Contrast enhancement technique based on local detection of edges
Computer Vision, Graphics, and Image Processing
Adaptive contrast enhancement and de-enhancement
Pattern Recognition
Thresholding using two-dimensional histogram and fuzzy entropy principle
IEEE Transactions on Image Processing
A new fuzzy relaxation algorithm for image enhancement
International Journal of Knowledge-based and Intelligent Engineering Systems
On information energy of the image fuzzy sets
Information Sciences: an International Journal
Information Sciences: an International Journal
A high performance edge detector based on fuzzy inference rules
Information Sciences: an International Journal
Identification of different stages of diabetic retinopathy using retinal optical images
Information Sciences: an International Journal
Filtering noise on mammographic phantom images using local contrast modification functions
Image and Vision Computing
Mammographic Image Contrast Enhancement through the Use of Moving Contrast Sweep
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
Computers in Biology and Medicine
Contrast enhancement for image by WNN and GA combining PSNR with information entropy
Fuzzy Optimization and Decision Making
Computer-aided detection and diagnosis of breast cancer with mammography: recent advances
IEEE Transactions on Information Technology in Biomedicine
Information Sciences: an International Journal
Fuzzy clustering algorithms for unsupervised change detection in remote sensing images
Information Sciences: an International Journal
Contrast enhancement for image with simulated annealing algorithm and wavelet neural network
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
A fuzzy rule-based approach for characterization of mammogram masses into BI-RADS shape categories
Computers in Biology and Medicine
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Breast cancer continues to be a significant public health problem in the United States. Primary prevention seems impossible since the causes of this disease still remain unknown. Early detection is the key to improving breast cancer prognosis. Mammography has been one of the most reliable methods for early detection of breast carcinomas. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous number of mammograms generated in widespread screening. Automated breast cancer diagnosis attracts much attention of the researchers. However, the fuzzy nature of the mammograms and the low contrast between the breast cancer and its surroundings make the automated breast cancer detection very difficult. Mammogram contrast enhancement is critical and essential to breast cancer diagnosis.This paper uses an adaptive fuzzy logic contrast enhancement method to enhance mammographic features. We first normalize the mammograms to reduce the effects of different illuminations. Then, we fuzzify the normalized images based on the maximum fuzzy entropy principle. The local contrast is measured and enhanced by utilizing both the global and local information so that the fine details of mammograms can be enhanced and the noise can be suppressed. The histogram of the mammogram provides the global information and the fuzzy entropy of local window is computed to analyze the local information. Then, we use both the global and local information to define and enhance the contrast. Finally, the defuzzification is performed to transform the enhanced mammogram back to the spatial domain. The experiments demonstrate that the proposed method can effectively enhance the contours and fine details of the mammographic features which will be useful for breast cancer diagnosis.