Volumetric texture features from higher-order images for diagnosis of colon lesions via CT colonography

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• 作者：Bowen Song (1) (2)
  Guopeng Zhang (3)
  Hongbing Lu (3)
  Huafeng Wang (1)
  Wei Zhu (2)
  Perry J. Pickhardt (4)
  Zhengrong Liang (1)
  
• 关键词：CT colonography ; Colorectal lesions ; Texture feature ; Textural biomarker ; Gradient ; Curvature ; Computer ; aided diagnosis
• 刊名：International Journal of Computer Assisted Radiology and Surgery
• 出版年：2014
• 出版时间：November 2014
• 年：2014
• 卷：9
• 期：6
• 页码：1021-1031
• 全文大小：1,359 KB
• 参考文献：1. American Cancer Society (2012) Cancer facts & figures 2012. American Cancer Society, Atlanta, GA
  2. Eddy D (1990) Screening for colorectal cancer. Ann Intern Med 113:373鈥?84 CrossRef
  3. Gluecker T, Johnson C, Harmsen W, Offord K, Harris A, Wilson L, Ahlquist D (2003) Colorectal cancer screening with CT colonography, colonoscopy, and double-contrast barium enema examination: prospective assessment of patient perceptions and preferences. Radiology 227(2):378鈥?84 CrossRef
  4. Summers R, Beaulieu C, Pusanik L, Malley J, Jeffrey R, Glazer D, Napel S (2000) Automated polyp detector for CT colonography: feasibility study. Radiology 216(1):284鈥?90 CrossRef
  5. Yoshida H, Nappi J (2001) Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps. IEEE Trans Med Imaging 20(12):1261鈥?274 CrossRef
  6. Wang Z, Liang Z, Li L, Li X, Li B, Anderson J, Harrington D (2005) Reduction of false positives by internal features for polyp detection in CT-based virtual colonoscopy. Med Phys 32(12):3602鈥?616 CrossRef
  7. Zhu H, Fan Y, Lu H, Liang Z (2010) Improving initial polyp candidate extraction for CT colonography. Phys Med Biol 55:2087鈥?102 CrossRef
  8. Zhu H, Fan Y, Lu H, Liang Z (2011) Improved curvature estimation for computer-aided detection of colonic polyps in CT colonography. Acad Radiol 18(8):1024鈥?034 CrossRef
  9. Pickhardt P, Kim D (2009) Colorectal cancer screening with CT colonography: key concepts regarding polyp prevalence, size, histology, morphology, and natural history. AJR Am J Roentgenol 193(1):40鈥?6. doi:10.2214/AJR.08.1709 CrossRef
  10. Kumar V, Abbas A, Fausto N, Aster J, Perkins J (2010) Polyps. In: Robbins SL, Cotran RS (eds) Pathologic basis of disease, 8th edn. Saunders/Elsevier, Philadelphia, PA. ISBN 978-1-4160-3121-5
  11. Winawer S, Zauber A (2002) The advanced adenoma as the primary target of screening. Gastrointest Endosc Clin N Am 12:1鈥? CrossRef
  12. Mills S, Carter D, Greenson J, Reuter V, Stoler M et al. (2010) Sternberg鈥檚 diagnostic surgical pathology, 5th edn. Lippincott Williams & Wilkins
  13. Castellano G, Bonilha L, Li L, Cendes F (2004) Texture analysis of medical images. Clin Radiol 59(12):1061鈥?069 CrossRef
  14. Haralick R, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610鈥?21 CrossRef
  15. Ji Q, Engel J, Craine E (2000) Texture analysis for classification of cervix lesions. IEEE Trans Med Imaging 19(11):1144鈥?149 CrossRef
  16. Philips C, Li D, Raicu D, Furst J (2008) Directional invariance of co-occurrence matrices within the liver. In: International conference on biocomputation, bioinformatics, and biomedical technologies
  17. Fiori M, Mus茅 P, Aguirre S, Sapiro G (2010) Automatic colon polyp flagging via gometric and texture features. Conf Proc IEEE Eng Med Biol Soc 1:3170鈥?173. doi:10.1109/IEMBS.2010.5627185
  18. Yao J, Dwyer A, Mollura D (2011) Computer-aided diagnosis of pulmonary infections using texture analysis and support vector machine classification. Acad Radiol 18(3):306鈥?14 CrossRef
  19. Rahmim A, Couhglin J, Gonzalez M, Endres C, Zhou Y, Wong D, Wahl R, Sossi V, Pomper M (2012) Novel parametric PET image quantification using texture and shape analysis. In: Proceedings of the IEEE nuclear science symposium and medical imaging conference
  20. Ng F, Ganeshan B, Kozarski R, Miles K, Goh V (2013) Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology 266(1):177鈥?84 CrossRef
  21. Suzuki K et al. (2012) Computer-aided Diagnosis (CADx) for distinguishing neoplastic from non-neoplastic lesions toward CT colonography (CTC) beyond detection. In: Program of scientific assembly and annual meeting of radiological society of North America (RSNA), LL-GIS-TU5C, November 2012
  22. Zhang G, Song B, Zhu H, Liang Z (2012) Computer-aided diagnosis in CT colonography based on bi-labeled classifier. In: The 26th international congress and exhibition on computer assisted radiology and surgery (CARS). International Journal of CARS, vol 7(Suppl), p S274
