Title :
Adaptive Automatic Segmentation of HEp-2 Cells in Indirect Immunofluorescence Images
Author :
Huang, Yu-Len ; Jao, Yu-Lang ; Hsieh, Tsu-Yi ; Chung, Chia-Wei
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Tunghai Univ., Taichung
Abstract :
Indirect immunofluorescence (IIF) with HEp-2 cells is used for the detection of antinuclear autoantibodies (ANA) in systemic autoimmune diseases. An automatic inspection system for ANA testing can be divided into HEp-2 cell detection, fluorescence pattern classification and computer aided diagnosis phases. This study focused on the first phase of cell detecting and locating. This study presented an adaptive edged- based segmentation method for automatically detecting outlines of fluorescence cells in IIF images. The proposed method evaluated 2573 cells with six distinct fluorescence patterns from 45 images. The results of computer simulations revealed that the proposed method always identified cell outlines as were obtained by manual sketched. Such a method provides robust and fast automatic segmentation of HEp-2 fluorescent patterns in ANA testing.
Keywords :
diseases; image classification; image segmentation; medical image processing; patient diagnosis; HEp-2 cells; adaptive automatic segmentation; antinuclear autoantibodies; autoimmune diseases; automatic inspection system; computer aided diagnosis phases; fluorescence pattern classification; indirect immunofluorescence images; Automatic testing; Computer simulation; Diseases; Fluorescence; Image edge detection; Image segmentation; Inspection; Pattern classification; Phase detection; System testing;
Conference_Titel :
Sensor Networks, Ubiquitous and Trustworthy Computing, 2008. SUTC '08. IEEE International Conference on
Conference_Location :
Taichung
Print_ISBN :
978-0-7695-3158-8
Electronic_ISBN :
978-0-7695-3158-8
DOI :
10.1109/SUTC.2008.73
