



@book {2396,
	title = {Lung parenchyma segmentation from CT images based on material decomposition},
	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): 1Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Vinhais, C.; Instituto de Engenharia Biom{\'e}dica, Laborat{\'o}rio de Sinal e Imagem Biom{\'e}dica, Campus da FEUP, Rua Roberto Frias, s/n, 4200-465 Porto, Portugal; email: cav@isep.ipp.ptReferences: Armato, S.G., Giger, M.L., Moran, C.J., Doi, K., MacMahon, H., Computerized detection of lung nodules in computed tomography scans (1998) Proceedings of the 1st International Workshop on Computer-aided Diagnosis, pp. 119-124;Armato III, S.G., Giger, M.L., Morgan, C.J., Blackburn, J.T., Doi, K., MacMahon, H., Computerized detection of pulmonary nodules on CT scans (1999) RadioGraphics, 19 (5), pp. 1303-1311; 
Brown, M.S., McNitt-Gray, M.F., Goldin, J.G., Suh, R.D., Sayre, J.W., Aberle, D.R., Patient-specific models for lung nodule detection and surveillance in CT images (2001) IEEE Trans. on Medical Imaging, 20 (12), pp. 1242-1250; 
Gurcan, M.N., Sahiner, B., Petrick, N., Chan, H.-P., Kazerooni, E.A., Cascade, P.N., Hadjiiski, L., Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system (2002) Medical Physics, 29 (11), pp. 2552-2558; 
Armato III, S.G., Sensakovic, W.F., Automated lung segmentation for thoracic CT: Impact on computer-aided diagnosis (2004) Academic Radiology, 11, pp. 1011-1021; 
Kemerink, G.J., Lamers, R.J.S., Pellis, B.J., Kruize, H.H., Van Engelshoven, J.M.A., On segmentation of lung parenchyma in quantitative computed tomography of the lung (1998) Medical Physics, 25 (12), pp. 2432-2439; 
Brown, M.S., McNitt-Gray, M.F., Goldin, J.G., Greaser, L.E., Aberle, D.R., Knowledge-based method for segmentation and quantitative analysis of lung function from CT (1998) Proceedings of the 1st International Workshop on Computer-aided Diagnosis, pp. 113-118; 
Leader, J.K., Zheng, B., Rogers, R.M., Sciurba, F.C., Perez, A., Chapman, B.E., Patel, S., Gur, D., Automated lung segmentation in X-ray computed tomography: Development and evaluation of a heuristic threshold-based scheme (2003) Academic Radiology, 10, pp. 1224-1236; 
Hu, S., Hoffman, E.A., Reinhardt, J.M., Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images (2001) IEEE Trans. on Medical Imaging, 20 (6), pp. 490-498; 
Alvarez, R.E., Macovsky, A., Energy-selective reconstruction in X-ray computerized tomography (1976) Phys. Med. Biol., 21, pp. 733-744; 
Lehmann, L.A., Alvarez, R.E., Macovski, A., Brody, W.R., Generalized image combinations in dual KVP digital radiography (1981) Medical Physics, 8 (5), pp. 659-667; 
Macovsky, A., (1983) Medical Imaging Systems, , Information and Systems Science Series, Prentice Hall, Inc; 
Tissue substitutes in radiation dosimetry and measurement (1989) Report 44 of the International Commission on Radiation Units and Measurements, , Bethesda, MD; 
Berger, M.J., Hubbell, J.H., XCOM: Photon cross sections on a personal computer (1987) NBSIR, 87 (3597); 
Boone, J.M., Seibert, J.A., An accurate method for computer-generating tungsten anode x-ray spectra from 30 to 140 kV (1997) Medical Physics, 24 (11), pp. 1661-1670; 
Rijsbergen, C.J.V., (1979) Information Retrieval, , London, Butterworths, 2nd ed
},
	month = {2006///},
	pages = {624 - 635},
	address = {Povoa de Varzim},
	abstract = {We present a fully automated method for extracting the lung region from volumetric X-ray CT images based on material decomposition. By modeling the human thorax as a composition of different materials, the proposed method follows a threshold-based, hierarchical voxel classification strategy. The segmentation procedure involves the automatic computation of threshold values and consists on three main steps: patient segmentation and decomposition, large airways extraction and lung parenchyma decomposition, and lung region of interest segmentation. Experimental results were performed on thoracic CT images acquired from 30 patients. The method provides a reproducible set of thresholds for accurate extraction of the lung parenchyma, needed for computer aided diagnosis systems. {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.},
