Here we are identifying lung fissure and lung lobes. The lung lobes and nodules in CT image are segmented using two stage approaches such as modified adaptive fissure sweep and adaptive thresholding. Initially pre-processing is used to remove the noise present in CT image using filter, then the fissure regions are located using adaptive fissure sweep technique. Lung Nodules are segmented using thresholding. In order to identify the actual fissure locations and curvatures from the fissure regions using discrete wavelet transform. Fissure regions are identified using fissure sweeping technique and then the locations are identified using discrete wavelet transform. Lung lobes are segmented in clinical CT image using adaptive fissure sweeping for finding the fissure region and using watershed transform to enhance the fissure. Pre-processing is done to remove the noise present in the CT image. In order to remove the Gaussian noise, Wiener filter is used. Then the vessels are enhanced using the morphological operators followed by lung segmentation. The fissure regions are identified, enhanced and verified. Lung nodules are segmented using adaptive threshold. Through the project we have developed a segmentation algorithm for identifying the fissures and nodules from CT images. The results indicate a potential for developing an automatic algorithm to segment lung lobes for surgical planning of treating lung disease.