Fuzzy entropy based on differential evolution for breast gland segmentation
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For the diagnosis and treatment of breast tumors, the automatic detection of glands is a crucial step. The true segmentation of the gland is directly related to effective treatment effect of the patient. Therefore, it is necessary to propose an automatic segmentation algorithm based on mammary gland features. A segmentation method of differential evolution (DE) fuzzy entropy based on mammary gland is proposed in the paper. According to the image fuzzy entropy, the evaluation function of image segmentation is constructed in the first step. Then, the method adopts DE, the image fuzzy entropy parameter is regard as the initial population of individual. After the mutation, crossover and selection of three evolutionary processes to search for the maximum fuzzy entropy of parameters, the optimal threshold of the segmented gland is achieved. Finally, the mammary gland is segmented by the threshold method of maximum fuzzy entropy. Eight breast images with four tissue types are tested 100 times, with accuracy (Acc), sensitivity (Sen), specificity (Spe), positive predictive value (PPV), negative predicted value (NPV), and average structural similarity (Mssim) to measure the segmentation result. The Acc of the proposed algorithm is 98.46 ± 8.02E−03%, 95.93 ± 2.38E−02%, 93.88 ± 6.59E−02%, 94.73 ± 1.82E−01%, 96.19 ± 1.15E−02%, and 97.51 ± 1.36E−02%, 96.64 ± 6.35E−02%, and 94.76 ± 6.21E−02%, respectively. The mean Mssim values of the 100 tests were 0.985, 0.933, 0.924, 0.907, 0.984, 0.928, 0.938, and 0.941, respectively. Our proposed algorithm is more effective and robust in comparison to the other fuzzy entropy based on swarm intelligent optimization algorithms. The experimental results show that the proposed algorithm has higher accuracy in the segmentation of mammary glands, and may serve as a gold standard in the analysis of treatment of breast tumors.
KeywordsImage segmentation Differential evolution Maximum fuzzy entropy Threshold method
Computer aided diagnosis
Fuzzy entropy segmentation based on particle swarm algorithm
Fuzzy entropy segmentation based on bacterial foraging optimization algorithm
Fuzzy entropy segmentation based on artificial bee colony algorithm
Fuzzy entropy segmentation based on bat algorithm
Fuzzy entropy segmentation based on firefly algorithm
This work is supported by National Natural Science Foundation of China Nos. 61231002 and 51075068, by the Foundation of the Fujian Education Department under Grant No. JA15035, by the Foundation of Quanzhou under Grant Nos. 2014Z103 and 2015Z114, by the 2016 Postgraduate Innovation Ability Cultivating Projects No. 1611422002.
Compliance with ethical standards
Conflict of interest
The author declares that there is no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
Research involving human and animal participants
This article does not contain any studies with animals performed by any of the authors.
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