A computer-aided diagnosis system for lung nodule detection in pulmonary CT images based on LS-SVM and wavelet transformation

One of the most important areas of biomedical engineering is medical imaging. Fully automated schemes are currently being explored as Computer-Aided Diagnosis (CAD) systems to provide a second opinion to medical professionals; of these systems, abnormal region detector in medical images is one of the most critical CAD systems in development. The primary motivation in using these systems is due to the fact that reading an enormous number of images is a time-consuming task for the radiologist. This task can be sped up by using a CAD system which highlights abnormal regions of interest. Low false positive rates and high sensitivity are essential requirement[s] of such a system. The initial requirement of processing any organ is an accurate segmentation of the target of interest in the images. A segmentation method based on the wavelet transformation is proposed which accurately extracts lung regions in the thoracic CT images. After this step, an Aritifical Intelligence system, known as Least Squares Support Vector Machine (LS-SVM), is employed to classify nodules within the regions of interest. It is a well known fact that the lung nodules, except the pleural nodules, are mostly spherical structures whereas other structures including blood vessels are shaped as other structures such as tubular. Therfore, an enhancment filter is developed in which spherical structures are accentuated. Processing three different real databases revealed that the proposed system has reached the objective of a CAD system to provide reliable opinion for the doctors in the diagnosis fashion.