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Using Global Shape Descriptors for Content Medical-Based Image Retrieval
Abstract
In this chapter, the authors propose a new method belonging to content medical-based image retrieval approaches and that uses a set of region-based shape descriptors. The search engine discussed in this work allows the classification of newly acquired medical images into some well known categories and also to get the images that are more similar to a query image. The final goal is to help the medical staff to annotate these images. To achieve this task, the authors propose a set of three descriptors that are based on: (1) Hu, (2) Zernike moments, and (3) Fourier transform-based signature, which are considered as region descriptors. The advantage of using this kind of global descriptor is that they are very fast, real time, and they do not need any segmentation step. The authors propose also a comparative study between these three approaches. The search engines are tested by using a database composed of 75 images that have different sizes, and that are classified into five classes. The results provided by the proposed retrieval approaches are given with high precision. The comparison between the three approaches is achieved using classification matrices and the recall/precision curves. The three proposed retrieval approaches produce accurate results in real time. This proves the advantage of using global shape features as a preliminary classification step in an automated aided diagnosis system.
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