- About this Journal ·
- Abstracting and Indexing ·
- Advance Access ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Advances in Fuzzy Systems
Volume 2012 (2012), Article ID 327861, 10 pages
Artificial Neural Network-Statistical Approach for PET Volume Analysis and Classification
1Department of Electronic and Computer Engineering, School of Engineering and Design, Brunel University, Uxbridge UB83PH, UK
2Nanotechnology and Integrated BioEngineering Centre, University of Ulster, Newtownabbey BT37OQB, UK
3Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland
4Geneva Neuroscience Center, Geneva University, CH-1205 Geneva, Switzerland
5Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, 9700 RB Groningen, The Netherlands
Received 14 September 2011; Accepted 12 January 2012
Academic Editor: Jiann-Shing Shieh
Copyright © 2012 Mhd Saeed Sharif et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- H. Zaidi and I. El Naqa, “PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 37, no. 11, pp. 2165–2187, 2010.
- H. Zaidi, M. Diaz-Gomez, A. Boudraa, and D. O. Slosman, “Fuzzy clustering-based segmented attenuation correction in whole-body PET imaging,” Physics in Medicine and Biology, vol. 47, no. 7, pp. 1143–1160, 2002.
- I. J. Kalet and M. M. Austin-Seymour, “Use of medical imagery in the delivery and planning of radiation therapy,” Journal of the American Medical Informatics Association, vol. 4, no. 5, pp. 327–339, 1997.
- D. W. G. Montgomery and A. Amira, “Automated multiscale segmentation of oncological cerebral MR image volumes,” in Proceedings of the IEEE International Conference on Computer Systems and Information Technology, 2005.
- D. Delbeke and W. H. Martin, “Positron emission tomography imaging in oncology,” Radiologic Clinics of North America, vol. 39, no. 5, pp. 883–917, 2001.
- D. A. Mankoff, M. Muzi, and H. Zaidi, “Quantitative analysis in nuclear oncologic imaging,” in Quantitative Analysis of Nuclear Medicine Images, H. Zaidi, Ed., pp. 494–536, Springer, New York, NY, USA, 2006.
- M. Aristophanous, B. C. Penney, and C. A. Pelizzari, “The development and testing of a digital PET phantom for the evaluation of tumor volume segmentation techniques,” Medical Physics, vol. 35, no. 7, pp. 3331–3342, 2008.
- H. Vees, S. Senthamizhchelvan, R. Miralbell, D. C. Weber, O. Ratib, and H. Zaidi, “Assessment of various strategies for 18F-FET PET-guided delineation of target volumes in high-grade glioma patients,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 36, no. 2, pp. 182–193, 2009.
- S. Basu, “Selecting the optimal image segmentation strategy in the era of multitracer multimodality imaging: a critical step for image-guided radiation therapy,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 36, no. 2, pp. 180–181, 2009.
- G. F. Luger, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Pearson Education, 2009.
- Y. Zhengchun and L. Hongji, “Face detection based on SCNN and wavelet invariant moment in color image,” in Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR '07), pp. 783–787, November 2007.
- L. Sun and S. Wu, “Handwritten Chinese character recognition based on supervised competitive learning neural network and block-based relative fuzzy feature extraction,” in Applications of Neural Networks and Machine Learning in Image Processing IX, Proceedings of SPIE, pp. 65–70, January 2005.
- S. Joo, W. K. Moon, and H. C. Kim, “Computer-aidied diagnosis of solid breast nodules on ultrasound with digital image processing and artificial neural network,” in Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '04), pp. 1397–1400, September 2004.
- S. G. Mougiakakou, I. Valavanis, K. S. Nikita, A. Nikita, and D. Kelekis, “Characterization of CT liver lesions based on texture features and a multiple neural network classification scheme,” in Proceddings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1287–1290, September 2003.
- W. E. Reddick, J. O. Glass, E. N. Cook, T. David Elkin, and R. J. Deaton, “Automated segmentation and classification of multispectral magnetic resonance images of brain using artificial neural networks,” IEEE Transactions on Medical Imaging, vol. 16, no. 6, pp. 911–918, 1997.
- C. C. Reyes-Aldasoro and A. L. Aldeco, “Image segmentation and compression using neural networks,” in Advances in Artificial Perception and Robotics (CIMAT '00), October 2000.
- K. Suzuki, H. Abe, H. MacMahon, and K. Doi, “Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN),” IEEE Transactions on Medical Imaging, vol. 25, no. 4, pp. 406–416, 2006.
- M. Halt, C. C. Le Rest, A. Turzo, C. Roux, and D. Visvikis, “A fuzzy locally adaptive Bayesian segmentation approach for volume determination in PET,” IEEE Transactions on Medical Imaging, vol. 28, no. 6, pp. 881–893, 2009.
- S. Belhassen and H. Zaidi, “A novel fuzzy C-means algorithm for unsupervised heterogeneous tumor quantification in PET,” Medical Physics, vol. 37, no. 3, pp. 1309–1324, 2010.
