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الثلاثاء، 5 أغسطس، 2014

Semantic Content Based Medical Image Retrieval Using Invariant Contourlet Features with Relevance Feedback Techniques


 Author        : Hassan Moustafa Abd El-Rahman Fayed
Degree        : Ph.D. Electric Communication
Title: Semantic Content Based Medical Image Retrieval Using Invariant Contourlet Features with Relevance Feedback Techniques
 Abstract
The humans live in a world where they are surrounded by ever increasing numbers of images. More often than not, these images have very little metadata by which they can be indexed and searched. In order to avoid information overload, techniques need to be developed to enable these image collections to be searched by their content. Much of the previous work on image retrieval has used global features such as color and texture to describe the content of the image. However, these global features are insufficient to accurately describe the image content when different parts of the image have different characteristics.
Advances in digital imaging technologies and the increasing prevalence of picture archival systems have led to an exponential growth in the number of images generated and stored in hospitals during recent years. Thus, automatic medical image annotation and categorization can be very useful for the purposes of image database management. Conventional image retrieval systems are based on textual annotation where key information about the image is stored. In medical images it forms an essential component on a patient’s record. However, in many occasions this information is very often lost as consequences of image compression or human error. Also, given the number of different standards adopted for medical image annotation, building a comprehensive ontology regarding medical terms is not always consensual. Recently, advances in Content Based Image Retrieval prompted researchers towards new approaches in information retrieval for image databases. In medical applications it already met some degree of success in constrained problems.
This thesis discusses how this problem can be circumvented by using Interest points (also known as key points) or salient interest regions to select the areas of the image that are most interesting to describe the medical image characteristics. The thesis discusses a number of different saliency detectors that are suitable for robust retrieval purposes and performs a comparison between a numbers of these key points’ detectors. Using these robust retrieval techniques, a new paradigm in image retrieval is discussed; the detecting and matching features across multiple views of a scene are a critical first step in many computer vision algorithms for dynamic scene analysis.
Further, relevance feedback technique is used to bridge the gap between low levels features and high level concepts. The proposed method is tested on a large medical image database which shows a significant improvement in precision and average retrieval rate (ARR) with relevance feedback.

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