Title :
An adaptive lung nodule detection algorithm
Author :
Guo, Wei ; Wei, Ying ; Zhou, Hanxun ; Xue, DingYe
Author_Institution :
Sch. of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China
Abstract :
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.
Keywords :
computerised tomography; feature extraction; image classification; medical image processing; object detection; patient diagnosis; support vector machines; SVM approach; adaptive lung nodule detection algorithm; classifier threshold; computed tomography image; database; feature classification; feature extraction; feature selection; modified Mahalanobis distance; nonlinear classifier; radiology department; support vector machine; Computed tomography; Detection algorithms; Feature extraction; Image databases; Lungs; Performance analysis; Radiology; Spatial databases; Support vector machine classification; Support vector machines; an adaptive classification; feature extraction; lung nodule detection; modified Mahalanobis distance vector; the Support Vector Machine;
Conference_Titel :
Control and Decision Conference, 2009. CCDC '09. Chinese
Conference_Location :
Guilin
Print_ISBN :
978-1-4244-2722-2
Electronic_ISBN :
978-1-4244-2723-9
DOI :
10.1109/CCDC.2009.5192686
