CAJ | 학술논문

目的基于激光共聚焦扫描显微镜皮肤图像,研发一种能够准确、有效地识别在体黑色素瘤的计算机辅助医学诊断方法.资料与方法通过小波分析法,提取40例黑色素瘤和40例常见良性痣患者激光共聚焦扫描显微镜图像的纹理特征,基于小波系数的标准差、能量以及熵值特征参数,采用分类与回归树算法对图像进行自动分类.结果该算法对对良性痣正确分类率达92.50%.结论该计算机辅助诊断方法不但提高了恶性黑色素瘤早期诊断的准确度,还降低了良性痣的误诊率,为临床早期发现和诊断黑色素瘤提供了客观依据.
목적기우격광공취초소묘현미경피부도상,연발일충능구준학、유효지식별재체흑색소류적계산궤보조의학진단방법.자료여방법통과소파분석법,제취40례흑색소류화40례상견량성지환자격광공취초소묘현미경도상적문리특정,기우소파계수적표준차、능량이급적치특정삼수,채용분류여회귀수산법대도상진행자동분류.결과해산법대대량성지정학분류솔체92.50%.결론해계산궤보조진단방법불단제고료악성흑색소류조기진단적준학도,환강저료량성지적오진솔,위림상조기발현화진단흑색소류제공료객관의거.
Purpose To develop an accurate, effective melanoma computer-aided diagnosis algorithms in vivo based on laser scanning confocal microscope images. Materials and Methods Forty patients with melanoma and forty patients with benign nevus were performed with laser scanning confocal microscope imaging, and the texture features were extracted using wavelet analysis. The standard deviation, energy and entropy characteristic parameter were applied with classification and regression tree to automatic classification based on wavelet coefficient. Results The algorithm showed that 95.00%of melanoma and 92.50% of benign nevus could be correctly classified. Conclusion This computer-aided diagnosis algorithms can improve the accuracy of melanoma diagnosis, and also can decrease the misdiagnostic rate of benign nevus, which provides an objective evidence for the early detection and diagnosis of melanoma.