Multimodal Indexing and Information Retrieval in Medical Image Mammographies: Digital Learning Based on Gabor Filters Model

Abstract : In this chapter, we propose a new indexing approach on medical “image scanner” databases combining the analysis process of the texture characteristics with the information contents. The proposed model is based on the digital image components using the vector of characteristics. This vector represent the morphological processing result on image texture. It is linked to semantic attributes of the image using the annotations of medical professionals. Our context of study is based on “Mammographic Image Analysis” (MIAS) in databases. The first aspect concerning the morphology processing on images called the “numerical signature” vector. In our approach, the image analysis of the texture is based on the Gabor Wavelets (or Filters) Theory. In offline processing for each image in MIAS databases, the Gabor Wavelets determine all numerical signatures: vectors of image characteristics as multi-index. In online, the query by image is in real-time processing to define the query signature (or image-query vectors) and to determine similarities by matching of multi-index with all images in databases. The similarities are built between the image-query and images in MIAS databases using the same Gabors’ algorithms implemented. In order to evaluate the robustness of our system (based on multi-index, semantic attributes, query and information retrieval by image), we experiment with a controlled database of 320 mammographies. The performance results show a set of successful criteria in image representations based on the Gabor’s Wavelets, semantic attributes and combining with significant ratios in the system recall and precision.