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Enhancing Visual Diagnosis of X-Ray Images by the Method of Selective Dilation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 71 - Number 2 |
| Year of Publication: 2013 |
| Authors: Sachdeep. S, Monica Subashini. M |
10.5120/12328-8567
|
Sachdeep. S, Monica Subashini. M . Enhancing Visual Diagnosis of X-Ray Images by the Method of Selective Dilation. International Journal of Computer Applications. 71, 2 ( June 2013), 5-10. DOI=10.5120/12328-8567
Abstract
This paper presents a basic design and experimental results of a crack detection method on X-Ray images. The proposed method utilizes a customized algorithm wherein a section of the X- Ray image suspected to contain the irregularity, like a crack in the bone, is separated from the rest of the X-Ray image and the cropped image is altered so as to make the presence of irregularities more easily detectable. This primarily involves the detection and expansion of the irregularity in terms of size, or the number of pixels it occupies in the image. The algorithm helps isolate the irregularity and selectively dilate it, without interfering with the other parts of the image, thereby making it more easily visible to the human eye. This operation is accomplished by a Matlab supported operation called dilation. In a grayscale X-Ray image, the pixels constituting the image may take several intensity values. This obscures the distinction between boundaries, impeding visual diagnosis. This problem is avoided by converting the grayscale image to a binary image, creating a clear distinction between boundaries. This paper investigates the possibility of employing this approach to provide detection and selective amplification of irregularities given the region in the image in which the irregularity is suspected to lie. Then the processed image is compared with the original input image. The application of the proposed method for enhancing visual diagnosis is demonstrated by examples.
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