Abstract
In medical imaging, Computer-Aided Detection (CAD) aims to improve diagnostic decision, detection performance and nodule detection. Computed Tomography (CT) technology allows us to gain isotropic acquisition of complete chest within a single breath hold. Analysis of data becomes time consuming for manual interpretation. Hence, automation of data of the CT images is necessary. Lesions in lung are potential manifestations of lung cancer, and early detection helps to increase the chance of survival rate. This paper focuses the literature on computer analysis of abnormal pulmonary CT. All these work deals with three main steps: pre-processing, segmentation of nodule candidates and nodule classification. In addition, the challenges, limitations and future directions are discussed.
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