In this chapter, an overview of recent developments in image analysis and understanding techniques for automated detection of breast cancer from digital mammograms is presented. The various steps in the design of an automated system (i.e. Computer Aided Detection [CADe] and Computer Aided Diagnostics (CADx)] include preparation of image database for classification, image pre-processing, mammogram image enhancement and restoration, segmentation of Region Of Interest (ROI) for cancer detection, feature extraction of selected ROIs, feature evaluation and selection, and classification of selected mammogram images in to benign, malignant, and normal. In this chapter, a detailed overview of the various methods developed in recent years for each stage required in the design of an automated system for breast cancer detection is discussed. Further, the design, implementation and performance analysis of a CAD tool is also presented. The various types of features extracted for classification purpose in the proposed tool include histogram features, texture features, geometric features, wavelet features, and Gabor features. The proposed CAD tool uses fuzzy c-means segmentation algorithm, the feature selection algorithm based on the concepts of genetic algorithm which uses mutual information as a fitness function, and linear support vector machine as a classifier.
Laser-induced photoacoustic imaging for breast cancer detection using multivariate image analysis.
