"Original image has low contrast and further image processing techniques may end up withundesired results, so an initial step of contrast enhancement seems to be necessary. The result of thisstep is an enhanced image in which the lung area is clearly distinct from background. So if we do notenhance the image, the accurate segmentation cannot be obtained.Segmentation is done in two steps. At first, an initial segmented image is estimated, and thenthe accurate segmentation is performed with morphological operations and thresholding.We use spatial Gabor filter based on vertical orientation to approximate ribs’ location. Thenwe apply subtraction method.The overall illumination of images in the database is varying in a wide range.We usedadaptive histogram equalization to normalize their histograms. This step normalizes contrast acrossdifferent images. Many image analysis applications e.g. object recognition, tracking, etc., require spatiallylocalized features. Gabor filters are a popular tool for this task of extracting spatially localized spectralfeatures.A spatial Gabor filter is obtained by modulating a sinusoid with a Gaussian. For the case ofone-dimensional (1D) signals, a 1D sinusoid is modulated with a Gaussian. This filter will thereforerespond to some frequency, but only in a localized part of the signal. For 2D signals such as images,consider the sinusoid. By combining this with a Gaussian, we obtain a Gabor filter.For obtaining the ASM-based contours a publicly available reference image database has beenused. We divided the image set to two parts. One set was used for training and the other for testingthe algorithm. Here we used test images for training purpose.A morphological open operation for signal enhancement and a ring-shaped median filter forsignal suppression are applied separately and then obtained results are subtracted to make anenhanced version of image.Then we apply subtraction method for rib free image. Simulated output: "