IJPAM: Volume 67, No. 3 (2011)


John Schmeelk
Virginia Commonwealth University Qatar
P.O. Box 8095, Doha, QATAR
e-mail: [email protected]

Abstract.Image edge detection is an integral component of image processing to enhance the clarity of edges and the type of edges. Issues regarding edge techniques were introduced in my 2008 paper on transforms, filters, and edge detectors, see [15]. The current paper provides a deeper analysis regarding image edge detection using matrices; partial derivatives; convolutions; and the software, MATLAB 7.9.0, and MATLAB Image Processing Toolbox 6.4. Edge detection has applications in all areas of research, including medical research, see [6], [13]. For example, a patient can be diagnosed as having prostate cancer by studying the edges of the cells (see Figure 1). One can study a magnetic resonance (MR) brain image to indicate the edge functional, as illustrated in Russ, see [13] and Figure 2. Additionally, a patient can be diagnosed with an aneurysm by studying an angiogram (see Figure 3). The physician can study the angiogram, an image of the view of the problematic blood vessels, and determine the diameter of the increased size. The previous paper (see [15]) studied letters using vertical, horizontal, and Sobel transforms. This paper will study images to include the letter $O$ and two images, those of Cameraman and Rice, included in the library of the Image Processing Toolbox 6.4. We then compare the techniques implemented in the previous paper (see [15]) and the images, letter $O,$ Cameraman, and Rice, using vertical, horizontal, Sobel, and Canny transforms implementing the software, MATLAB 7.9.0, and Image Processing Toolbox 6.4.

Received: January 11, 2011

Source: International Journal of Pure and Applied Mathematics
ISSN: 1311-8080
Year: 2011
Volume: 67
Issue: 3