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BioMed Research International
Volume 2013 (2013), Article ID 175271, 25 pages
New Morphological Features for Grading Pancreatic Ductal Adenocarcinomas
Department of Computer & Information Engineering, Inha University, 253 Yonghyun-dong,
Nam-gu, Incheon 402-751, Republic of Korea
Received 17 January 2013; Revised 7 April 2013; Accepted 24 April 2013
Academic Editor: Xin-yuan Guan
Copyright © 2013 Jae-Won Song and Ju-Hong Lee. 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.
- M. N. Gurcan, L. E. Boucheron, A. Can, A. Madabhushi, N. M. Rajpoot, and B. Yener, “Histopathological image analysis: a review,” IEEE Reviews in Biomedical Engineering, vol. 2, pp. 147–171, 2009.
- A. E. Tutac, D. Racoceanu, T. Putti, W. Xiong, W. K. Leow, and V. Cretu, “Knowledge-guided semantic indexing of breast cancer histopathology images,” in Proceedings of the International Conference on BioMedical Engineering and Informatics (BMEI '08), vol. 2, pp. 107–112, May 2008.
- S. M. Ismail, A. B. Colclough, J. S. Dinnen et al., “Observer variation in histopathological diagnosis and grading of cervical intraepithelial neoplasia,” British Medical Journal, vol. 298, no. 6675, pp. 707–710, 1989.
- A. Andrion, C. Magnani, P. G. Betta et al., “Malignant mesothelioma of the pleura: interobserver variability,” Journal of Clinical Pathology, vol. 48, no. 9, pp. 856–860, 1995.
- C. Demir and B. Yener, “Automated cancer diagnosis based on histopathological systematic images: a systematic survey,” Rensselaer Polytechnic Institute, 2005.
- S. Al-Janabi, A. Huisman, and P. J. Van Diest, “Digital pathology: current status and future perspectives,” Histopathology, vol. 61, no. 1, pp. 1–9, 2012.
- A. Madabhushi, “Digital pathology image analysis: opportunities and challenges,” Imaging in Medicine, vol. 1, no. 1, pp. 7–10, 2009.
- R. S. Weinstein, A. R. Graham, L. C. Richter et al., “Overview of telepathology, virtual microscopy, and whole slide imaging: prospects for the future,” Human Pathology, vol. 40, no. 8, pp. 1057–1069, 2009.
- J. I. Epstein and G. J. Netto, Biopsy Interpretation of the Prostate, Lippincott Williams & Wilkins, Philadelphia, Pa, USA, 4th edition, 2007.
- D. F. Gleason and G. T. Mellinger, “Prediction of prognosis for prostatic adenocarcinoma by combined histological grading and clinical staging,” Journal of Urology, vol. 167, no. 2, pp. 953–959, 2002.
- A. Tabesh, M. Teverovskiy, H. Y. Pang et al., “Multifeature prostate cancer diagnosis and gleason grading of histological images,” IEEE Transactions on Medical Imaging, vol. 26, no. 10, pp. 1366–1378, 2007.
- S. Naik, S. Doyle, M. Feldman, J. Tomaszewski, and A. Madabhushi, “Gland segmentation and computerized gleason grading of prostate histology by integrating low-, high-level and domain specific information,” in Proceedings of the Microscopic Image Analysis with Applications in Biology (MIAAB '07), 2007.
- P.-W. Huang and C. H. Lee, “Automatic classification for pathological prostate images based on fractal analysis,” IEEE Transactions on Medical Imaging, vol. 28, no. 7, pp. 1037–1050, 2009.
- A. W. Wetzel, “Evaluation of prostate tumor grades by content-based image retrieval,” in Proceedings of the 27th AIPR Workshop on Advances in Computer-Assisted Recognition, vol. 3584, pp. 244–252, 1999.
- S. Doyle, M. Hwang, K. Shah, A. Madabhushi, M. Feldman, and J. Tomaszeweski, “Automated grading of prostate cancer using architectural and textural image features,” in Proceedings of the 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI '07), pp. 1284–1287, April 2007.
- S.-K. Tai, Y.-C. Wu, C.-Y. Li, Y. J. Jan, and S. C. Lin, “Computer-assisted detection and grading of prostatic cancer in biopsy image,” 2010, http://www.libsearch.com/view/1046167.
- Y. Peng, Y. Jiang, S. T. Chuang, and X. J. Yang, “Computer-aided detection of prostate cancer on tissue sections,” Applied Immunohistochemistry & Molecular Morphology, vol. 17, no. 5, pp. 442–450, 2009.
- N. H. Anderson, P. W. Hamilton, P. H. Bartels, D. Thompson, R. Montironi, and J. M. Sloan, “Computerized scene segmentation for the discrimination of architectural features in ductal proliferative lesions of the breast,” The Journal of Pathology, vol. 181, no. 4, pp. 374–380, 1997.
