Increasing Visual Perception Brain Stroke Detection System

Stroke has been the third among the top ten leading causes of death in Taiwan. The danger is not only high mortality, but also gave the family a heavy economic. Recently, inspects the ischemic stroke the method to be divided into two categories: Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). The former CT is the most regularly adoption because done once the time of the latter MRI is longer than the time of the former CT, and the latter MRI is the most expensive than the former CT. Due to academic studies of ischemic stroke detection is very finite, it is low successful rate to correctly detect stroke initial period. In tradition, we have to depend on radiologist´s professional knowledge to diagnose ischemic stroke image, it will make radiologists increase fatigue so that made an error diagnostic. Therefore, we propose a Increasing visual perception brain stroke detection system. We use mathematic morphology to extract brain area. Then using median filter to remove noise, and uses canny edge detection to find out the edge of the brain tissue, and setting peak value in edge histogram as seed to perform region growing. Finally, we can clearly recognize the area of stroke. In the experimental result the recognition successful rate can reach 85%. Our successful rate cannot reach 100% because each patient´s brain CT image is not certainly upright and foursquare. Future research direction, we will use other methods to correct gradient and CT image for each patient to improve successful rate of detection.