Abslract-This work is concerned with ihe automatic indexing of medical images according to their medical modality for image retrieval purposes inside the ClSMeF health-catalogue. Tbe paper investigates the extraction of an accurate modality signature from gray-level medical images based on vadws histogram weigtttlng-schemes. Our medlcal image database contains six main modalities and was selected by a medical specialist, from a real healthcare environment. We extraded and compared the relative contribution of different weight+ histogram feature vectors In describing the visual content of medical images. The highest modality dassitication accuracy (78.67%) was o b~h e d with the LBP (Local Binary Pattern) weighted histogram, using a SVM classifier.
At present time the Internet has become a major source of information and a powerful didactic tool. Furthermore, the development of digital equipment, allows to acquire and store large quantities of medical data, including images. In the context of the CISMeF on-line health-catalogue, our work is centered on the automatic categorization of medical images according to their visual content, for further indexation and retrieval tasks. The aim of the present study is to assess the performance of a new image symbolic descriptor for medical modality, anatomic region and view angle image categorization. This descriptor is issued from the unsupervised partition of statistical and texture image subblock representations. A medical image database of 10322 images from 33 classes was ground-truthed by a domain expert. Despite the complexity and variability of medical images, the compact symbolic representation approach proposed in this paper achieves high recognition rates. Thus, using kNN classifiers, we obtain an average precision of 83% and a top performance of 91.19%.
In this paper we evaluate the relevance of the extracting image-related information [3] [4]. More recently, information extracted from the visual content of medical im-content-based image description, annotation, indexing and ages and from the image-related text-regions, as well as the retrieval methods were proven to be powerful tools when performance gain obtained by combining the two approaches. searching for images in non-annotated databases [5], [6]. Over First we annotate the images using a content-based annotation hears for cm bin ontexttatddates [5], [ nOne
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