Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
The visualization toolkit (2nd ed.): an object-oriented approach to 3D graphics
The visualization toolkit (2nd ed.): an object-oriented approach to 3D graphics
A linear-time component-labeling algorithm using contour tracing technique
Computer Vision and Image Understanding
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This study attempted to develop a method for 3D visualization and quantitative analysis of cell nuclei for renal cell carcinoma (RCC) grading and evaluated the feasibility of such quantitative analysis. We compared the correct classification rate (CCR) for each of the classifiers based on the 2D features of cell nuclei (diameter, area, perimeter, and circularity) and the 3D features of cell nuclei (volume, surface area, and spherical shape factor). The results showed that the classifier using the 3D features provided better results for grading. Our method could overcome the limitations inherent in 2D analysis and could improve the accuracy and reproducibility of quantification of cell nuclei.