During the last decade, several works have dealt with computer automatic diagnosis (CAD) of masses in digital mammograms. Generally, the main difficulty remains the detection of masses. This work proposes an efficient methodology for mass detection based on a new local feature extraction. Local binary pattern (LBP) operator and its variants proposed by Ojala are a powerful tool for textures classification. However, it has been proved that such operators are not able to model at their own texture masses. We propose in this paper a new local pattern model named gray level and local difference (GLLD) where we take into consideration absolute gray level values as well as local difference as local binary features. Artificial neural networks (ANNs), support vector machine (SVM), and k-nearest neighbors (kNNs) are, then, used for classifying masses from nonmasses, illustrating better performance of ANN classifier. We have used 1000 regions of interest (ROIs) obtained from the Digital Database for Screening Mammography (DDSM). The area under the curve of the corresponding approach has been found to be A z = 0.95 for the mass detection step. A comparative study with previous approaches proves that our approach offers the best performances.

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Microcalcifications are small deposits of calcium accumulated in breast tissue. They are situated in higher gray level regions, with a very short gray level dynamic range. Besides, they have a small size with low contrast compared to the surrounding tissue. All these characteristics make microcalcification's preprocessing a crucial task. In this paper, we propose a new approach for enhancing microcalcifications in digitized mammogram, emphasizing corresponding gray level details. Accordingly, we propose an adaptive exponential function which creates a local modification of gray level to highlight details which are potential carriers of microcalcifications. We have applied the NLS twice: locally and globally. The performance of microcalcification's enhancement method is developing using Farabi Digital Database of Screening Mammography (FDDSM). Performance results are given in terms of the Seconde-Derivativelike Measure of enhancement (SDME). Our proposed approach achieve 118 of the local NLS and 115 of the Global NLS.

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