Automatic Detection and Classification of Solitary Pulmonary Nodules from Lung CT Images

Abstract

Cancer is one of the fatal diseases, posing threat to human life. Among different types of cancer, lung cancer can be considered as one of the most most deadly one. Lung nodules are small white spots that appear in lung parenchyma. Lung nodules are primarily of two types, solitary pulmonary nodules and juxtrapleural nodules. Solitary pulmonary nodules are round in shape, whereas juxtapleural nodules have a worm like shape, which are generally introduced through metathesis from the other cancerous organ of the human body. All lung nodules are not cancerous. Each of them has certain geometric features, upon which the nodules can be classified into cancerous and non-cancerous. Chest radiographs and computed tomography (CT) scan images are important for the purpose of diagnosis of the lung cancer. Manual detection of cancerous lung nodule is a very challenging task as it is time consuming and prone to human error. An automated detection is therefore needed for faster detection of malignant and benign lung nodules based on the shape features of the nodules. This automated detection will also help to reduce the cost of diagnosis by selectively choosing only the malignant nodule for biopsy tissue culture discarding the benign lung nodule. In this paper, we have proposed a novel method that detects and categorizes solitary pulmonary nodules responsible for lung cancer from lung CT images. Our method reduces variability in detection by automatic segmentation and classification of nodules. The experimental results are promising in respect to classification of lung nodules as malignant or benign.