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Segmentation of lung region is specifically difficult because of the large variation in quality of an image. Accurate identification of lung region is very important for clinical applications. The aim of this paper is to make segmentation of lung region accurately. In this paper, lung mask is calculated by taking the average of manual masks of most similar training images, and graph cut segmentation is applied with kernel energy to extract accurate boundaries. Kernel function is used to transform image data into higher dimension data in order to make piecewise constant model of graph cut formulation that becomes applicable then. Minimization of energy function contains graph cut iterations of image partitioning and iterations of updating the region parameters. Performance is evaluated by Dice coefficient. Dice coefficient is used to measure the similarity between manual and segmented mask. Experimental results of proposed methodology state the accuracy of 92.19%. It is observed that all the cases obtain the score more than 0.88 which is sufficient to detect lung region efficiently.
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- Segmentation of Chest Radiographs for Tuberculosis Screening Using Kernel Mapping and Graph Cuts
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