DocumentCode :
2059290
Title :
A genetic algorithm-neural network approach for Mycobacterium tuberculosis detection in Ziehl-Neelsen stained tissue slide images
Author :
Osman, M.K. ; Ahmad, F. ; Saad, Z. ; Mashor, M.Y. ; Jaafar, H.
Author_Institution :
Fac. of Electr. Eng., Univ. Teknol. MARA (UiTM) Malaysia, Shah Alam, Malaysia
fYear :
2010
fDate :
Nov. 29 2010-Dec. 1 2010
Firstpage :
1229
Lastpage :
1234
Abstract :
This paper describes a method using image processing and genetic algorithm-neural network (GA-NN) for automated Mycobacterium tuberculosis detection in tissues. The proposed method can be used to assist pathologists in tuberculosis (TB) diagnosis from tissue sections and replace the conventional manual screening process, which is time-consuming and labour-intensive. The approach consists of image segmentation, feature extraction and identification. It uses Ziehl-Neelsen stained tissue slides images which are acquired using a digital camera attached to a light microscope for diagnosis. To separate the tubercle bacilli from its background, moving k-mean clustering that uses C-Y colour information is applied. Then, seven Hu´s moment invariants are extracted as features to represent the bacilli. Finally, based on the input features, a GA-NN approach is used to classify into two classes: `true TB´ and `possible TB´. In this study, genetic algorithm (GA) is applied to select significant input features for neural network (NN). Experimental results demonstrated that the GA-NN approach able to produce better performance with fewer input features compared to the standard NN approach.
Keywords :
diseases; feature extraction; genetic algorithms; image segmentation; medical image processing; neural nets; patient diagnosis; pattern clustering; Hu moment invariants; Mycobacterium tuberculosis detection; Ziehl-Neelsen stained tissue slide images; feature extraction; feature identification; genetic algorithm; image processing; image segmentation; k-mean clustering; neural network; tuberculosis diagnosis; ZN-stained tissue; genetic algorithm; mycobacterium tuberculosis detection; neural network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on
Conference_Location :
Cairo
Print_ISBN :
978-1-4244-8134-7
Type :
conf
DOI :
10.1109/ISDA.2010.5687018
Filename :
5687018
Link To Document :
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