Penerapan metode segmentasi pada analisis citra digital Head CT Scan

Abstract

Image segmentation is an important research area in digital image processing with several applications in vision-guided autonomous robotics, product quality inspection, medical diagnosis, the analysis of remotely sensed images, etc. The aim of image segmentation can be defined as partitioning an image into homogeneous regions in terms of the features of pixels extracted from the image. Edge detection and thresholding are simple segmentation in images and the focuses in our research. This research used k-mean clustering method as the main tool. The image data chosen were normal diagnosed head CT Scan photos, and those which have indication of damages of brain effect of lacunar infark and hemorrhage. Feature extraction utilized a curve fitting procedure for the computed autocorrelation of each image segment. The results show a promising guide for further diagnosis steps.