Detection Of Lung Nodules In CT Images Based On 3D SVMs

Computer-aided Diagnosis (CAD) of lung nodules is the intersecting subject of image processing and medicine. Realible results of lung CAD depend on effective image segmentation, extraction and machine learning technology. Lung CAD usually includes two main parts:extraction and accurate recognition of potential nodules. Major focuses of the two parts by current lung CAD schemes can be summarized as follows:(1)In the part of segmentation and extraction, researches focus on repairing the lossed nodules which adhere to normal structure seriously. As gray level of these juxta-nodules is usually similar to the surrounding normal structures, they are hard to be distinguished from normal structures by threshold method and image enhancement; On the other hand, as the complex of lung areas, they are hard to be extracted by mask matching.(2)In the part of accurate recognition, researches focus on removing False Positives (FP) caused by some large vessels and bronchus. Lung nodules usually are defined as sphere with the diameter in a certain range, but cross profiles of some large vessels are also sphere, which would increase FP. In addition, instability, minimization and overfitting of some traditional machine learning methods such as Nerual Network (NN) would affect seriously the performance of recognition.Aiming at above problems, main purpose in the paper is effectively improving the performance of classifiers. It would be achived by analyzing 3D features of potential nodules in 3D space, and 3D Volum of Interest (VOI) of potential nodules in successive sections are chosen as the identify object instead of 2D ROI. The details are as follows:(1)In order to analyze more information in 3D space and effectively remove the adhesions between different structures, a fast Three Dimension Principle Component Analysis (3DPCA) is presented. Firstly, feature points in 3D space made up of a CT set are extracted by 3DPCA; Secondly, region grow method is used to obtain the whole suspected lesions by choosing the feature points as the seed points; Lastly, a fast decomposition algorithm of Higher-Order Singular Value Decomposition (HOSVD) is designed in order to reduce computation of 3DPCA. Recognition accuracy of lung lesions is improved by 3DPCA comparing with traditional 2DPCA, and computation is reduced to 1/3 by fast 3DPCA.(2)In order to repair lossed potential nodules which adhere to normal structure seriously, extraction algorithm based on restrict by successive sections is proposed. Firstly, potential nodules in all sections are extracted respectively by combining of threshold method, image enhancement (by Dot Filter) and rolling ball method; Secondly, let the sections including potential nodules as base sections, locations of potential nodules as base locations and lung area of base sections as restrict conditions; At last, lossed potential nodules are repaired by region grow method. It is proved by experiment that the lossed nodules can be repaired more effectively by above extraction algorithm comparing with rolling ball method, region grow method, Snake and GVF Snake method. And 3D VOI of potential nodules are made for further recognition.(3)In order to improve performance of recognition,3D VOI of potential nodules are chosen as the identification objects instead of traditional 2D ROI. For above reason, SVMs3Dmatrix which can process the input samples based on 3D matrix patterns is proposed.a.Unfolding SVMs3DmatrixFirstly, unfolding method of high-order tensor is used to unfold the input sample based on 3D matrix into three 2D matrixes; Secondly, SVMsmatnx is used to obtain three decisions of the three 2D matrixes; At last, the three people's voting method is used to obtain the final decision.b.Non-unfolding SVMs3DmatrixMultiplication of 3D matrix is used to design non-unfolding SVMs3Dmatrix, which can process input samples based on 3D matrix patterns directly and avoiding unfolding. Firstly, new optimum codition of SVMs is conducted by increasing two right-vectors; Secondly, the left-vector and two right-vectors can be solved iteratively by the gradient descent method.It is proved by experiment that the perfomence of SVMs3Dmatrix is better than that of Two Dimension Linear Discrimination Analysis (2D-LDA), Massitive Training Artificial Neural Network (MTANN) and SVMsmatrix. Furthermore, as avoiding the unfolding, memory occupy of non-unfolding SVMs3Dmatrix is effectively reduced comparing with that of unfolding SVMs3Dmatrix.(4) In order to better meet complex and variety of lung nodules, Multi-class SVMs based on 3D matrix pattern (MC-SVMs3Dmatrix) is further designed on the basis of two-class SVMs3Dmatrix. Firstly, several current MC-SVMs are compared, and sencondly, one based on encoding is chosen to be extended to MC-SVMs3Dmatrix. It is proved by experiment that the performance of MC-SVMs3Dmatrix is better than that of two-class SVMs3Dmatrix.Data set in the paper is made up of 96 cases form Chest No.1-No.3 in XX Tumor Hospital (2009.6-2010.6). All of them are with expert notations. Other CAD schemes and method in the paper are compared by ROC. The results confirm the availability of the method in the paper.