Increasing of Edges Recognition in Cardiac Scintography for Ischemic Patients

Abstract

The detection of ischemia heart disease was usually scored by a trained nuclear medicine Physician by determining the ischemia location and size subjectively (by eyes). This subjective method will add to the 5% tolerance error, which might compromise the whole process of treatment especially in patients with severe conditions. The aim of this study is to increase the edge recognition in cardiac scintography images in patients with ischemic heart disease using L*a*b* color space and K-means clustering. First, we read the nuclear cardiac images. We then to convert the images form RGB color space to L*a*b* color space. Then we classify the colors in 'a*b*' space using K-means clustering. Then we label every pixel in the Image using the results from K-means. We then create images that segment the cardiac image by colour. Finally, we segment the cardiac image into a separate image. The sample of this study was (146 cases) and they showed increase enhancement. This segmentation technique (automatic scoring) and segmented images was adjudicated by three nuclear medicine physician as being comparable to other segmentation techniques created with manual editing (subjective scoring). This technique showed potentials increasing of detection of the myocardial ischemia rather than conventional one.

Research in Medical Imaging Using Image Processing Techniques

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