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