title: 3D texture analysis in renal cell carcinoma tissue image grading.
abstract: 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.
authors.affiliation: Department of Computer Engineering, Inje University, Injero 197, UHRC, Gimhae, Gyeongnam 621-749, Republic of Korea.
authors.forename: Tae-Yun
authors.identifier: 0000-0001-5544-2381
authors.initials: TY
authors.lastname: Kim
authors.affiliation: Department of Pathology, Yonsei University, Seoul 120-749, Republic of Korea.
authors.forename: Nam-Hoon
authors.initials: NH
authors.lastname: Cho
authors.affiliation: Department of Anatomy, Gachon University, Incheon 406-799, Republic of Korea.
authors.forename: Goo-Bo
authors.identifier: 0000-0003-3844-2368
authors.initials: GB
authors.lastname: Jeong
authors.affiliation: Centre for Image Analysis, Uppsala University, 75105 Uppsala, Sweden.
authors.forename: Ewert
authors.identifier: 0000-0002-1636-3469
authors.initials: E
authors.lastname: Bengtsson
authors.affiliation: Department of Computer Engineering, Inje University, Injero 197, UHRC, Gimhae, Gyeongnam 621-749, Republic of Korea.
authors.forename: Heung-Kook
authors.initials: HK
authors.lastname: Choi
mesh_terms: D000465:Algorithms; D002292:Carcinoma, Renal Cell / Q000473:pathology*; D003952:Diagnostic Imaging / Q000379:methods; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008113:Liver Neoplasms / Q000473:pathology*; D018613:Microscopy, Confocal / Q000379:methods*; D015233:Models, Statistical; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D058067:Wavelet Analysis