	keywords = {Biological organs, Classification (of information), Computer simulation, Computerized tomography, Image segmentation, Lung parenchyma, Medical imaging, Patient segmentation, Patient treatment, Volumetric X-ray CT images, Voxel classification strategy},
	isbn = {03029743 (ISSN); 3540448942 (ISBN); 9783540448945 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33749660395\&partnerID=40\&md5=d07d50c5605d8c249a87771d3469fa78},
	author = {Vinhais, C. and Campilho, A.}
}
@book {2427,
	title = {Genetic model-based segmentation of chest X-ray images using free form deformations},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {3656 LNCS},
	year = {2005},
	note = {Conference code: 67124Export Date: 21 November 2012Source: Scopusdoi: 10.1007/11559573_116Language of Original Document: EnglishCorrespondence Address: Vinhais, C.; INEB - Instituto de Engenharia Biom{\'e}dica, Laborat{\'o}rio de Sinai e Imagem Biom{\'e}dica, Campus da FEUP, Rua Roberto Frias, s/n, 4200-465 Porto, Portugal; email: cav@isep.ipp.ptReferences: Carreira, M., Cabello, D., Penedo, M., Mosquera, A., Computer-aided diagnoses: Automatic detection of lung nodules (1998) Medical Physics, 25 (10), pp. 1998-2006;Nakaromi, N., Doi, K., MacMahon, H., Sasaki, Y., Montner, S.M., Effect on heart-size parameters computed from digital chest radiographs on detection of cardiomegaly: Potencial usefulness for computer-aided diagnosis (1991) Inv. Radiology, 26, pp. 546-550; 
Van Ginneken, B., Romeny, B.H., Viergever, M.A., Computer-aided diagnosis in chest radiography: A survey (2001) IEEE Trans. on Medical Imaging, 20 (12), pp. 1228-1241; 
Cootes, T.E., Hill, A., Taylor, C.J., Haslam, J., The use of active shape models for locating structures in medical images (1994) Image, and Vision Computing, 12 (6), pp. 355-366; 
Sederberg, T.W., Parry, S.R., Free-form deformation of solid geometric models (1986) ACM SIGGRAPH Computer Graphics, 20 (4), pp. 151-160; 
Field, D.J., Relations between the statistics of natural images and the response properties of cortical cells (1987) J. Opt. Soc. Am. A, 4 (12), pp. 2379-2394; 
Petkov, N., Kruizinga, P., Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli: Bar and grating cells (1997) Biol. Cybern., 76, pp. 83-96; 
Carpenter, M.B., (1991) Core Text of Neuroanatomy. 4th Ed.; 
Lotjonen, J., Magnin, I., Nenonen, J., Katila, T., Reconstruction of 3-D geometry using 2-D profiles and a geometric prior model (1999) IEEE Trans. on Medical Imaging, 18 (10), pp. 992-1002; 
Goldberg, D.E., (1989) Genetic Algorithms in Search, Optimization and Machine Learning; 
Gofman, Y., Kiryati, N., Detecting symmetry in grey level images: The global Optimization approach (1998) International Journal of Computer Vision (IJCV), 29, pp. 29-45
},
	month = {2005///},
	pages = {958 - 965},
	address = {Toronto},
	abstract = {A method is proposed to segment digital posterior-anterior chest X-ray images. The segmentation is achieved through the registration of a deformable prior model, describing the anatomical structures of interest, to the X-ray image. The deformation of the model is performed using a deformation grid. A coarse matching of the model is done using anatomical landmarks automatically extracted from the image, and maps of oriented edges are used to guide the deformation process, optimized with a probabilistic genetic algorithm. The method is applied to extract the ribcage and delineate the mediastinum and diaphragms. The segmentation is needed for defining the lungs region, used in computer-aided diagnosis systems. {\textcopyright} Springer-Verlag Berlin Heidelberg 2005.},
	keywords = {Computer aided analysis, Diaphragms, Feature extraction, Free Form Deformation, Gabor Filters, Genetic Algorithms, Image segmentation, Model-based Image Segmentation, Optimization, Pattern matching, X ray analysis},
	isbn = {03029743 (ISSN); 3540290699 (ISBN); 9783540290698 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33645986088\&partnerID=40\&md5=cda7c9ce6977b972158c866963cddc4b},
