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Erschienen in:
2016 | OriginalPaper | Buchkapitel
Decrease in False Assumption for Detection Using Digital Mammography
verfasst von : Chiranji Lal Chowdhary, Gudavalli Vijaya Krishna Sai, D. P. Acharjya
Erschienen in: Computational Intelligence in Data Mining—Volume 2
Verlag: Springer India
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Abstract
Our research work elaborated in the design and construction of a method that bring sustenance for a reduction in false assumptions during the detection of breast cancer. Our key drive of this research work was to elude the false assumptions in the detection practice in a cost effective manner. We proposed a unique method to decrease false assumption in breast cancer detection cases and split this method in three different modules as preprocessing, formation of homogeneous blocks and color quantization. The preprocessing convoluted in eradicating the extraneous slices. The formation homogeneous blocks sub-method was to do segmentation of the image. The task of the third sub-method (i.e. color quantization) was to break the colors amid different regions.