- Y. Li and Z. Chi, “MR brain image segmentation based on self-organizing map network,” International Journal of Information Technology, vol. 11, no. 8, 2005.
- M. S. Sharif, A. Amira, and H. Zaidi, “3D oncological PET volume analysis using CNN and LVQNN,” in Proceedings of IEEE International Symposium on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems (ISCAS '10), pp. 1783–1786, June 2010.
- C. Collet and F. Murtagh, “Multiband segmentation based on a hierarchical Markov model,” Pattern Recognition, vol. 37, no. 12, pp. 2337–2347, 2004.
- D. L. Weakliem, “A critique of the Bayesian information criterion for model selection,” Sociological Methods and Research, vol. 27, no. 3, pp. 359–397, 1999.
- C. T. Volinsky and A. E. Raftery, “Bayesian information criterion for censored survival models,” Biometrics, vol. 56, no. 1, pp. 256–262, 2000.
- J. T. Bushberg, et al., The Essential Physics of Medical Imaging, Lippincott Williams and Wilkins, Philadelphia, Pa, USA, 2006.
- G. Dreyfus, Neural Networks Methodology and Applications, Springer, Berlin, Germany, 2005.
- B. Curry and P. H. Morgan, “Evaluating Kohonen's learning rule: an approach through genetic algorithms,” European Journal of Operational Research, vol. 154, no. 1, pp. 191–205, 2004.
- S. Clippingdale and R. Wilson, “Self-similar neural networks based on a Kohonen learning rule,” Neural Networks, vol. 9, no. 5, pp. 747–763, 1996.
- M. A. Arbib, The Handbook of Brain Theory and Neural Networks, Massachusetts Institute of Technology, 2003.
- M. Swiercz, J. Kochanowicz, J. Weigele et al., “Learning vector quantization neural networks improve accuracy of transcranial color-coded duplex sonography in detection of middle cerebral artery spasm—preliminary report,” Neuroinformatics, vol. 6, no. 4, pp. 279–290, 2008.
- P. Somervuo and T. Kohonen, “Self-organizing maps and learning vector quantization forfeature sequences,” Neural Processing Letters, vol. 10, no. 2, pp. 151–159, 1999.
- C. Jonsson, R. Odh, P. O. Schnell, and S. A. Larsson, “A comparison of the imaging properties of a 3- and 4-ring biograph PET scanner using a novel extended NEMA phantom,” in Proceedings of IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS-MIC '07), pp. 2865–2867, November 2007.
- M. D. R. Thomas, D. L. Bailey, and L. Livieratos, “A dual modality approach to quantitative quality control in emission tomography,” Physics in Medicine and Biology, vol. 50, no. 15, pp. N187–N194, 2005.
- H. Herzog, L. Tellmann, C. Hocke, U. Pietrzyk, M. E. Casey, and T. Kawert, “NEMA NU2-2001 guided performance evaluation of four siemens ECAT PET scanners,” IEEE Transactions on Nuclear Science, vol. 51, no. 5, pp. 2662–2669, 2004.
- H. Bergmann, G. Dobrozemsky, G. Minear, R. Nicoletti, and M. Samal, “An inter-laboratory comparison study of image quality of PET scanners using the NEMA NU 2-2001 procedure for assessment of image quality,” Physics in Medicine and Biology, vol. 50, no. 10, pp. 2193–2207, 2005.
- J. M. Wilson and T. G. Turkington, “Multisphere phantom and analysis algorithm for PET image quality assessment,” Physics in Medicine and Biology, vol. 53, no. 12, pp. 3267–3278, 2008.
- H. Zaidi, F. Schoenahl, and O. Ratib, “Geneva PET/CT facility: design considerations and performance characteristics of two commercial (biograph 16/64) scanners,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 34, supplement 2, p. S166, 2007.
- M. S. Sharif, M. Abbod, A. Amira, and H. Zaidi, “Artificial neural network-based system for PET volume segmentation,” International Journal of Biomedical Imaging, vol. 2010, Article ID 105610, 11 pages, 2010.
- M. Vuk and T. Curk, “ROC curve, lift chart and calibration plot,” Metodoloski zvezki, vol. 3, no. 1, pp. 89–108, 2006.
- J. -F. Daisne, M. Sibomana, A. Bol, G. Cosnard, M. Lonneux, and V. Grégoire, “Evaluation of a multimodality image (CT, MRI and PET) coregistration procedure on phantom and head and neck cancer patients: accuracy, reproducibility and consistency,” Radiotherapy and Oncology, vol. 69, no. 3, pp. 237–245, 2003.
- J. F. Daisne, T. Duprez, B. Weynand et al., “Tumor volume in pharyngolaryngeal squamous cell carcinoma: Comparison at CT, MR imaging, and FDG PET and validation with surgical specimen,” Radiology, vol. 233, no. 1, pp. 93–100, 2004.
- X. Geets, J. A. Lee, A. Bol, M. Lonneux, and V. Grégoire, “A gradient-based method for segmenting FDG-PET images: methodology and validation,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 34, no. 9, pp. 1427–1438, 2007.