RESEARCH ARTICLE


Region Quad-Tree Decomposition Based Edge Detection for Medical Images



Sumeet Dua*, Naveen Kandiraju, Pradeep Chowriappa
Data Mining Research Laboratory, Department of Computer Science, College of Engineering and Science, Louisiana Tech University, Ruston, LA 71272, USA


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© Dua et al.; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Data Mining Research Laboratory, Department of Computer Science, College of Engineering and Science, Louisiana Tech University, Ruston, LA 71272, USA; Tel: 318-257-2830; Fax: 318-257-4922; E-mail: sdua@latech.edu


Abstract

Edge detection in medical images has generated significant interest in the medical informatics community, especially in recent years. With the advent of imaging technology in biomedical and clinical domains, the growth in medical digital images has exceeded our capacity to analyze and store them for efficient representation and retrieval, especially for data mining applications. Medical decision support applications frequently demand the ability to identify and locate sharp discontinuities in an image for feature extraction and interpretation of image content, which can then be exploited for decision support analysis. However, due to the inherent high dimensional nature of the image content and the presence of ill-defined edges, edge detection using classical procedures is difficult, if not impossible, for sensitive and specific medical informatics-based discovery. In this paper, we propose a new edge detection technique based on the regional recursive hierarchical decomposition using quadtree and post-filtration of edges using a finite difference operator. We show that in medical images of common origin, focal and/or penumbral blurred edges can be characterized by an estimable intensity gradient. This gradient can further be used for dismissing false alarms. A detailed validation and comparison with related works on diabetic retinopathy images and CT scan images show that the proposed approach is efficient and accurate.

Keywords: Edge detection, quad trees, medical image mining, retinal image analysis.