Certain Investigations on Methodologies for Screening Lung Cancer Using Low Dose Computed Tomography Images

Abstract

Lung cancer is the leading cause of cancer death. It usually does newlinenot cause any symptoms in the early stage of its evolution. Most of the victims newlinehave been diagnosed in an advanced stage, where the symptoms become newlineprominent, which results in poor curative treatment and high mortality rate. newlineScreening is looking for early signs of lung cancer before a person has any newlinesymptoms. Screening tests are recommended for people with high risk of newlinedeveloping the disease such as long history of smoking, coal miners and longterm newlineexposure to carcinogens. Chest X-ray, Computed Tomography (CT) and newlineSputum cytology have been studied for a long time as the choices for lung newlinecancer screening. Recently, Low Dose Computed Tomography has become the newlinestandard for screening lung cancer which lowers the risk by 20% as compared newlinewith chest X-rays.Lung nodule is an important clinical observation in Computed newlineTomography images. The probability that a nodule can be malignant is about newline40%. Distinguishing between pulmonary vessels and nodule is a challenging newlinetask since they share similar shape and intensity characteristics. The newlinecomplexity of nodule detection process increases when the lung nodules are newlineattached with the blood vessel or near the lung wall. Computerized nodule newlinedetection schemes have shown substantial increase in diagnostic accuracy of newlinelung cancer detection.A typical Computer Aided Diagnosis system consists of the newlinefollowing steps: pre-processing, lung parenchyma segmentation, region of newlineinterest detection, feature extraction and nodule classification. The first four newlinesteps deal with image processing and final classification step encounters the newlineproblem of pattern recognition