Skip to Main content Skip to Navigation
Conference papers

Texture-Based Graph Regularization Process for 2D and 3D Ultrasound Image Segmentation

Abstract : In this paper, we propose to improve an unsupervised segmentation algorithm based on the graph diffusion and regularization model described by Ta by using Haralick texture features. With this framework, segmentation is performed by diffusing an indicator function over a graph representing an image. The benefit of our approach is to combine two non-local processing techniques: at pixel level with texture features and through the use of a graph structure, which allows to efficiently express relations between non-adjacent pixels. Applied on ultrasound images, and compared to a vector-valued Chan & Vese active contour, our method shows an improvement of the quality of segmentation.
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-01027445
Contributor : Denis Maurel Connect in order to contact the contributor
Submitted on : Tuesday, July 22, 2014 - 8:46:35 AM
Last modification on : Thursday, March 3, 2022 - 5:32:01 PM

Identifiers

  • HAL Id : hal-01027445, version 1

Citation

Cyrille Faucheux, Julien Olivier, Romuald Boné, Pascal Makris. Texture-Based Graph Regularization Process for 2D and 3D Ultrasound Image Segmentation. IEEE 2012 International Conference on Image Processing, Sep 2012, Orlando, United States. pp.2333-2336. ⟨hal-01027445⟩

Share

Metrics

Record views

43