Advanced algorithmic approaches to medical image segmentation
Approaches for automated detection and classification of masses in mammograms
Pattern Recognition
Filtering noise on mammographic phantom images using local contrast modification functions
Image and Vision Computing
Computers in Biology and Medicine
Detection of clustered microcalcifications in small field digital mammography
Computer Methods and Programs in Biomedicine
Computer-aided detection and diagnosis of breast cancer with mammography: recent advances
IEEE Transactions on Information Technology in Biomedicine
A proposed system for edge mammogram image
AEE'10 Proceedings of the 9th WSEAS international conference on Applications of electrical engineering
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
Short Communication: Histogram Modified Local Contrast Enhancement for mammogram images
Applied Soft Computing
Image enhancement optimization for hand-luggage screening at airports
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Saliency based mass detection from screening mammograms
Signal Processing
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Mammograms are difficult to interpret, especially of cancers at their early stages. We analyze the effectiveness of our adaptive neighborhood contrast enhancement (ANCE) technique in increasing the sensitivity of breast cancer diagnosis. Seventy-eight screen-film mammograms of 21 difficult cases (14 benign and seven malignant), 222 screen-film mammograms of 28 interval cancer patients and six benign control cases were digitized with a high-resolution of about 4096\×2048\×10-bit pixels and then processed with the ANCE method. Unprocessed and processed digitized mammograms as well as the original films were presented to six experienced radiologists for a receiver operating characteristic (ROC) evaluation for the difficult case set and to three reference radiologists for the interval cancer set. The results show that the radiologists' performance with the ANCE-processed images is the best among the three sets of images (original, digitized, and enhanced) in terms of area under the ROC curve and that diagnostic sensitivity is improved by the ANCE algorithm. All of the 19 interval cancer cases not detected with the original films of earlier mammographic examinations were diagnosed as malignant with the corresponding ANCE-processed versions, while only one of the six benign cases initially labeled correctly with the original mammograms was interpreted as malignant after enhancement. This study demonstrates the potential for improvement of diagnostic performance in early detection of breast cancer with digital image enhancement.