Multimodal Indexing and Information Retrieval System Based on Mammographic Image Analysis: Gabor Model in Digital Learning
In this chapter, we propose a new indexing approach on medical "image scanner" databases combining the analysis process of the texture characteristics with the descriptive information. The proposed model is based on the digital image components using the characteristics vector. This vector represents the morphological processing result on image texture. It is linked to image semantic attributes using the annotations of medical professionals. Our application context is based on "Mammographic Image Analysis" (MIAS) in databases. The first aspect concerning the morphology processing on images called the "numerical signature" vector. In this approach, the texture analysis of the image 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: image characteristics as multi index vectors. In online, the query processing by image in real time defines the query signature (or image query vectors) and determines all similarities by multi index matching with images in databases. The similarities are built between the image query and images in MIAS databases using the same Gabors' algorithms implementation. 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 efficient results show a set of successful criteria in image representations based on the Gabor's Wavelets, semantic attributes and significant ratios in the system recall and precision. The objective is to design an intelligent application to assist medical professionals in the decision making on tumor dignosis based on mammography scanner. Multimodal Indexing and Information Retrieval System Based on Mammographic Image Analysis: Gabor Model in Digital Learning