Texture and color based image segmentation and pathology detection in capsule endoscopy videos
Computer Methods and Programs in Biomedicine
| Hi-index | 0.00 |
This paper undertakes the problem of quantitative inspection of 3D vascular tree images. Through the use of cluster analysis, it confirms the correspondence between texture descriptors and various vessel system parameters, such as blood viscosity and the number of tree branches. Moreover, it is shown that unsupervised selection of significant texture parameters, especially in the synthetic data sets corresponding to noisy images, becomes feasible if the search for relevant attributes is guided by the clustering stability-based optimization criterion.