Development of an algorithm for Detecting Symptom level in patients with Scleroderma

In this study, locality of scleroderma was detected. Diagnostic method is difficult for scleroderma (skin curing; Scleroderma), and it is done by comparing the images of the normal subjects to the scleroderma patients, after performing monochrome processing. The saturation, brightness, and contrast are adjusted, and they were converted by using the process of Well Filter. As a result, the images were able to be used to clearly distinguish the symptoms of scleroderma. In addition, in a video of a healthy person, the line of sight of the observation given the image of scleroderma patients above sea level of height as $0^{\circ}$ is to implement the closing process to the rear Well Filter even only in so that the horizontal plane, and out at intervals of graph the amplitude difference of the video have I asked. The diagnostic criteria were determined for the healthy subjects and the scleroderma patients.

본 연구에서는 피부 경화증 환자의 증상정도 알고리즘을 개발하였다. 진단 방법은 피부경화증을 흑백처리 한 후 정상인의 이미지와 비교하였다. 채도, 밝기 및 콘트라스트 조정의 필터를 프로세스를 통해 변환 하였다. 그 결과 화상이 선명한 경화증의 증상을 구별하는데 사용될 수 있었다. 건강한 사람의 영상에서 경화증 환자의 이미지를 주고 폐쇄 프로세스를 적용하여 진폭의 차이로 정상인과 피부 경화증 환자를 결정하였다.