An adaptive lung nodule detection algorithm

An adaptive lung nodule detection algorithm is presented in computed tomography (CT) images. Here, we present the details of the proposed algorithm and provide a performance analysis using a database from the department of radiology. Our algorithm consists of a feature selected part and a feature classified part. In the feature selected part, eight image features are extracted and Support Vector Machine (SVM) approach is applied to evaluate the classified performance of each feature. In the feature classified part, a nonlinear classifier is constructed on the basis of modified Mahalanobis distance. The adaptive algorithm is used to adjust the threshold in the classifier. The experiment indicated that the algorithm has a good sensitivity and accuracy for lung nodule detection.

[1]  K. Doi,et al.  Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening. , 2004, Academic radiology.

[2]  M. McNitt-Gray,et al.  Lung micronodules: automated method for detection at thin-section CT--initial experience. , 2003, Radiology.

[3]  Jamshid Dehmeshki,et al.  Automated detection of lung nodules in CT images using shape-based genetic algorithm , 2007, Comput. Medical Imaging Graph..

[4]  Lawrence H. Schwartz,et al.  Application of the LDM algorithm to identify small lung nodules on low-dose MSCT scans , 2004, SPIE Medical Imaging.

[5]  Ilaria Gori,et al.  Lung nodule detection in low-dose and thin-slice computed tomography , 2008, Comput. Biol. Medicine.

[6]  V. Vapnik.  Estimation of Dependences Based on Empirical Data , 2006 .

[7]  L. Schwartz,et al.  Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm , 2003, Journal of applied clinical medical physics.

[8]  Lubomir M. Hadjiiski,et al.  Computer-aided detection of lung nodules: false positive reduction using a 3D gradient field method and 3D ellipsoid fitting. , 2005, Medical physics.

[9]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[10]  E. Kazerooni,et al.  Computer-aided detection of lung nodules : False positive reduction using a 3 D gradient field method and 3 D ellipsoid fitting , 2005 .

[11]  Qiang Li,et al.  Recent progress in computer-aided diagnosis of lung nodules on thin-section CT , 2007, Comput. Medical Imaging Graph..

[12]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .