PLEURAL PLAQUE DETECTION USING HYBRID SEGMENTATION ALGORITHM AND ELM CLASSIFICATION

Pleural plaques are by far the most common indication of significant exposure to asbestos and act as a biomarker for the diagnosis of
lung cancer in later stages. Plaques can develop on both layers of the pleura, a thin membrane that surrounds the lungs and aids in
breathing. They most commonly develop on the parietal pleura, which lines the inside of the rib cage, but can also affect the visceral
pleura, which lines the lungs. For lung image segmentation, many clustering and threshold techniques have been proposed. Here
initially the image is pre-processed using anisotropic diffusion filter. Then pleural plaque is detected and segmented using region
growing approach followed by layer refinement using active contour model (ACM). Finally the segmented image is classified so as to
yield normal and abnormal set using Extreme Learning Machine (ELM). Segmentation is carried using chest CT images to determine
the efficiency of proposed technique