A combined topological and statistical approach for interactive segmentation of 3D images

Abstract

International audienceThis paper presents a new framework for an interactive segmentation of 3D images. The framework is based on a bimodal data structure defined by a Region Adjacency Graph (RAG) that is associated with a Hierarchical Classification Tree (HCT). The RAG provides information about the spatial and topological organisation of the extracted regions of the image. The HCT provides information about the similarities between the extracted regions of the image based on a predefined set of features. The first contribution of our work is the combination of a RAG and a HCT. An incremental system was obtained by defining operators that work with and on the RAG and the HCT. If a static predefined processing chain has been defined, these operators can be used in batch mode. If a scheduler is available, they can be used in an adaptive manner. Finally, if a user chooses the operator to be used after each step, the operators can be used interactively. The second contribution of this paper is the formal description of these operators. To give the user the ability to incrementally improve the segmentation, powerful visualisation of the segmentation state and interfaces have been proposed, an important advantage of the proposed framework. To validate the proposed framework, a user study has been conducted in a concrete case of texture segmentation. Our system obtains very satisfactory results even for complex volumetric textures, and helps real users by providing high quality segmentations. The system has been used by specialists in sonography to segment 3D ultrasound images of the skin. Some examples of segmentation are presented to illustrate the benefit of the interactivity provided by our approach

3D Solid Texture Classification Using Locally-Oriented Wavelet Transforms

“…For the segmentation task, the database was used to evaluate several state-of-the-art descriptors in [47] and [48] and to design new features capable to encode the human perception of texture [49]- [51]. Suzuki et al used the database to evaluate a texture retrieval technique developed previously [52], [53], as well as a novel key-point detector [54].…”