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Microcalcifications are very tiny deposits of calcium allocated in the breast tissue. Their gray level is similar to the dense normal breast tissue so its very difficult to differentiate between them. Once detected, its very difficult to between malign end benign microcalcifications. In this paper, we apply a new method to extract features of microcalcifications in order to classify them into malign...
Microcalcifications are tiny deposits of calcium located in breast tissue. They appeared as very small highlighted regions in comparison with their surrounding tissue. Spatial non linear enhancement can be applied for microcalcification detection. However, efficiency of a such approach depends on breast density: in case of extreme breast density, the contrast between microcalcification’s details and...
This paper studies the computer-aided diagnosis technique potential in discriminating accurately benign masses among a given subset of 100 patients which makes it possible to degrade cases from Breast Imaging-Reporting and Data System (BIRADS) 3 to BIRADS 2 avoiding prospective biopsies. Such accuracy is required since expert radiologists assign BIRADS3 category by default mostly for reducing false...
Microcalcifications are tiny deposits of calcium located in breast tissue. They appeared as very small highlighted regions in comparaison with their surrounding tissue. The difference of contrast between microcalcifications and the normal tissue depend on the breast density: The more the breast is dense, the less is the contrast. In this context, we propose to enhance microcalcifications details for...
In this paper, we present a novel extension of the Gray Level and Local Difference (GLLD) method and it is named as Multi-scale GLLD for texture classification. In the GLLD, a local region is described by its central pixel and the local difference sign-magnitude. The central pixels representing the image gray level are transformed into a binary code by global thresholding. The local difference sign-magnitude...
Computer-Aided Detection and Diagnosis (CADD) systems have been created in the last two decades to help radiologists either in the automatic detection or diagnosis of abnormalities in mammographic images. Accordingly, the developed algorithms in an existing amount of mammographic images should be assessed on a large database. For a fair validation, the database should be very representative including...
An improved computer system has been presented to classify the mass and identify the different stages of breast cancer using artificial neural network (ANN). In this paper, we extract texture and shape features. The accuracy of the proposed system is 99.5%. The images from Farabi digital database for screening mammography have been applied for the development of the proposed system. This later may...
Masses are important elements in the diagnosis of breast cancer. Many studies discussed the problem of detection and/or diagnosis of masses and most of these researches were based on shape descriptors to make decision. Textural descriptors contribute in indicating the presence of masses. Morphological descriptors determine their malignancy degree. Thus, we decided in our work to make a combination...
Single modality biometric recognition system is often not able to meet the desired system performance requirements. Several studies have shown that multimodal biometric identification systems improve the recognition accuracy and allow performances that are required for many security applications. In this paper, we have developed a multimodal biometric recognition system which combines two modalities:...
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