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Three-dimensional texture analysis of renal cell carcinoma cell nuclei for computerized automatic grading
(School of computer engineering, Inje University, Korea)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
(School of computer engineering, Inje University, Korea)
(Ubiquitous Healthcare Research Center, Inje University, Korea)
2010 (English)In: Journal of medical systems, ISSN 0148-5598, E-ISSN 1573-689X, Vol. 34, no 4, 709-716 p.Article in journal (Refereed) Published
Abstract [en]

The extraction of important features in cancer cell image analysis is a key process in grading renal cell carcinoma. In this study, we analyzed the three-dimensional chromatin texture of cell nuclei based on digital image cytometry. Individual images of 2,423 cell nuclei were extracted from 80 renal cell carcinomas (RCCs) using confocal laser scanning microscopy (CLSM). First, we applied the 3D texture mapping method to render the volume of entire tissue sections. Then, we determined the chromatin texture quantitatively by calculating 3D gray level co-occurrence matrices and 3D run length matrices. Finally, to demonstrate the suitability of 3D texture features for classification, we performed a discriminant analysis. In addition, we conducted a principal component analysis to obtain optimized texture features. Automatic grading of cell nuclei using 3D texture features had an accuracy of 78.30%. Combining 3D textural and 3D morphological features improved the accuracy to 82.19%.

Place, publisher, year, edition, pages
Netherlands: Springer , 2010. Vol. 34, no 4, 709-716 p.
Keyword [en]
Digital image cytometry, Three-dimensional texture analysis, Nuclear chromatin, Renal cell carcinoma
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:uu:diva-114076DOI: 10.1007/s10916-009-9285-6ISI: 000280071200031PubMedID: 20703926OAI: oai:DiVA.org:uu-114076DiVA: diva2:292790
Available from: 2010-02-09 Created: 2010-02-09 Last updated: 2017-12-12Bibliographically approved

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