Feature Selection and Performance Evaluation of Support Vector Machine (SVM)-Based Classifier for Differentiating Benign and Malignant Pulmonary Nodules by Computed Tomography

There are lots of work being done to develop computer-assisted diagnosis and detection (CAD) technologies and systems to improve the diagnostic quality for pulmonary nodules. Another way to improve accuracy of diagnosis on new images is to recall or find images with similar features from archived historical images which already have confirmed diagnostic results, and the content-based image retrieval (CBIR) technology has been proposed for this purpose. In this paper, we present a method to find and select texture features of solitary pulmonary nodules (SPNs) detected by computed tomography (CT) and evaluate the performance of support vector machine (SVM)-based classifiers in differentiating benign from malignant SPNs. Seventy-seven biopsy-confirmed CT cases of SPNs were included in this study. A total of 67 features were extracted by a feature extraction procedure, and around 25 features were finally selected after 300 genetic generations. We constructed the SVM-based classifier with the selected features and evaluated the performance of the classifier by comparing the classification results of the SVM-based classifier with six senior radiologists′ observations. The evaluation results not only showed that most of the selected features are characteristics frequently considered by radiologists and used in CAD analyses previously reported in classifying SPNs, but also indicated that some newly found features have important contribution in differentiating benign from malignant SPNs in SVM-based feature space. The results of this research can be used to build the highly efficient feature index of a CBIR system for CT images with pulmonary nodules.

[1]  Katherine P. Andriole,et al.  Front Matter: Volume 6919 , 2008 .

[2]  David A. Clausi,et al.  Designing Gabor filters for optimal texture separability , 2000, Pattern Recognit..

[3]  Thomas Martin Deserno,et al.  Ontology of Gaps in Content-Based Image Retrieval , 2009, Journal of Digital Imaging.

[4]  Guozhen Zhang,et al.  Content-based image retrieval in picture archiving and communication systems , 2006, SPIE Medical Imaging.

[5]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .

[7]  Rolf W. Günther,et al.  Integration of a research CBIR system with RIS and PACS for radiological routine , 2008, SPIE Medical Imaging.

[8]  Tom Fawcett,et al.  ROC Graphs: Notes and Practical Considerations for Data Mining Researchers , 2003 .

[9]  Alexandre César Muniz de Oliveira,et al.  Comparison of FLDA, MLP and SVM in Diagnosis of Lung Nodule , 2005, MLDM.

[10]  J Konishi,et al.  Solitary pulmonary nodule: preliminary study of evaluation with incremental dynamic CT. , 1995, Radiology.

[11]  Antoine Geissbühler,et al.  Erratum to "A review of content-based image retrieval systems in medical applications - Clinical benefits and future directions" [I. J. Medical Informatics 73 (1) (2004) 1-23] , 2009, Int. J. Medical Informatics.

[12]  Henning Müller,et al.  A classification framework for lung tissue categorization , 2008, SPIE Medical Imaging.

[13]  K Nakamura,et al.  Computerized analysis of the likelihood of malignancy in solitary pulmonary nodules with use of artificial neural networks. , 2000, Radiology.

[14]  Marcelo Gattass,et al.  Diagnosis of lung nodule using semivariogram and geometric measures in computerized tomography images , 2005, Comput. Methods Programs Biomed..

[15]  D. Haussler,et al.  Knowledge-based analysis of microarray gene expression , 2000 .

[16]  E. Fishman,et al.  Solitary pulmonary nodules: CT assessment. , 1986, Radiology.

[17]  Noboru Niki,et al.  Hybrid Classification Approach of Malignant and Benign Pulmonary Nodules Based on Topological and Histogram Features , 2000, MICCAI.

[18]  Antoine Geissbühler,et al.  A Review of Content{Based Image Retrieval Systems in Medical Applications { Clinical Bene(cid:12)ts and Future Directions , 2022 .

[19]  Sameer Antani,et al.  Gaps in content-based image retrieval , 2007, SPIE Medical Imaging.

[20]  Lance M. Kaplan,et al.  Texture segmentation using multiscale Hurst features , 1997, Proceedings of International Conference on Image Processing.

[21]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[22]  Kunio Doi,et al.  Computer-aided diagnosis to distinguish benign from malignant solitary pulmonary nodules on radiographs: ROC analysis of radiologists' performance--initial experience. , 2003, Radiology.

[23]  Michael F. McNitt-Gray,et al.  Pattern classification approach to characterizing solitary pulmonary nodules imaged on high-resolution computed tomography , 1996, Medical Imaging.

[24]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2004 .

[25]  Thorsten Joachims,et al.  Text categorization with support vector machines , 1999 .

[26]  Michael Unser,et al.  Texture classification and segmentation using wavelet frames , 1995, IEEE Trans. Image Process..

[27]  K. Doi,et al.  Usefulness of an artificial neural network for differentiating benign from malignant pulmonary nodules on high-resolution CT: evaluation with receiver operating characteristic analysis. , 2002, AJR. American journal of roentgenology.

[28]  Sumit K. Shah,et al.  Computer aided characterization of the solitary pulmonary nodule using volumetric and contrast enhancement features. , 2005, Academic radiology.