Abstract
The mammography is the most effective procedure for an early diagnosis of the breast cancer. Mammographic screening has been shown to be effective in reducing mortality rates by 30%–70%. In the analysis of Mammography images using Computer-Aided Diagnosis, Segmentation stage is one of the most Significant step, since it affects the accuracy of the Feature Extraction & Classification. In this paper, Rough k-means approach is used for segmentation of tumor from breast parenchyma. Pixel objects which definitely belong to the tumor region are classified under lower approximation, where as objects which possibly belong to the same are categorized as upper approximation. The difference of upper and lower approximation will result with objects in the rough boundaries. The segmentation algorithm has been verified on Mammograms from Mias database and the CICRI database. (Central India Cancer Research Institute, Nagpur, India). Geometrical and Textural features were calculated for segmented region. Once the features were computed for each region of interest (ROI), they are used as inputs to Artificial Neural Network (ANN) for classification as Benign or Malignant. Results of Rough k-means segmentation were compared with Otsu method of segmentation using ANN. Results indicate that Rough k-means method performs better than Otsu method in terms of classification accuracy up to 95% and can also reduces the number of biopsies required in the diagnostic process.
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Bora, V.B., Kothari, A.G., Keskar, A.G. (2012). Mammogram Segmentation Using Rough k-Means and Mass Lesion Classification with Artificial Neural Network. In: Hassanien, A.E., Salem, AB.M., Ramadan, R., Kim, Th. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2012. Communications in Computer and Information Science, vol 322. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35326-0_7
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DOI: https://doi.org/10.1007/978-3-642-35326-0_7
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