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Microcalcifications Detection in Mammograms Based on Ant Colony Optimization and Markov Random Field - Coursework Example

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From the paper "Microcalcifications Detection in Mammograms Based on Ant Colony Optimization and Markov Random Field" it is clear that the importance of the segmentation phase during the analysis of x-ray breast images led us to investigate new intelligent techniques…
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Microcalcifications Detection in Mammograms Based on Ant Colony Optimization and Markov Random Field
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Download file to see previous pages Oncologists confirm that microcalcifications are the first sign of breast cancer that may warrant a biopsy. The microcalcifications are commonly defined as a tiny deposit of minerals (calcium) that appear like localized high-intensity regions in the mammogram. Microcalcifications are minute (less than half a millimeter) spots. They occur intramammary inside and around the ducts, inside the lobules, in vascular structures, in interlobular connective tissues or fat. Microcalcification detection in mammograms is particularly challenging since abnormalities can pass unnoticed depending on the breast density [6].
Contemporarily, mammography assessments are carried out on patients to detect premature, clinically unsuspected, breast cancer. Computer-assisted schemes that work on image processing are utilized to enhance the diagnostic efficiency. These schemes improve the performances of the automatic computer-aided detections systems that are capable of serving as a pre-reader to the expert radiologist and offer him the second-opinion in the diagnosis [1]. In the literature, many Computer-Aided Detection (CAD) systems for clustered microcalcifications detection were proposed.
Monmarche [2] proposed a computerized methodology for microcalcifications detection on mammograms based on three phases. First, noise filtering is performed to decrease the normal background structure of the mammogram. Secondly, potential microcalcifications are recognized with the aid of global gray-level thresholding, morphological erosion and a local adaptive gray-level thresholding. Thirdly, the number of falsely identified microcalcifications is reduced by examining the statistical and textural features of the abnormalities detected. Lau and Bischof [3] proposed a microcalcification detection algorithm based on merging morphological and mathematical processing of mammograms. ...Download file to see next pages Read More
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