| Summary: | 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. © 2010 IEEE.
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