A combined Approach Based on Fuzzy Classification and Contextual Region Growing to Image Segmentation

TL;DR

This paper introduces a novel image segmentation method combining fuzzy semantic classification with context-based region-growing, effectively bridging the semantic gap and improving segmentation quality, demonstrated on mammogram images.

Contribution

It proposes a high-level segmentation approach that integrates fuzzy classification with region-growing, utilizing domain knowledge and fuzzy contextual information.

Findings

Effective segmentation on mammogram images

Reduces semantic gap in image analysis

Utilizes fuzzy contextual information for improved accuracy

Abstract

We present in this paper an image segmentation approach that combines a fuzzy semantic region classification and a context based region-growing. Input image is first over-segmented. Then, prior domain knowledge is used to perform a fuzzy classification of these regions to provide a fuzzy semantic labeling. This allows the proposed approach to operate at high level instead of using low-level features and consequently to remedy to the problem of the semantic gap. Each over-segmented region is represented by a vector giving its corresponding membership degrees to the different thematic labels and the whole image is therefore represented by a Regions Partition Matrix. The segmentation is achieved on this matrix instead of the image pixels through two main phases: focusing and propagation. The focusing aims at selecting seeds regions from which information propagation will be performed. Thepropagation phase allows to spread toward others regions and using fuzzy contextual information the needed knowledge ensuring the semantic segmentation. An application of the proposed approach on mammograms shows promising results