Region quad-tree decomposition based edge detection for medical images

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; medical image mining; quad trees; retinal image analysis..

Fig. (1)

The coverage of a quad tree.

Fig. (2)

Partitioning a node for recursive definition. Leftmost square: height = i+1. Rightmost square: Height = i.

Fig. (3)

Sample CT scan of the human head.

Fig. (4)

Quadtree decomposed image with 1x1 and 2x2 blocks preserved.

Fig. (5)

Block diagram demonstrating the various phases of the proposed algorithm.

Fig. (6)

Final edge map of the image given in Fig. (3).

Fig. (7)

(a) Sample image taken from Peripheral Vascular Surgical and Society, (b) Edge map obtained with threshold of 0.1.

Fig. (8)

(a) Sample CT scan for sinus, (b) Edge map obtained with threshold of 0.09.

Fig. (9)

(a) Sample CT scan of head, (b) Edge map obtained with threshold of 0.07.

Fig. (10)

(a) CT scan of lungs, (b) Edge map obtained with threshold of 0.09.

Fig. (11)

(a) Sample CT scan image, (b) Edge map obtained with threshold of 0.06.

Fig. (12)

Graph comparing the execution time taken by Canny approach with the proposed algorithm.

Fig. (13)

Performance evaluation of the proposed quad_edge_detection algorithm on four independent Retinal scan images.