Discriminal exploration of image features for describing visual impressions of black fabrics
Design and application of hybrid intelligent systems
Texture analysis on MRI images of non-Hodgkin lymphoma
Computers in Biology and Medicine
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Data mining for large scale 3D seismic data analysis
Machine Vision and Applications
Supervised segmentation of volume textures using 3D probabilistic relaxation
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
A supervised learning based approach to detect crohn's disease in abdominal MR volumes
MICCAI'12 Proceedings of the 4th international conference on Abdominal Imaging: computational and clinical applications
Landmark detection in cardiac MRI using learned local image statistics
STACOM'12 Proceedings of the third international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
| Hi-index | 0.01 |
Two approaches to the characterization of three-dimensional (3-D) textures are presented: one based on gradient vectors and one on generalized co-occurrence matrices. They are investigated with the help of simulated data for their behavior in the presence of noise and for various values of the parameters they depend on. They are also applied to several medical volume images characterized by the presence of microtextures and their potential as diagnostic tools and tools for quantifying and monitoring the progress of various pathologies is discussed. No firm medical conclusions can be drawn as not enough clinical data are available. The gradient based method appears to be more appropriate for the characterization of microtextures. It also shows more consistent behavior as a descriptor of pathologies than the generalized co-occurrence matrix approach