Abstract
Segmentation of pulmonary X-ray computed tomography (CT) images is a precursor to most pulmonary image analysis applications. Digital Image Processing is currently a hot research area in medicine and it is believed that they will receive extensive application to biomedical systems in the next few years. In Digital Image Processing, neural networks are ideal in recognizing diseases using scans since there is no need to provide a specific algorithm on how to identify the disease. This paper describes an algorithm to separate the lung tissue from a Chest CT to reduce the amount of data that needs to be analyzed. Our goal is to have a fully automatic algorithm for segmenting the lung tissue, and to separate the two lung sides as well. Fuzzy c-Means clustering is used to segment the lungs. Cleaning is performed to remove air, noise and airways. Finally, a sequence of morphological operations is used to smooth the irregular boundary. The database used for evaluation is taken from a radiology-teaching file. Our current evaluation shows that the applied segmentation algorithm works on a large number of different cases. The textural features were extracted from the segmented lungs and it was given as input to CFBP. The neural networks are used to identify the various lung diseases.
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