  23. Engel K (2006) Real-time volume graphics. A K Peters, Wellesley, MA
  24. Monga O, Benayoun S (1995) Using partial derivatives of 3D images to extract typical surface features. Comput Vis Image Underst 61(2):171鈥?89 CrossRef
  25. van Wijk C, van Ravesteijn V, Vos F, van Vliet L (2010) Detection and segmentation of colonic polyps on implicit isosurfaces by second principal curvature flow. IEEE Trans Med Imaging 29(3):688鈥?98 CrossRef
  26. Deriche R (1987) Using Canny鈥檚 criteria to derive a recursively implemented optimal edge detector. Int J Comput Vis 1(2):167鈥?87
  27. Haralick R, Shapiro L (1993) Computer and robot vision. Addison-Wesley Publishing Company, Reading, MA 1:453鈥?08
  28. Tesar L, Smutek D (2004) Bayesian classification of sonograms of thyroid gland based on Gaussian mixtures. In: Proceedings of Norvegian conference on image processing and pattern recognition, NOBIM, pp 36鈥?0
  29. Tesar L, Smutek D, Shimizu A, Kobatake H (2007) 3D extension of Haralick texture features for medical image analysis. In: SPPR 2007 proceedings of the fourth conference on IASTED international conference, pp 350鈥?55
  30. Tesar L, Shimizu A, Smutek D, Kobatake H, Nawano S (2008) Medical image analysis of 3D CT images based on extension of Haralick texture features. Comput Med Imaging Graph 32(6):513鈥?20 CrossRef
  31. Liang Z, Cohen H, Posniak E, Fiore E, Wang Z, Li B, Anderson J, Harrington D (2008) Texture-based CAD improves diagnosis for low-dose CT colonography. In: Proceedings of the SPIE medical imaging CD-ROM
  32. Showalter C, Clymer B, Richmond B, Powell K (2006) Three-dimensional texture analysis of cancellous bone cores evaluated at clinical CT resolutions. Osteoporos Int 17:259鈥?66 CrossRef
  33. Pickhardt P, Kim D, Pooler B, Hinshaw J, Barlow D, Jensen D, Reichelderfer M, Cash B (2013) Assessment of volumetric growth rates of small colorectal polyps with CT colonography: a longitudinal study of natural history. Lancet Oncol 14(8):711鈥?20 CrossRef
  34. Li L, Chen D, Lakare S, Kreeger K, Bitter I, Kaufman A, Wax M, Djuric P, Liang Z (2002) An image segmentation approach to extract colon lumen through colonic material tagging and hidden Markov random field model for virtual colonography. In: Proceedings of the SPIE medical imaging vol 4683, pp 406鈥?11
  35. Wang S, Li L, Cohen H, Mankes S, Chen J, Liang Z (2008) An EM approach to MAP solution of segmenting tissue mixture percentages with application to CT-based virtual colonoscopy. Med Phys 35(12):5787鈥?798 CrossRef
  36. Pickhardt P, Choi J, Hwang I, Butler J, Puckett M, Hildebrandt H, Wong R, Nugent P, Mysliwiec P, Schindler W (2003) Computed tomographic virtual colonoscopy to screen for colorectal neoplasia in asymptomatic adults. N Engl J Med 349:2191鈥?200 CrossRef
  37. American College of Radiology (2005) ACR practice guideline for the performance of computed tomography (CT) colonography in adults. ACR Pract Guidel 29:295鈥?98
  38. Chang C, Lin C (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(27):1鈥?7 CrossRef
  39. Song B, Zhang G, Zhu W, Liang Z (2014) ROC operating point selection for classification of imbalanced data with application to computer-aided polyp detection in CT colonography. Int J Comput Assist Radiol Surg 9(1):79鈥?9 CrossRef
  40. Fidler J, Johnson C, MacCarty R, Welch T, Hara A, Harmsen W (2002) Detection of flat lesions in the colon with CT colonography. Abdom Imaging 27:292鈥?00 CrossRef
  41. Pickhardt P, Choi J, Hwang I, Schindler W (2004) Nonadenomatous polyps at CT colonography: prevalence, size distribution, and detection rates. Radiology 232:784鈥?90
  42. Pickhardt P, Kim D, Robbins J (2010) Flat (nonpolypoid) colorectal lesions identified at CT colonography in a US screening population. Acad Radiol 17:784鈥?0 CrossRef
  43. Shah J, Hynan L, Rockey D (2009) Management of small polyps detected by screening CT colonography: patient and physician preferences. Am J Med 122(7):687.e1-9
  44. Summers R (2010) Polyp size measurement at CT Colonography: what do we know and what do we need to know? Radiology 255(3):707鈥?20 CrossRef
  45. Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27:861鈥?74 CrossRef
  46. Suzuki K. et al. (2012) Observer performance study: effect of computer-aided diagnosis (CADx) on the performance of radiologists in distinguishing neoplastic from non-neoplastic lesions in CT colonography (CTC). In: Program of scientific assembly and annual meeting of radiological society of North America (RANA), SSA 07鈥?8, November, 2012
  
