Abstract
The paper puts forward a new data set comprising 357 histopathological image samples obtained from colon tissues and distinguished into four cancer grades. At the same time, it proposes an automatic methodology for extracting knowledge from these images and discriminating between the disease stages on its base. The approach identifies the glands and nuclei and uses morphological and topological features related to these components to generate 76 attributes that are further used for classification via support vector machines. The values of one parameter used for the identification of the nuclei are tuned and surprisingly good results are reached when overlapping nuclei are identified as singular objects.
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Acknowledgments
Present work was supported by the research grant no. 26/2014, code PN-II-PT-PCCA-2013-4-1153, entitled IMEDIATREAT—Intelligent Medical Information System for the Diagnosis and Monitoring of the Treatment of Patients with Colorectal Neoplasm—financed by the Romanian Ministry of National Education (MEN)—Research and the Executive Agency for Higher Education Research Development and Innovation Funding (UEFISCDI).
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Stoean, C., Stoean, R., Sandita, A., Ciobanu, D., Mesina, C., Gruia, C.L. (2016). SVM-Based Cancer Grading from Histopathological Images Using Morphological and Topological Features of Glands and Nuclei. In: Pietro, G., Gallo, L., Howlett, R., Jain, L. (eds) Intelligent Interactive Multimedia Systems and Services 2016. Smart Innovation, Systems and Technologies, vol 55. Springer, Cham. https://doi.org/10.1007/978-3-319-39345-2_13
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DOI: https://doi.org/10.1007/978-3-319-39345-2_13
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