A Method of Pulmonary Nodule Detection utilizing multiple Support Vector Machines
It has been proven that early detection of pulmonary nodules is an important clinical indication for early-stage lung cancer diagnosis. Recently, support vector machines(SVMs) have been extensively used in pattern recognition. However, the application object for SVMs used for false positives(FPs) reduction when detecting lung nodules is generally based on only axial plane. In this paper, we propose a computerized system aimed at lung nodules detection in Multi-Slice Computed Tomography(MSCT) scans with multiple SVMs; it segments the lung field, extracts three sets of candidates regions with two dimensional(2D) dot-enhancement filter on three slice directions respectively, reduces the FPs with multiple SVMs, and then, integrates the classification results by using pixel analysis and region growing method. The proposed scheme is applied on two lung CT datasets. The experimental results illustrate the efficiency of the proposed method.
pulmonary nodule detection computed tomography (CT) multiple support vector machines (SVMs) false positive reduction pixel analysis
Yang Liu Jinzhu Yang Dazhe Zhao Jiren Liu
Key Laboratory of Medical Image Computing (Northeastern University), Ministry of Education Northeast ey Laboratory of Medical Image Computing (Northeastern University), Ministry of Education Northeaste School of Information Science & Engineering Northeastern University Shenyang, China
国际会议
太原
英文
118-121
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）