Automated segmentation of lung images using textural echo state neural networks

A novel method of Lung image segmentation based on Textural Echo State Neural Network is proposed in this paper. This work combines the textural features of different pixels in the lung images in order to increase the performance of region of interest segmentation. For segmenting lung images, a two-step process is done. First, an unsupervised border detection is done in order to separate the foreground lung images from the background. This process begins by identifying the textural features that represent the lung border clearly by the process of Texture analysis. In the second step, the obtained features are further given as input towards the Echo state neural networks as training pattern in order to obtain the segmented lung region. The proposed algorithm is trained and tested for 450 skin lesion images in order to evaluate its accuracy. An average accuracy of 97.8% for segmenting lung images has been obtained.