Feature Extraction Of Lung Nodule And Method Study On CT Images

Lung cancer is the biggest disease threatening human health and life at present. The CT scan has become the main means of lung cancer early detection and diagnosis. The nodules detection and benign and malignant recognition based on CT images have also become the international hotspot. The pulmonary nodules of feature extraction became the main factors of benign and malignant recognition. Leaves symptoms and Burr symptoms is the most important features.We puts forward a new method of identifying the leaves symptoms and burr symptoms, the method uses the vector fork deposition and least-square method to deal with the noise. It can accurately find out the convex boundary point. Because the calculate angle is not needed, time complexity and algorithm thought also are considerable and can effectively identify the leaves symptoms. In aspect of extracting burr features, we use RBST algorithm that puts the polar coordinates into Cartesian coordinates. Through effectively interpolation, we get the gray value around blur area and make the part of grey value into the form of rectangular graph. We analyze the tile figure. We can get the burr levy value through average histogram method and the Fourier spectral analysis method which can discriminate the burrs feature of nodules well.The system use the test cases provided by 2nd affiliated hospital of Suzhou university. After testing, we compare the experimental results with the case report the doctor provides,we find the correctness and false-positive rate is higher. We think that can effectively extract the symptoms of leaves and burr .Also, the performance also have been improved greatly. Thus it helps doctors improve the recognition rate of benign and malignant pulmonary nodules.