Blood corpuscles classification schemes for automated diagnosis of hepatitis using ISODATA algorithm and Run Length Encoding
نویسندگان
چکیده
This paper is the second part of a case study concerning shape classification and shape recognition for automatic diagnosis of hepatitis. Two new methods are proposed and tested here. The first method is a classification schema based on ISODATA algorithm. This approach requires corpuscle segmentation to be performed on each input image in order to extract area, perimeter and circularity coefficients for all corpuscles which are further classified according to these features. The second proposed method is an adaptive hard erosion procedure based on k-Means and Run Length Encoding, designed for speed and real-time applications. A series of experimentally derived conclusions are supplied in the final part of the paper. 2000 Mathematics Subject Classification: Primary 92C50; Secondary 68T10, 68U10. Faculty of Mathematics and Computer Science, University of Piteşti Faculty of Mathematics and Computer Science, University of Bucharest Faculty of Mathematics and Computer Science, University Spiru Haret 1 2 L. State, I. Paraschiv-Munteanu, N. Popescu-Bodorin
منابع مشابه
Blood corpuscles classification schemes for automated diagnosis of hepatitis
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