- C. Bilgin, C. Demir, C. Nagi, and B. Yener, “Cell-graph mining for breast tissue modeling and classification,” in Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '07), pp. 5311–5314, 2007.
- A. N. Basavanhally, S. Ganesan, S. Agner et al., “Computerized image-based detection and grading of lymphocytic infiltration in HER2+ breast cancer histopathology,” IEEE Transactions on Biomedical Engineering, vol. 57, no. 3, pp. 642–653, 2010.
- S. Doyle, S. Agner, A. Madabhushi, M. Feldman, and J. Tomaszewski, “Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features,” in Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI '08), pp. 496–499, May 2008.
- W. H. Wolberg, W. N. Street, D. M. Heisey, and O. L. Mangasarian, “Computer-derived nuclear features distinguish malignant from benign breast cytology,” Human Pathology, vol. 26, no. 7, pp. 792–796, 1995.
- R. Fernandez-Gonzalez, T. Deschamps, A. Idica, R. Malladi, and C. Ortiz de Solorzano, “Automatic segmentation of histological structures in mammary gland tissue sections,” Journal of Biomedical Optics, vol. 9, no. 3, pp. 444–453, 2004.
- W. H. Wolberg, W. N. Street, and O. L. Mangasarian, “Breast cytology diagnosis with digital image analysis,” Analytical and Quantitative Cytology and Histology, vol. 15, no. 6, pp. 396–404, 1993.
- A. N. Esgiar, R. N. G. Naguib, B. S. Sharif, M. K. Bennett, and A. Murray, “Fractal analysis in the detection of colonic cancer images,” IEEE Transactions on Information Technology in Biomedicine, vol. 6, no. 1, pp. 54–58, 2002.
- H. K. Choi, T. Jarkrans, E. Bengtsson et al., “Image analysis based grading of bladder carcinoma. Comparison of object, texture and graph based methods and their reproducibility,” Analytical Cellular Pathology, vol. 15, no. 1, pp. 1–18, 1997.
- M. N. Gurcan, J. Kong, O. Sertel, B. B. Cambazoglu, J. Saltz, and U. Catalyurek, “Computerized pathological image analysis for neuroblastoma prognosis,” Annual Symposium proceedings, pp. 304–308, 2007.
- O. Sertel, J. Kong, H. Shimada, U. V. Catalyurek, J. H. Saltz, and M. N. Gurcan, “Computer-aided prognosis of neuroblastoma on whole-slide images: classification of stromal development,” Pattern Recognition, vol. 42, no. 6, pp. 1093–1103, 2009.
- B. B. Cambazoglu, O. Sertel, J. Kong, J. Saltz, M. N. Gurcan, and U. V. Catalyurek, “Efficient processing of pathological images using the grid: computer-aided prognosis of neuroblastoma,” in Proceedings of the 16th International Symposium on High Performance Distributed Computing (HPDC '07), pp. 35–41, New York, NY, USA, June 2007.
- K. Belkacem-Boussaid, M. Pennell, G. Lozanski, A. Shana'ah, and M. N. Gurcan, “Effect of pathologist agreement on evaluating a computer-aided assisted system: recognizing centroblast cells in follicular lymphoma cases,” in Proceedings of the 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI '10), pp. 1411–1414, April 2010.
- O. Sertel, G. Lozanski, A. Shanáah, and M. N. Gurcan, “Computer-aided detection of centroblasts for follicular lymphoma grading using adaptive likelihood-based cell segmentation,” IEEE Transactions on Biomedical Engineering, vol. 57, no. 10, pp. 2613–2616, 2010.
- J. W. Song, J. H. Lee, T. S. Park, S. J. Chun, and J. H. Choi, “Mucinous cystadenoma classification system using automated epithelial tissue detection,” in Proceedings of the International Conference on Machine Vision, 2010.
- G. Klöppel, R. H. Hruban, D. S. Longnecker, G. Adler, S. E. Kern, and T. J. Partanen, “Ductal adenocarcinoma of the pancreas,” in Word Health Organization Classification of Tumours. Pathology and Genetics of Tumours of the Digestive System, International Agency for Research on Cancer, Lyon, France, 3rd edition, 2000.
- R. H. Hruban, P. Boffetta, C. Iacobuzio-Donahue et al., “Ductal adenocarcinoma of the pancreas,” in WHO Classification of Tumours of the Digestive System, vol. 3, pp. 217–224, International Agency for Research on Cancer, Lyon, France, 4th edition, 2010.
- C. J. Yeo, J. L. Cameron, T. A. Sohn et al., “Six hundred fifty consecutive pancreaticoduodenectomies in the 1990s: pathology, complications, and outcomes,” Annals of Surgery, vol. 226, no. 3, pp. 248–260, 1997.
