Gradient and texture analysis for the classification of mammographic masses

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A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs (2007, methodology): “[78] computed Haralick’s texture measures using adaptive ribbons of pixels extracted around mammographic masses (see Fig.”
Approaches for automated detection and classification of masses in mammograms (2006, unknown): “Almost twenty significant shape features are extracted in a variety of classifiers [70,118,124,129–131, 133–139,141,142,193].”
A curated mammography data set for use in computer-aided detection and diagnosis research (2017, unknown): “Mudigonda et al.(26) 39 Public (MIAS) 82.”
Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions (2003, background): “This is different from digital mammography where tumors and microcalcifications are typically the brighter regions in the image [3], [5]–[7], [14], [41].”
Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances (2009, methodology): “[71] proposed a method using both gradient-based and texture-based features to differentiate benign masses from malignant tumors.”
Exploring feature-based approaches in PET images for predicting cancer treatment outcomes (2009, background): “Therefore, they have been widely applied in many complex industrial and medical pattern recognition tasks [20-25].”