Aboul Ella Hassanien (Abo)

The risk of hepatitis-C virus is considered as a challenge in
the field of medicine. Applying feature reduction technique and generating
rules based on the selected features were considered as an important
step in data mining. It is needed by medical experts to analyze the generated
rules to find out if these rules are important in real life cases.
This paper presents an application of a rough set analysis to discover
the dependency between the attributes, and to generate a set of reducts
consisting of a minimal number of attributes. The experimental results
obtained, show that the overall accuracy offered by the rough sets is high.

This paper presents a feature-based retinal image registration schema. A structural feature, namely, bifurcation structure, has been used for the proposed feature-based registration schema. The bifurcation structure is composed of a master bifurcation point and its three connected neighbors. The characteristic vector of each bifurcation structure consists of the normalized branching angle and length, which is invariant against translation, rotation, scaling, and even modest distortion. The proposed schema is composed of five fundamental phases, namely, input retinal images pre-processing, vascular network detection, noise removal, bifurcation points detection in vascular networks, and bifurcation points matching in pairs of retinal images. The effectiveness of the proposed schema is demonstrated by the experiments with 12 pairs retinal images collected from clinical patients. The registration is carried out through optimizing a certain similarity function, namely, normalized correlation of images. It has been observed that the proposed schema has achieved good performance accuracy.

“Prevention is better than cure”, true statement which all of us neglect. One of the most reasons which cause speedy recovery from any diseases is to discover it in advanced stages. From here come the importance of computer systems which preserve time and achieve accurate results in knowing the diseases and its first symptoms .One of these systems is retinal image analysis system which considered as a key role and the first step of Computer Aided Diagnosis Systems (CAD). In addition to monitor the patient health status under different treatment methods to ensure How it effects on the disease.. In this chapter the authors examine most of approaches that are used for vessel segmentation for retinal images, and a review of techniques is presented comparing between their quality and accessibility, analyzing and catgrizing them. This chapter gives a description and highlights the key points and the performance measures of each one.

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Rough sets offer an effective approach of managing uncertainties and can be employed for tasks such as data dependency analysis, feature identification, dimensionality reduction, and pattern classification. As these tasks are common in many medical applications it is only natural that rough sets, despite their relative ‘youth’ compared to other techniques, provide a suitable method in such applications. In this paper, we provide a short summary on the use of rough sets in the medical informatics domain, focussing on applications of medical image segmentation, pattern classification and computer assisted medical decision making.

Rough set theory is a new soft computing tool which deals with vagueness and uncertainty. It has attracted the attention of researchers and practitioners worldwide, and has been successfully applied to many fields such as knowledge discovery, decision support, pattern recognition, and machine learning. Rough Computing: Theories, Technologies and Applications offers the most comprehensive coverage of key rough computing research, surveying a full range of topics from granular computing to pansystems theory. With its unique coverage of the defining issues of the field, this commanding research collection provides libraries with a single, authoritative reference to this highly advanced technological topic.