- G. Klöppel, G. Lingenthal, M. Von Bulow, and H. F. Kern, “Histological and find structural features of pancreatic ductal adenocarcinoma in relation to growth and prognosis: studies in xenografted tumours and clinico-histopathological correlation in a series of 75 cases,” Histopathology, vol. 9, no. 8, pp. 841–856, 1985.
- J. Lüttges, S. Schemm, I. Vogel, J. Hedderich, B. Kremer, and G. Klöppel, “The grade of pancreatic ductal carcinoma is an independent prognostic factor and is superior to the immunohistochemical assessment of proliferation,” Journal of Pathology, vol. 191, no. 2, pp. 154–161, 2000.
- R. Adams and L. Bischof, “Seeded region growing,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 6, pp. 641–647, 1994.
- K. Rodenacker and E. Bengtsson, “A feature set for cytometry on digitized microscopic images,” Analytical Cellular Pathology, vol. 25, no. 1, pp. 1–36, 2003.
- P. K. Sahoo, S. Soltani, A. K. C. Wong, and Y. C. Chen, “A survey of thresholding techniques,” Computer Vision, Graphics, and Image Processing, vol. 41, no. 2, pp. 233–260, 1988.
- N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans Syst Man Cybern, vol. 9, no. 1, pp. 62–66, 1979.
- A. Z. Chitade and S. K. Katiyar, “Color based image segmentation using K-means clustering,” International Journal of Engineering Science and Technology, vol. 2, no. 10, pp. 5319–5325, 2010.
- R. C. González and R. E. Woods, Digital Image Processing, Prentice Hall, New York, NY, USA, 2008.
- C. Pan, C.-X. Zheng, and H.-J. Wang, “Robust color image segmentation based on mean shift and marker-controlled watershed algorithm,” in Proceedings of the International Conference on Machine Learning and Cybernetics, vol. 5, pp. 2752–2756, November 2003.
- R. J. Hyndman and A. B. Koehler, “Another look at measures of forecast accuracy,” International Journal of Forecasting, vol. 22, no. 4, pp. 679–688, 2006.
- F.-L. Chung, T. C. Fu, V. Ng, and R. W. P. Luk, “An evolutionary approach to pattern-based time series segmentation,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 5, pp. 471–489, 2004.
- K.-P. Chan and A. W. Fu, “Efficient time series matching by wavelets,” in Proceedings of the 15th International Conference on Data Engineering (ICDE '99), pp. 126–133, 1999.
- M. Kosmahl, U. Pauser, M. Anlauf, and G. Klöppel, “Pancreatic ductal adenocarcinomas with cystic features: neither rare nor uniform,” Modern Pathology, vol. 18, no. 9, pp. 1157–1164, 2005.
- “ScanScope CS-Asperio,” 2012, http://www.aperio.com/lifescience/capture/cs.
- M. Kallergi, “Evaluation strategies for medical-image analysis and processing methodologies,” in Medical Image Analysis Methods, L. Costaridou, Ed., vol. 18, CRC Press, New York, NY, USA, 2005.
- J. K. Udupa, V. R. LeBlanc, H. Schmidt et al., “A methodology for evaluating image segmentation algorithms,” in Proceedings of the Medical Imaging 2002: Image Processing, pp. 266–277, February 2002.
- T. Allen, Particle Size Measurement: Volume 1: Powder Sampling and Particle Size Measurement, Springer, New York, NY, USA, 1996.
- W. Rasband, “ImageJ, 2012-1997,” 2012, http://imagej.nih.gov/ij/.
- V. N. Vapnik, Statistical Learning Theory, Wiley-Interscience, New York, NY, USA, 1st edition, 1998.
- C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121–167, 1998.
- T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition, Springer, New York, NY, USA, 2nd edition, 2009.
- C.-C. Chang and C.-J. Lin, “LIBSVM:a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, pp. 1–27, 2011.
- D. L. Verbyla and J. A. Litvaitis, “Resampling methods for evaluating classification accuracy of wildlife habitat models,” Environmental Management, vol. 13, no. 6, pp. 783–787, 1989.
- H. J. Adér, G. J. Mellenbergh, and D. J. Hand, Advising on Research Methods: a Consultant's Companion, Johannes van Kessel, Huizen, Netherlands, 2008.
- J. A. Swets, “Measuring the accuracy of diagnostic systems,” Science, vol. 240, no. 4857, pp. 1285–1293, 1988.
- M. Greiner, D. Pfeiffer, and R. D. Smith, “Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests,” Preventive Veterinary Medicine, vol. 45, no. 1-2, pp. 23–41, 2000.
- L. J. Williams and H. Abdi, “Fisher's least significance difference (LSD) test,” in Encyclopedia of Research Design, pp. 491–494, SAGE, Thousand Oaks, Calif, USA, 1st edition, 2010.