An automatic segmentation and classification framework for anti-nuclear antibody images

Abstract

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 segmentation and recognition of HEp-2 cells had never been reported before. This study proposes a method based on the watershed algorithm to automatically detect the HEp-2 cells with different patterns. 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 SVM classifier designed based on the features calculated from the segmented cells achieves an average accuracy of 96.90%, which outperforms other methods presented in previous studies. The proposed method can be used to develop a computer-aided system to assist the physicians in the diagnosis of auto-immune diseases.

Design of a Computer-Assisted System to Automatically Detect Cell Types Using ANA IIF Images for the Diagnosis of Autoimmune Diseases

“…In this study, we designed a computer-assisted system for the diagnosis of autoimmune diseases based on the method proposed previously [12] by analyzing the IIF cell images of patients to generate their corresponding patientbased reports to facilitate disease diagnosis. The system provides a GUI for the physician to review the IIF image and the information of each detected cell.…”

“…The images classified as one of the two groups are segmented using different methods, i.e., Cell Detection 1 and Cell Detection 2 [12]. The detected regions of the first group were extracted according to the cell contours obtained from the general watershed segmentation [17] and marker-controlled watershed segmentation [18].…”

“…Please refer to [12] for detailed description of the parameters used for designing the cell detector.…”

“…e FS, and f DS central areas (1 feature) [12], and statistics of light and dark regions inside the blobs (6 features) [12].…”

“…In a previous study, we proposed a method based on the watershed algorithm to automatically detect the HEp-2 cells with different patterns [12]. It was shown that the segmentation performance of the proposed method achieved 89 % when evaluated based on percent volume overlap (PVO) rate [13,14].…”