammogram enhancement methods use transform-domain filtering, which possibly produce some artifacts or not well highlight all local details in images. This paper presents a new enhancement method based on intuitionistic fuzzy sets.
METHODS: The presented algorithm initially separates a mammogram via a global threshold and then fuzzifies the image utilizing the intuitionistic fuzzy membership function that adopts restricted equivalence functions. After that, the presented scheme hyperbolizes membership degrees of foreground and background areas, defuzzifies the fuzzy plane, and achieves a filtered image via normalization. Finally, an enhanced mammogram is obtained by fusing the original image with filtered one. These implementations can be processed in parallel.
RESULTS: This algorithm can improve the contrast and visual quality of regions of interest.
CONCLUSION: Real data experiments demonstrate that our method has better performance regarding the improvement of contrast and visual quality of abnormalities in mammograms (such as masses and/or microcalcifications), compared with classical baseline methods.
SIGNIFICANCE: This algorithm has potential for understanding and determining abnormalities.
METHODS: The presented algorithm initially separates a mammogram via a global threshold and then fuzzifies the image utilizing the intuitionistic fuzzy membership function that adopts restricted equivalence functions. After that, the presented scheme hyperbolizes membership degrees of foreground and background areas, defuzzifies the fuzzy plane, and achieves a filtered image via normalization. Finally, an enhanced mammogram is obtained by fusing the original image with filtered one. These implementations can be processed in parallel.
RESULTS: This algorithm can improve the contrast and visual quality of regions of interest.
CONCLUSION: Real data experiments demonstrate that our method has better performance regarding the improvement of contrast and visual quality of abnormalities in mammograms (such as masses and/or microcalcifications), compared with classical baseline methods.
SIGNIFICANCE: This algorithm has potential for understanding and determining abnormalities.