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ReserarchID
SFR2X417
Crossref Journal DOI 10.17406/GJSFR
Print ISSN 0975-5896
e-ISSN 2249-4626
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Breast cancer is one of the main causes of cancer death in women. Detection is efficiently performed by using digital mammograms. Small clusters of micro calcifications appearing as a collection of white spots on mammograms show an early warning of breast cancer. Early detection performed on X-ray mammography is the key to improving breast cancer diagnosis. To increase radiologists’ diagnostic performance, several computer-aided diagnosis (CAD) schemes have been developed to improve the detection of primary identification of this disease. In this research, an attempt is made to develop an adaptive K-means clustering algorithm for breast image segmentation to detect microcalcifications. The method was tested over several images of image databases taken from Mammocare, Ghana for cancer research and diagnosis. The algorithm works faster so that any radiologist can take a clear decision about the appearance of microcalcifications by visual inspection of digital mammograms and detection accuracy has also improved as compared to some existing works.
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