Segmentation And Recognition Of Lung Nodules In CT Images Based On SVM

The segmentation and recognition of lung nodules in computed tomography (CT)images based on the support vector machine is the core of the computer-aideddiagnosis (CAD).The CAD can provide concise and accurate information andimprove the efficiency of medical diagnosis. The accurate image segmentationmethod, the reasonable image feature extraction and the powerful generalizationability of the machine learning are the key of the computer-aided diagnosis. The mainpoint of the segmentation and recognition of images is introduced as follows:1. In the segmentation and extraction of lung nodules aspects, the characteristicsanalysis of lung nodules section is the key in research. According to the features of theimage and the lung nodules, the highly targeted methods are used to extract thesolitary lung nodules and the adhesion lung wall of lung nodules respectively.2. In the tumor recognition aspects, the research focuses on how to reasonablychoose the appropriate image features. By the statistical theory, the identification andclassification system which expected risky is as small as possible is designed.For the above problems, the main research is as follows:1. According to the characteristics of the computer tomography image and lungnodules images, a method of image segmentation based on the improved rolling ballis used to extract the adhesion lung wall of lung nodules region; the region growingmethod is used to extract the independent nodules region in the lung area. Theextraction results could be a study sample database for the follow-up identification.The experimental results show that the improved rolling ball method can extractnodules which adhesive the wall of lung and the methods based on region growingcan extract the independent nodules.2. The recognition method for CT images of lung nodules combined the earlyregion of interest (ROI) extraction based on the rule with the least square supportvector machine (LSSVM) is proposed. By the analysis of the characteristics of theextraction nodules and the prior knowledge of the lung nodules, the sample extractionmethod based on the specific rules is used, and the certain number of the non-nodulesregion of interest is excluded. Then the method based on the least square supportvector machine is used for recognition to achieve the mapping which is from lowdimension space to high dimension space. So the problems of the local optimalsolutions, the complex high-dimensional operation, the poor generalization ability andthe over learning caused by the artificial neural network are avoided effectively. Theexperimental results show that the least squares support vector machine could reducethe false positives after the reasonable selection of the image characters when thefalse negatives are increased in a small amount.