Y. Alp Aslandogan

A methodological approach to the classification of dermoscopy images

In this paper a methodological approach to the classification of pigmented skin lesions in dermoscopy images is presented. First, automatic border detection is performed to separate the lesion from the background skin. Shape features are then extracted from this border. For the extraction of color and texture related features, the image is divided into various clinically significant regions using the Euclidean distance transform. This feature data is fed into an optimization framework, which ranks the features using various feature selection algorithms and determines the optimal feature subset size according to the area under the ROC curve measure obtained from support vector machine classification. The issue of class imbalance is addressed using various sampling strategies, and the classifier generalization error is estimated using Monte Carlo cross validation. Experiments on a set of 564 images yielded a specificity of 92.34% and a sensitivity of 93.33%.

Unsupervised border detection in dermoscopy images.

Background: As a result of the advances in skin imaging technology and the development of suitable image processing techniques, during the last decade, there has been a significant increase of interest in the computer‐aided diagnosis of skin cancer. Automated border detection is one of the most important steps in this procedure as the accuracy of the subsequent steps crucially depends on the accuracy of this step.

Nonlinear vector filtering for impulsive noise removal from color images

A comprehensive survey of 48 filters for impulsive noise removal from color images is presented. The filters are formulated using a uniform notation and categorized into 8 families. The performance of these filters is compared on a large set of images that cover a variety of domains using three effectiveness and one efficiency criteria. In order to ensure a fair efficiency comparison, a fast and accurate approximation for the inverse cosine function is introduced. In addition, commonly used distance measures (Minkowski, angular, and directional-distance) are analyzed and evaluated. Finally, suggestions are provided on how to choose a filter given certain requirements.

Robust content-based image indexing using contextual clues and automatic pseudofeedback

Abstract.In this paper we present a robust information integration approach to identifying images of persons in large collections such as the Web. The underlying system relies on combining content analysis, which involves face detection and recognition, with context analysis, which involves extraction of text or HTML features. Two aspects are explored to test the robustness of this approach: sensitivity of the retrieval performance to the context analysis parameters and automatic construction of a facial image database via automatic pseudofeedback. For the sensitivity testing, we reevaluate system performance while varying context analysis parameters. This is compared with a learning approach where association rules among textual feature values and image relevance are learned via the CN2 algorithm. A face database is constructed by clustering after an initial retrieval relying on face detection and context analysis alone. Experimental results indicate that the approach is robust for identifying and indexing person images.

Database research at UT Arlington

The Information Technology Laboratory (or ITLab) at the Computer Science and Engineering Department at The University of Texas at Arlington was established by Sharma Chakravarthy in Spring 2000. The mission of the ITLab is to conduct research and development on all aspects of information technology. Some of the topics currently being investigated are: Data Warehousing/Information Integration, Data Mining/Knowledge Discovery, Stream Data Processing, Web Search, Image Databases, Active/Push Technology, Video Streaming, and Object-Oriented, Temporal & Heterogeneous Databases.