Feature Descriptor Optimization in Medical Image Retrieval Based on Genetic Algorithm

This paper presents an approach to represent and match images for retrieval in medical archives. A multidimensional low-level feature space including shape andtexture is used to represent the image input. The large intensity variation and low contrast are main characteristics of themedical images. This presents a challenge to matching among theimages, and is handled via an illumination-invariant representation. In accordance with this issue, we used severaltechniques based on Local Binary Pattern (LBP) such as Uniform LBP, Local Binary Count (LBC) and Complete LBC (CLBC) to extract texture features. Furthermore, one dimensional Fourier Descriptor (1-D FD) and 2-D Modified Generic FourierDescriptor (MGFD) are used to extract shape features frommedical images. Combining feature descriptors in content-based image retrieval (CBIR) systems, plays a key role due to improvethe retrieval performance and reduce semantic gap between the visual features and semantics concepts. Hence, we present anapproach based on Genetic Algorithm (GA) to optimize the contribution of each feature descriptors in retrieval process, andlink a bridge between query concepts and low level features. The obtained results show that the proposed GA-based approach significantly improves the accuracy of content-based medical image retrieval (CBMIR) system.

Feature Descriptor Optimization in Medical Image Retrieval Based on Genetic Algorithm Keywords:

content-based medical image retrieval , visual features , semantic concepts , Genetic Algorithm