Detection of Lung Nodules using Image Processing Techniques
International Journal of Computer Applications
Lung cancer is characterized by uncontrolled cell growth in tissues of the lung. Recently, image processing techniques are widely used in several medical areas for image improvement in earlier detection and treatment stages, where the time factor is very important to discover the abnormality issues in target images, especially in various cancer tumors such as lung cancer, breast cancer, etc. This paper aims to develop an efficient lung nodule detection system by performing nodule segmentation
... rough thresholding and morphological operations. Thresholding is one of the most powerful tools for image segmentation. The segmented image obtained from thresholding has the advantages of smaller storage space, fast processing speed and ease in manipulation, compared with the gray level image which usually contains 256 levels. Image Segmentation using the watershed transforms works well if we can identify or "mark" foreground objects and background locations, to find "catchment basins" and "watershed ridge lines" in an image by treating it as a surface where light pixels are high and dark pixels are low. Morphological operations apply a structuring element to an input image, creating an output image of the same size. By choosing the size and shape of the neighborhood, it can possible to construct a morphological operation that is sensitive to specific shapes in the input image. Morphological operation has been widely used to produce binary and grayscale images, with morphological techniques being applied to noise reduction, image enhancement, and feature detection. The proposed method has two stages: lung region segmentation through thresholding and then segmenting the lung nodules through thresholding and morphological operations.