DETECTION OF MICROCALCIFICATION CLUSTERS USING STATISTICAL PARAMETERS AND DYADIC CONTOURLET TRANSFORM BASED PRECISION ENHANCEMENT
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 9, Pages 2423-2436
Abstract—Recent scenario, breast cancer found to be a threat and dangerous carcinoma among women in the world. In contemplation of reducing the breast, cancer-related death needs an efficient computer-aided diagnosis (CAD) system. The discrimination of microcalcification clusters (MCCs) is an important manifestation for the early diagnosis of breast cancer. This paper focuses on the detection of breast cancers cells size below 2mm. To achieve précised enhanced cancer cell region an efficient technique dyadic Counterlet transform (DCTs) in two dimension is proposed. The enhancement of cancer cell region obtained through preserving the boundaries and borders with curvature for a small region.
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