ANA免疫螢光顯影影像分類之電腦輔助系統建構

Development of Computer-Aided System for Classifying ANA Immunofluorescence Images

葉進儀(Jinn-Yi Yeh) ； 謝祖怡(Tsu-Yi Hsien) ； 黃嘉政(Jia-Jheng Huang) ； 楊雅琇(Ya-Soiu Yang)

《醫療資訊雜誌》 19卷2期 (2010/06) Pp. 39-60

https://doi.org/10.29829/TJTAMI.201006.0003

摘要

HEp-2 cells are used for the identification of antinuclear autoantibodies (ANA). They allow for recognition of over 30 different nuclear and cytoplasmic patterns, which are given by upwards of 100 different autoantibodies. Therefore, indirect immunofluorescence with HEp-2 cells presents the major screening method for detection of autoantibodies in systemic autoimmune disease. However, this method requires highly specialized technicians and is also time consuming. This research applies image processing and data mining techniques to analyze the immunofluorescence images of HEp-2 cell. These techniques include the Canny edge detection method for segment a cell, a co-occurrence matrix for texture analysis, and a neural network for classification. The goal of this research is to find out relevant features and construct out a computer-aided diagnosis system to distinguish immunofluorescence images. Experimental results show that the error rate of this approach is 15.62% better than the one of human expert 23.6%. The Kappa value is about 0.75 which is greater than 0.4. This implies that this approach can help clinical doctors to diagnose diseases more efficiently with the computer-aided diagnosis system.

關鍵字

HEp-2細胞 ； 免疫螢光顯影 ； 邊界檢測 ； 紋理分析

並列摘要

HEp-2 cells are used for the identification of antinuclear autoantibodies (ANA). They allow for recognition of over 30 different nuclear and cytoplasmic patterns, which are given by upwards of 100 different autoantibodies. Therefore, indirect immunofluorescence with HEp-2 cells presents the major screening method for detection of autoantibodies in systemic autoimmune disease. However, this method requires highly specialized technicians and is also time consuming. This research applies image processing and data mining techniques to analyze the immunofluorescence images of HEp-2 cell. These techniques include the Canny edge detection method for segment a cell, a co-occurrence matrix for texture analysis, and a neural network for classification. The goal of this research is to find out relevant features and construct out a computer-aided diagnosis system to distinguish immunofluorescence images. Experimental results show that the error rate of this approach is 15.62% better than the one of human expert 23.6%. The Kappa value is about 0.75 which is greater than 0.4. This implies that this approach can help clinical doctors to diagnose diseases more efficiently with the computer-aided diagnosis system.

並列關鍵字

HEp-2 Cell ； Immunofluorescence ； Edge Detection ； Texture Anal