	author = {Vinhais, C. and Campilho, A.}
}
@book {2461,
	title = {Ribcage Boundary Delineation in Chest X-ray Images},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {3212},
	year = {2004},
	note = {Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Vinhais, C.; INEB - Instituto de Engenharia Biom{\'e}dica, Laborat{\'o}rio de Sinal e Imagem Biom{\'e}dica, Campus da FEUP, Rua Roberto Frias, s/n, 4200-465 Porto, Portugal; email: cav@isep.ipp.ptReferences: Carreira, M., Cabello, D., Computer-Aided Diagnoses: Automatic Detection of Lung Nodules (1998) Med. Phys., 25 (10), pp. 1998-2006;Xu, X., Development of an Improved CAD Scheme for Automated Detection of Lung Nodules in Digital Chest Images (1997) Med. Phys., 25 (9), pp. 1395-1403; 
Lo, S.B., Artificial Convolution Neural Network Techniques and Applications for Lung Nodule Detection (1995) IEEE Trans, on Medical Imaging, 14 (4), pp. 711-718; 
Sanada, S., Doi, K., MacMahon, H., Image feature analysis and computer-aided diagnosis in digital radiography: Automated detection of pneumothorax in chest images (1992) Med. Phys., 19, pp. 1153-1160; 
Nakaromi, N., Doi, K., MacMahon, H., Sasaki, Y., Montner, S.M., Effect on heart-size parameters computed from digital chest radiographs on detection of cardiomegaly: Potencial usefulness for computer-aided diagnosis (1991) Inv. Radiology, 26, pp. 546-550; 
Van Ginneken, B., Romeny, B.H., Viergever, M.A., Computer-aided Diagnosis in Chest Radiography: A survey (2001) IEEE Trans, on Medical Imaging, 20 (12), pp. 1228-1241; 
Xu, X., Doi, K., Image feature analysis for computer-aided diagnosis: Accurate determination of ribcage boundary in chest radiographs (1995) Med. Phys., 22 (5), pp. 617-626; 
Nakaromi, N., Doi, K., Sabeti, V., MacMahon, H., Image feature analysis and computer-aided diagnosis in digital radiography: Automated analysis of sizes of heart and lung in chest images (1990) Med. Phys., 17, pp. 342-435; 
Yue, Z., Goshtasby, A., Ackerman, L.V., Automatic Detection of Rib Borders in Chest Radiographs (1995) IEEE Trans, on Medical Imaging, 14 (3), pp. 525-536; 
Goldberg, D.E., Genetic Algorithms in Search, Optimization \& Machine Learning, pp. 15767-15775. , Reading, MA: Addison Wiley, 1989. - ISBN 0-201-; 
Vinhais, C., Campilho, A., Optimal Detection of Symmetry Axis in Digital Chest X-ray Images (2003) 1st Iberian Conference on Pattern Recognition and Image Analysis - IbPRIA 2003, 2652, pp. 1082-1089. , Lecture Notes in Computer Science, Springer-Verlag, Berlin Heidelberg New York; 
Gofman, Y., Kiryati, N., Detecting Symmetry in Grey Level Images: The Global Optimization Approach (1998) International Journal of Computer Vision (IJCV), 29, pp. 29-45; 
Crist{\'o}bal, G., Navarro, R., Space and frequency variant image enhancement based on Gabor representation (1994) Pattern Recognit. Lett., 15, pp. 273-277; 
Mehrotra, R., Namuduri, K.R., Ranganathan, N., Gabor filter-based edge detection (1992) Pattern Recognit., 25 (12), pp. 1479-1494; 
Petkov, N., Kruizinga, P., Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli: Bar and grating cells (1997) Biol. Cybern., 76, pp. 83-96; 
Carpenter, M.B., (1991) Core Text of Neuroanatomy, , 4th ed., Willams \& Wilkins, Baltimore, ISBN 0-683-01457-9
},
	month = {2004///},
	pages = {59 - 67},
	abstract = {We propose a method for segmenting the ribcage boundary of digital postero-anterior chest X-ray images. The segmentation is achieved by first defining image landmarks: the center of the ribcage and, using polar transformation from this point, two initial points belonging to the ribcage. A bank of Gabor filters (in analogy with the simple cells present in the human visual cortex) is used to obtain an orientation edges enhanced image. In this enhanced image, an edge following, starting from the landmarks previously determined, is performed for delineating the left and right sections of the ribcage. The complete segmentation is then accomplished by connecting these sections with the top section of the ribcage, obtained by means of spline interpolation. {\textcopyright} Springer-Verlag 2004.},
