关键词: 肺结节检测, 相关向量机, 粒子群优化, 二阶锥规划

Abstract: As a novel machine learning method, Relevance Vector Machine（RVM） has drawn many scholars’ attention. The recently developed Multi-Kernel Learning RVM（MKLRVM） method makes it a more preferable classifier in the field of pattern recognition. The setup of the kernel parameters and the combination weights between kernel functions in MKLRVM plays an important role in classification accuracy, while they are usually empirically determined rather than a quantitative method. To address this issue, the Particle Swarm Optimization（PSO） and the Second-Order Cone Programming（SOCP） are introduced to find the optimal parameters, reasonable combination of kernel functions is constructed, and a quick calculation method is given. Finally the improved MKLRVM is applied in lung nodule detection using the CT images from the publicly available LIDC database. Through the image processing module, the features of nodule candidates are extracted. The proposed MKLRVM is employed to classify the nodules, and experimental results demonstrate that the computation efficiency is improved and better classification performances are achieved by the presented PSO and SOCP based MKLRVM method, compared to that of the PSO based MKLRVM.

Key words: lung nodule detection, Relevance Vector Machine（RVM）, Particle Swarm Optimization（PSO）, Second-Order Cone Programming（SOCP）