



@book {2397,
	title = {A multiclassifier approach for lung nodule classification},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {4142 LNCS},
	year = {2006},
	note = {Conference code: 68310Cited By (since 1996): 3Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Pereira, C.S.; Instituto de Engenharia Biom{\'e}dica, Rua Roberto Frias, 4200-465 Porto, Portugal; email: cpereira@estgl.ipv.ptReferences: Jemal, A., Murray, T., Ward, E., Samuels, A., Tiwari, R.C., Ghafoor, A., Feuer, E.J., Thun, M.J., (2005) Cancer Statistics, 2005, 55, pp. 10-30. , CA Cancer J Clin;Keserci, B., Yoshida, H., Computerized detection of pulmonary nodules in chest radiographs based on morphological features and wavelet snake model (2002) Medical Image Analysis, 6, pp. 431-447; 
Van Ginneken, B., Romeny, B.M.H., Viergever, M.A., Computer-aided diagnosis in chest radiography: A survey (2001) IEEE Transactions on Medical Imaging, 20 (12), pp. 1228-1241; 
Wei, J., Hagihara, Y., Shimizu, A., Kobatake, H., Optimal image feature set for detecting lung nodules on chest X-ray images (2002) CARS 2002; 
Van Ginneken, B., Katsuragawa, S., Ter Haar Romeny, B.M., Kunio, D., Viergever, M.A., Automatic detection of abnormalities in chest radiographs using local texture analysis (2002) IEEE Transactions on Medical Imaging, 21, pp. 139-149; 
Schilham, A.M.R., Van Ginneken, B., Loog, M., Multi-scale nodule detection in chest radiographs (2003) Lecture Notes in Computer Science, LNCS 2878, pp. 602-609; 
Shiraishi, J., Katsuragawa, S., Ikezoe, J., Matsumoto, T., Kobayashi, T., Matsu, K.-I.K., Matsui, M., Doi, K., Development of a digital image database for chest radiographs with and without a lung nodule: Receiver operating characteristic analysis of radiologists{\textquoteright} detection of pulmonary nodules (2000) American Journal of Roentgenology, 174, pp. 71-74; 
Yoshida, H., Local contralateral subtraction based on bilateral symmetry of lung for reduction of false positives in computerized detection of pulmonary nodules (2004) IEEE Transactions on Biomedical Engineering, 51, pp. 778-789; 
Suzuki, K., Shiraishi, J., Abe, H., MacMahon, H., Doi, K., False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network (2005) Academic Radiology, 12, pp. 191-201; 
Randen, T., Husoy, J.H., Filtering for texture classification: A comparative study (1999) IEEE Transactions on Pattern Analysis and Machine Intelligence, 21 (4); 
Grigorescu, S.E., Petkov, N., Kruizinga, P., Comparison of texture features based on Gabor filters (2002) IEEE Transactions on Image Processing, 11 (10), pp. 195-203; 
Kumar, A., Pang, G.K.H., Defect detection in textured materials using Gabor filters (2002) IEEE Transactions on Industry Applications, 38 (2); 
Varma, M., Zisserman, A., Unifying statistical texture classification frameworks (2004) Image and Vision Computing, 22, pp. 1175-1183; 
Leung, T., Malik, J., Representing and recognizing the visual appearance of materials using three-dimensional textons (2001) International Journal of Computer Vision, 43, pp. 29-44; 
Alexandre, L.A., Campilho, A.C., Kamel, M., On combining classifiers using sum and product rules (2001) Pattern Recognition Letters, 22, pp. 1283-1289
},
	month = {2006///},
	pages = {612 - 623},
	address = {Povoa de Varzim},
	abstract = {The aim of this paper is to examine a multiclassifier approach to the classification of the lung nodules in X-ray chest radiographs. The approach investigated here is based on an image region-based classification whose output is the information of the presence or absence of a nodule in an image region. The classification was made, essentially, in two steps: firstly, a set of rotation invariant features was extracted from the responses of a multi-scale and multi-orientation filter bank; secondly, different classifiers (multi-layer perceptrons) are designed using different features sets and trained in different data. These classifiers are further combined in order to improve the classification performance. The obtained results are promising and can be used for reducing the false-positives nodules detected in a computer-aided diagnosis system. {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.},
	keywords = {Biological organs, Classification (of information), Computer aided diagnosis, False-positives nodules, Medical imaging, Multi-orientation filter bank, Radiography, X rays, X-ray chest radiographs},
	isbn = {03029743 (ISSN); 3540448942 (ISBN); 9783540448945 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33749658606\&partnerID=40\&md5=f6e83e868f5b1ee08b400ee9191138cd},
	author = {Pereira, C.S. and Alexandre, L.A. and Mendon{\c c}a, A.M. and Campilho, A.}
}