	keywords = {Edge following, Gabor Filters, Polar image transform, Probabilistic genetic algorithm, Ribcage boundary segmentation},
	isbn = {03029743 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-35048836666\&partnerID=40\&md5=14b16a871c602045da0d3bf1de34670c},
	author = {Vinhais, C. and Campilho, A.}
}
@book {2502,
	title = {Optimal detection of symmetry axis in digital chest X-ray images},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {2652},
	year = {2003},
	note = {Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Vinhais, C.; INEB - Institute de Engenharia Biom{\'e}dica, Laborat{\'o}rio de Sinai e Imagem Biom{\'e}dica, Campus da FEUP, Rua Roberto Frias, s/n, 4200-465 Porto, Portugal; email: cav@isep.ipp.ptReferences: Marola, G., On the Detection of the Axes of Symmetry of Symmetric and Almost Symmetric Planar Images (1989) IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, pp. 104-108;Marola, G., Using Symmetry for Detecting and Locating Objects in a Picture (1989) Computer Vision, Graphics and Image Processing, 46, pp. 179-195; 
Zabrodsky, H., Peleg, S., Avnir, D., A Measure of Symmetry Based on Shape Similarity (1992) Proceedings of Computer Vision and Pattern Recognition, pp. 703-706; 
Zabrodsky, H., Peleg, S., Avnir, D., Hierarchical Symmetry (1992) Proceedings of International Conference on Pattern Recognition, 3, pp. 9-11; 
O{\textquoteright}Mara, D., Owens, R., Measuring Symmetry in Digital Images (1996) Proceedings of TENCOM {\textquoteright}96-IEEE Region Ten Conference: Digital Signal Processing Applications, 1, pp. 151-156; 
Sun, C., Symmetry Detection using Gradient Information (1995) Pattern Recognition Letters, 16 (9), pp. 987-996; 
Sun, C., Si, D., Fast Reflectional Symmetry Detection using Orientation Histograms (1999) Real-Time Imaging, 5, pp. 63-74; 
Shen, D., Ip, H.H.S., Cheung, K.K.T., Teoh, E.K., Symmetry Detection by Generalized Complex (GC) Moments: A Close-form Solution (1999) IEEE Transactions on Pattern Analysis and Machine Intelligence, 21 (5), pp. 466-476; 
Shen, D., Ip, H.H.S., Teoh, E.K., An Energy of Asymmetry for Accurate Detection of Global Reflection Axes (2001) Image and Vision Computing, 19, pp. 283-297; 
Yuen, K.S.Y., Chan, W.W., Two Methods for Detecting Symmetries (1994) Pattern Recognition Letters, 15, pp. 279-286; 
Zielke, T., Brauckmann, M., Von Seelen, W., Intensity and Edge-based Symmetry Detection with an Application to Car-following (1990) Computer Vision, Graphics and Image Processing: Image Understanding, 58, pp. 177-190; 
Gofman, Y., Kiryati, N., Detecting Symmetry in Grey Level Images: The Global Optimization Approach (1998) International Journal of Computer Vision (IJCV), 29, pp. 29-45; 
Masuda, T., Yamamoto, K., Yamada, H., Detection of Partial Symmetry using Correlation with Rotated-reflected Images (1993) Pattern Recognition, 26 (8), pp. 1245-1253
},
	month = {2003///},
	pages = {1082 - 1089},
	abstract = {We present a method for detecting the axis of bilateral symmetry in a digital chest X-ray image and subsequently measuring the degree of symmetry of the image. The detection is achieved by analysing rotated-reflected digital chest X-ray images and it is posed as a global optimization problem solved with a probabilistic genetic algorithm (PGA). The global search is initially based on natural peak orientation information related to the orientation of the symmetry axis. Only a few generations of the PGA are needed to achieve convergence to all the images in the database. This method is applied directly on the intensity input image and does not require any prior segmentation. {\textcopyright} Springer-Verlag Berlin Heidelberg 2003.},
	keywords = {image processing, Probabilistic genetic algorithm, Reflectional Symmetry},
	isbn = {03029743 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-35248899557\&partnerID=40\&md5=f8181de2e6823d44786341f3d6642aed},
	author = {Vinhais, C. and Campilho, A.}
}
