![](/images/graphics-bg.png)
3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading
Joint Authors
Bengtsson, Ewert
Kim, Tae-Yun
Cho, Nam-Hoon
Jeong, Goo-Bo
Choi, Heung-Kook
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-10-09
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Kim, Tae-Yun& Cho, Nam-Hoon& Jeong, Goo-Bo& Bengtsson, Ewert& Choi, Heung-Kook. 2014. 3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading. Computational and Mathematical Methods in Medicine،Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1034667
Modern Language Association (MLA)
Kim, Tae-Yun…[et al.]. 3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading. Computational and Mathematical Methods in Medicine No. 2014 (2014), pp.1-12.
https://search.emarefa.net/detail/BIM-1034667
American Medical Association (AMA)
Kim, Tae-Yun& Cho, Nam-Hoon& Jeong, Goo-Bo& Bengtsson, Ewert& Choi, Heung-Kook. 3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading. Computational and Mathematical Methods in Medicine. 2014. Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1034667
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1034667