Title :
HEp-2 cell classification using multilevel wavelet decomposition
Author :
Katyal, Ranveer ; Kuse, Manohar ; Dash, Subrat Kumar
Author_Institution :
Dept. of Commun. & Comput. Eng., LNM Inst. of Inf. Technol., Jaipur, India
Abstract :
The analysis of anti-nuclear antibodies in HEp-2 cells by Indirect Immunofluorescence (IIF) is considered a powerful, sensitive, and comprehensive test for auto-antibodies analysis for autoimmune diseases. The aim of this study is to explore the use of wavelet texture analysis for automated categorization of auto-antibodies into one of the six categories of immunofluorescent staining. Gray level co-occurrence matrix (GLCM) features were extracted over sub-bands obtained from multi-level wavelet decomposition. In this study, an attempt is also made to investigate effect of different wavelet bases and their superiority on spatial domain features on classification task at hand. A qualitative as well as quantitative comparison is done between GLCM features in wavelet domain and spatial domain. Discrete Meyer wavelet has been found to be the most discriminating for this classification task.
Keywords :
biomedical optical imaging; cellular biophysics; discrete wavelet transforms; diseases; fluorescence; image classification; medical image processing; Discrete Meyer wavelet; GLCM features; HEp-2 cell classification; antinuclear antibody analysis; auto-antibody analysis; autoimmune diseases; automated categorization; gray level cooccurrence matrix features; indirect immunofluorescence; multilevel wavelet decomposition; spatial domain features; wavelet texture analysis; Accuracy; Discrete wavelet transforms; Diseases; Feature extraction; Immune system; Training; Wavelet domain; HEp-2 Cell Classification; Multi-level Wavelet De-composition; Wavelet Texture Representation;
Conference_Titel :
Region 10 Symposium, 2014 IEEE
Conference_Location :
Kuala Lumpur
Print_ISBN :
978-1-4799-2028-0
DOI :
10.1109/TENCONSpring.2014.6863014