• 作者单位：Bowen Song (1) (2)
  Guopeng Zhang (3)
  Hongbing Lu (3)
  Huafeng Wang (1)
  Wei Zhu (2)
  Perry J. Pickhardt (4)
  Zhengrong Liang (1)
  
  1. Department of Radiology, Stony Brook University, Stony Brook, NY聽, 11790, USA
  2. Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY聽, 11790, USA
  3. Department of Biomedical Engineering, Fourth Military Medical University, Xi鈥檃n聽, 710032, Shaanxi, China
  4. Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI聽, 53792, USA
  
• ISSN：1861-6429

文摘

Purpose Differentiation of colon lesions according to underlying pathology, e.g., neoplastic and non-neoplastic lesions, is of fundamental importance for patient management. Image intensity-based textural features have been recognized as useful biomarker for the differentiation task. In this paper, we introduce texture features from higher-order images, i.e., gradient and curvature images, beyond the intensity image, for that task. Methods Based on the Haralick texture analysis method, we introduce a virtual pathological model to explore the utility of texture features from high-order differentiations, i.e., gradient and curvature, of the image intensity distribution. The texture features were validated on a database consisting of 148 colon lesions, of which 35 are non-neoplastic lesions, using the support vector machine classifier and the merit of area under the curve (AUC) of the receiver operating characteristics. Results The AUC of classification was improved from 0.74 (using the image intensity alone) to 0.85 (by also considering the gradient and curvature images) in differentiating the neoplastic lesions from non-neoplastic ones, e.g., hyperplastic polyps from tubular adenomas, tubulovillous adenomas and adenocarcinomas. Conclusions The experimental results demonstrated that texture features from higher-order images can significantly improve the classification accuracy in pathological differentiation of colorectal lesions. The gain in differentiation capability shall increase the potential of computed tomography colonography for colorectal cancer screening by not only detecting polyps but also classifying them for optimal polyp management for the best outcome in personalized medicine.