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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