Computed tomography (CT) technology helps us to acquire high resolution, isotropic images of the lungs in a single breath hold. Analysis of these large volumes of data manually is very time consuming and tedious. Automation of analysis of the CT images is therefore vital in the study of CT images. This paper reviews the literature on computer analysis of the lungs in CT scans addressing segmentation of various lungs anatomical structures and works on detection and quantification of chest abnormalities.

Detection and segmentation of fissures is useful in the clinical interpretation of CT lung images to diagnose the presence of pathologies in the human lungs. A new automated method based on marker-based watershed transformation has been proposed to segment the fissures considering its unique structure as a long connected component. Marker based watershed transformation is applied and morphological operations are employed to specify the internal and external markers. The smaller regions in the resulting image are removed by a novel procedure called Small Segment Removal Algorithm (SSRA) to segment the fissures alone. The performance of the method is validated by experimenting with 6 CT image sets. An expert radiologist observation is used as reference to assess the performance. A promising accuracy of 96.61% is shown with the rms error in the range of 0.877±0.224 mm for the left oblique fissure and 0.803±0.262 mm for the right oblique fissure.

No abstract