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- Kim, Tae-Yun, et al.
(författare)
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Three-dimensional texture analysis of renal cell carcinoma cell nuclei for computerized automatic grading
- 2010
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Ingår i: Journal of medical systems. - Netherlands : Springer. - 0148-5598 .- 1573-689X. ; 34:4, s. 709-716
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Tidskriftsartikel (refereegranskat)abstract
- 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%.
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| 5. |
- Jarkrans, Torsten, et al.
(författare)
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Grading of transitional cell bladder carcinoma by image analysis of histological sections
- 1995
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Ingår i: Analytical Cellular Pathology. - : ELSEVIER SCI PUBL IRELAND LTD. - 0921-8912 .- 1878-3651. ; 8:2, s. 135-158
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Tidskriftsartikel (refereegranskat)abstract
- Image analysis of histological sections was used to achieve a more objective malignancy grading of transitional cell carcinoma of the bladder. Images from Feulgen-stained sections from a clinical material of 197 tumours were analyzed. Features at various
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| 6. |
- Kim, Tae-Yun, et al.
(författare)
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3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading
- 2014
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Ingår i: Computational & Mathematical Methods in Medicine. - : Hindawi Publishing Corporation. - 1748-670X .- 1748-6718. ; 2014, s. 536217:1-12
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Tidskriftsartikel (refereegranskat)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.
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