화질의 국소적 변화를 고려한 의용화상처리

Medical Image Processing with Local Variati on of the Image Quality

잡음을 포함한 저화질의용화상의 배경과 목적대상정을 분할하는 환경영역은 중요한 정보로 의학상 진단에 큰 의의를 갖고 있다. 이 논문의 목적은 화상의 농도변화를 정총화하여 통계적수법에 의해 환경영역을 결정하는 threshold를 구하는 방법을 제시하고 이를 비 scintigram에 적용하여 실험을 행했다. 전화상을 64개의 소영역으로 나누고 경계영역이 존재하는 부분온 선택하며 이 부분에 maximum likelihood법을 적용하여 threshold를 결정한뒤 내삽법에 의해 전화소에 대한 threshold를 구하고 수곽을 포함한 2식화면을 구했다. 이의 결과는 인간의 인식과 거의 같은 결과로 동적해석방법의 유효성이 증명되었다. The boundary has been one of the most important information in radiographic images and the degrees of difficulty involved varies greatly with the quality of the picture. These Buantifications are the means to diagnoses. The purpose of this paper is to quantify intensity variation and the threshold decision which is based on statistical principles and is developed to detect limits in liver scintigrams the entire picture is devide4 into 64 small regions. The kurtosis and variances for each smal region are used as indications to select the histograms the thresholds are computed according to the method o(maximum likelihood which minimizes the probability o( misclassification. Therefore Ive have demonstrated the applicability of the boundary detection and proved good agreement with human recognition, and we can use it for the diagnosis data of liver disease.

The boundary has been one of the most important information in radiographic images and the degrees of difficulty involved varies greatly with the quality of the picture. These Buantifications are the means to diagnoses. The purpose of this paper is to quantify intensity variation and the threshold decision which is based on statistical principles and is developed to detect limits in liver scintigrams the entire picture is devide4 into 64 small regions. The kurtosis and variances for each smal region are used as indications to select the histograms the thresholds are computed according to the method o(maximum likelihood which minimizes the probability o( misclassification. Therefore Ive have demonstrated the applicability of the boundary detection and proved good agreement with human recognition, and we can use it for the diagnosis data of liver disease.