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Comput Math Methods Med. 2014;2014:536217. doi: 10.1155/2014/536217. Epub 2014 Oct 9.

3D texture analysis in renal cell carcinoma tissue image grading.

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Department of Computer Engineering, Inje University, Injero 197, UHRC, Gimhae, Gyeongnam 621-749, Republic of Korea.
Department of Pathology, Yonsei University, Seoul 120-749, Republic of Korea.
Department of Anatomy, Gachon University, Incheon 406-799, Republic of Korea.
Centre for Image Analysis, Uppsala University, 75105 Uppsala, Sweden.


One of the most significant processes in cancer cell and tissue image analysis is the efficient extraction of features for grading purposes. This research applied two types of three-dimensional texture analysis methods to the extraction of feature values from renal cell carcinoma tissue images, and then evaluated the validity of the methods statistically through grade classification. First, we used a confocal laser scanning microscope to obtain image slices of four grades of renal cell carcinoma, which were then reconstructed into 3D volumes. Next, we extracted quantitative values using a 3D gray level cooccurrence matrix (GLCM) and a 3D wavelet based on two types of basis functions. To evaluate their validity, we predefined 6 different statistical classifiers and applied these to the extracted feature sets. In the grade classification results, 3D Haar wavelet texture features combined with principal component analysis showed the best discrimination results. Classification using 3D wavelet texture features was significantly better than 3D GLCM, suggesting that the former has potential for use in a computer-based grading system.

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