Abstract
We propose a method to classify breast lesions of ducatal origin. The materials were tissue sections of the intraductal proliferative lesions of the breast: benign(DH:ductal hyperplasia), ductal carcinoma in situ(DCIS). The total 40 images from 70 samples of ducts were digitally captured from 15 cases of DCIS and 25 cases of DH diagnosed by pathologist. To assess the correlation between computerized images analysis and visual analysis by a pathologist, we extracted the total lumen area/gland area, to segment the gland(duct) area used a snake algorithm, to segment the lumen used multilevel Otsus method in the duct from 20x images for distinguishing DH and DCIS. In duct image, we extracted the five texture features (correlation, entropy, contrast, angular second moment, and inverse difference moment) using the co-occurrence matrix for a distribution pattern of cells and pleomorphism of the nucleus. In the present study, we obtained classification accuracy rates of 91.33%, the architectural features of breast ducts has been advanced as a useful features in the classification for distiguishing DH and DCIS. We expect that the proposed method in this paper could be used as a useful diagnostic tool to differentiate the intraductal proliferative lesions of the breast.
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Hwang, H., Yoon, H., Choi, H., Kim, M., Choi, H. (2007). Image Analysis of Ductal Proliferative Lesions of Breast Using Architectural Features. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2007. Lecture Notes in Computer Science(), vol 4482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72530-5_17
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DOI: https://doi.org/10.1007/978-3-540-72530-5_17
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