Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated detection of masses in mammograms by local adaptive thresholding
Computers in Biology and Medicine
Approaches for automated detection and classification of masses in mammograms
Pattern Recognition
Computerized detection of breast masses in digitized mammograms
Computers in Biology and Medicine
Object- and spatial-level quantitative analysis of multispectral histopathology images for detection and characterization of cancer
Computer-aided detection and diagnosis of breast cancer with mammography: recent advances
IEEE Transactions on Information Technology in Biomedicine
Esaliency (Extended Saliency): Meaningful Attention Using Stochastic Image Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of regions of interest in mammograms in a topographic approach
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
Learning to Detect a Salient Object
IEEE Transactions on Pattern Analysis and Machine Intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Detection of masses in mammogram images using CNN, geostatistic functions and SVM
Computers in Biology and Medicine
Fully automated gradient based breast boundary detection for digitized X-ray mammograms
Computers in Biology and Medicine
IEEE Transactions on Information Technology in Biomedicine
An evaluation of contrast enhancement techniques for mammographic breast masses
IEEE Transactions on Information Technology in Biomedicine
Texture information in run-length matrices
IEEE Transactions on Image Processing
State-of-the-Art in Visual Attention Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pectoral muscle segmentation: A review
Computer Methods and Programs in Biomedicine
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Screening mammography has been successful in early detection of breast cancer, which has been one of the leading causes of death for women worldwide. Among commonly detected symptoms on mammograms, mass detection is a challenging problem as the task is affected by high complexity of breast tissues, the presence of pectoral muscles as well as varying shape and size of masses. In this research, a novel framework is proposed which automatically detects mass(es) from mammogram(s) even in the presence of pectoral muscles. The framework uses saliency based segmentation which does not require removal of pectoral muscles, if present. From segmented regions, different features are extracted followed by Support Vector Machine classification for mass detection. The experiments are performed using an existing experimental protocol on the MIAS database and the results show that the proposed framework with saliency based region segmentation outperforms the state-of-art algorithms.