Segmentation of Lung Images using Region Based Neural Networks
Z. Faizal Khan  

Department of Computer Science, College of Computing and Information Technology, Shaqra University, Kingdom of Saudi Arabia.

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Abstract: In this article, a neural network-based segmentation approach for CT lung images was proposed using the combination of Neural Networks and region growing which combines the regions of different pixels. The proposed approach expresses a method for segmenting the lung region from lung Computer Tomography (CT) images. This method is proposed to obtain an optimal segmented region. The first step begins by the process of finding the area which represents the lung region. In order to achieve this, the regions of all the pixel present in the entire image is grown. Second step is, the grown region values are given as input to the Echo state neural networks in order to obtain the segmented lung region. The proposed algorithm is trained and tested for 1,361 CT lung slices for the process of evaluating segmentation accuracy. An average of 98.50% is obtained as the segmentation accuracy for the input lung CT images.

Keywords: Computed Tomography (CT); Image Segmentation; Lung Images; Neural Networks

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