Summary
The paper presents k-means based hybrid segmentation method for breast cancer diagnosis problem. It is part of the computer system to support diagnosis based on microscope images of the fine needle biopsy. The system assumes distinguishing malignant from benign cases. Described method is an alternative to the previously presented algorithms based on fuzzy c-means clustering and competitive neural networks. However, it uses similar idea of combining clustering in RGB space with adaptive thresholding. At first, thresholding reveals objects on background. Then image is clustered with k-means algorithm to distinguish nuclei from red blood cells and other objects. Correct segmentation is crucial to obtain good quality features measurements and consequently successful diagnosis. The system of malignancy classification was tested on a set of real case medical images with promising results.
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Filipczuk, P., Kowal, M., Obuchowicz, A. (2011). Automatic Breast Cancer Diagnosis Based on K-Means Clustering and Adaptive Thresholding Hybrid Segmentation. In: Choraś, R.S. (eds) Image Processing and Communications Challenges 3. Advances in Intelligent and Soft Computing, vol 102. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23154-4_33
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DOI: https://doi.org/10.1007/978-3-642-23154-4_33
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