A Computer-Assisted System for Cell Detection of Anti-Nuclear Antibody Immunofluorescence Images

• Main Authors: Chung-Chuan Cheng, 鄭中川
• Other Authors: 廖俊睿
• Format: Others
• Language: en_US
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/46185472316759191468

博士 === 國立中興大學 === 電機工程學系所 === 104 === Autoimmune disease is a disorder of immune system due to the over-reaction of lymphocytes against one’s own body tissues. Anti-Nuclear Antibody (ANA) is an autoantibody produced by the immune system directed against the self-body tissues or cells, which plays an important role in the diagnosis of autoimmune diseases. Indirect Immunofluorescence (IIF) method with HEp-2 cells provides the major screening method to detect ANA for the diagnosis of autoimmune diseases. Fluorescence patterns at present are usually examined laboriously by experienced physicians through manually inspecting the slides with the help of a microscope, which usually suffers from inter-observer variability that limits its reproducibility. Previous researches only provided simple segmentation methods and criterions for cell segmentation and recognition, but a fully automatic framework for the detection and recognition of HEp-2 cells had never been reported before. This thesis proposes a method based on the watershed algorithm to automatically detect the HEp-2 cells with different patterns, and design a computer-assisted system to automatically recognize cell patterns of IIF images for the diagnosis of autoimmune diseases in the clinical setting. The experimental results show that the segmentation performance of the proposed method is satisfactory when evaluated with percent volume overlap (PVO: 89%). The classification performance using a Support Vector Machine (SVM) classifier designed based on the features calculated from the segmented cells achieves an average accuracy of 96.90%. The computer-assisted system simulates the functions of modern flow cytometer and provides the diagnostic reports generated by the system to the technicians and physicians through the radar graphs, box-plots, and tables. The time needed for the whole procedure is less than 30 min, which is more efficient than the manual operation of the physician after inspecting the ANA IIF images. Besides, the system can be easily deployed on many desktop and laptop computers. In conclusion, the designed system, containing functions for automatic detection of ANA cell pattern and generation of diagnostic report, is effective and efficient to assist physicians to diagnose patients with autoimmune diseases.