Computer-aided classification of colorectal polyps based on vascular patterns: a pilot study

Background and study aims: Recent studies have shown that narrow-band imaging (NBI) is a powerful diagnostic tool for differentiating between neoplastic and nonneoplastic colorectal polyps. The aim of the present study was to develop and evaluate a computer-based method for automated classification of colorectal polyps on the basis of vascularization features.

Patients and methods: In a prospective pilot study with 128 patients who were undergoing zoom NBI colonoscopy, 209 detected polyps were visualized and subsequently removed for histological analysis. The proposed computer-based method consists of image preprocessing, vessel segmentation, feature extraction, and classification. The results of the automated classification were compared to those of human observers blinded to the histological gold standard.

Results: Consensus decision between the human observers resulted in a sensitivity of 93.8 % and a specificity of 85.7 %. A “safe” decision, i. e., classifying polyps as neoplastic in cases when there was interobserver discrepancy, yielded a sensitivity of 96.9 % and a specificity of 71.4 %. The overall correct classification rates were 91.9 % for the consensus decision and 90.9 % for the safe decision. With ideal settings the computer-based approach achieved a sensitivity of approximately 90 % and a specificity of approximately 70 %, while the overall correct classification rate was 85.3 %. The computer-based classification showed a specificity of 61.2 % when a sensitivity of 93.8 % was selected, and a 53.1 % specificity with a sensitivity of 96.9 %.

Conclusions: Automated classification of colonic polyps on the basis of NBI vascularization features is feasible, but classification by observers is still superior. Further research is needed to clarify whether the performance of the automated classification system can be improved.

References

• 1 Vogelstein B, Fearon E R, Hamilton S R. et al . Genetic alterations during colorectal-tumor development.  N Engl J Med. 1988;  319 525-532
• 2 Winawer S J, Zauber A G, Ho M N. et al . Prevention of colorectal cancer by colonoscopic polypectomy. The National Polyp Study Workgroup.  N Engl J Med. 1993;  329 1977-1981
• 3 Heldwein W, Dollhopf M, Rösch T. et al . The Munich Polypectomy Study (MUPS): prospective analysis of complications and risk factors in 4000 colonic snare polypectomies.  Endoscopy. 2005;  37 1116-1122
• 4 Kudo S, Tamura S, Nakajima T. et al . Diagnosis of colorectal tumorous lesions by magnifying endoscopy.  Gastrointest Endosc. 1996;  44 8-14
• 5 Gono K, Obi T, Yamaguchi M. et al . Appearance of enhanced tissue features in narrow-band endoscopic imaging.  J Biomed Opt. 2004;  9 568-577
• 6 Tischendorf J JW, Wasmuth H E, Koch A. et al . Value of magnifying chromoendoscopy and narrow band imaging (NBI) in classifying colorectal polyps: a prospective controlled study.  Endoscopy. 2007;  39 1092-1096
• 7 Su M Y, Hsu C M, Ho Y P. et al . Comparative study of conventional colonoscopy, chromoendoscopy, and narrow-band imaging systems in differential diagnosis of neoplastic and nonneoplastic colonic polyps.  Am J Gastroenterol. 2006;  101 2711-2716
• 8 Chiu H M, Chang C Y, Chen C C. et al . A prospective comparative study of narrow-band imaging, chromoendoscopy, and conventional colonoscopy in the diagnosis of colorectal neoplasia.  Gut. 2007;  56 373-379
• 9 Rastogi A, Bansal A, Wani S. et al . Narrow-band imaging colonoscopy – a pilot feasibility study for the detection of polyps and correlation of surface patterns with polyp histological diagnosis.  Gastrointest Endosc. 2008;  67 280-286
• 10 Risau W. Mechanisms of angiogenesis.  Nature. 1997;  386 671-674
• 11 Rogart J N, Jain D, Siddiqui U D. et al . Narrow-band imaging without high magnification to differentiate polyps during real-time colonoscopy: improvement with experience.  Gastrointest Endosc. 2008;  68 1136-1145
• 12 Sikka S, Ringold D A, Jonnalagadda S. et al . Comparison of white light and narrow band high definition images in predicting colon polyp histology, using standard colonoscopes without optical magnification.  Endoscopy. 2008;  40 818-822
• 13 Gross S, Stehle T, Behrens A. et al . A comparison of blood vessel features and local binary patterns for colorectal polyp classification.  Proc SPIE Medical Imaging. 2009;  72602Q 1-8
• 14 Stehle T, Auer R, Gross S. et al . Classification of colon polyps in NBI endoscopy using vascularization features.  Proc SPIE Medical Imaging. 2009;  72602S 1-12
• 15 Stehle T. Removal of specular reflections in endoscopic images.  Polytechnica J Adv Eng. 2006;  46 32-36
• 16 Hajer J, Kamel H, Noureddine E. Blood vessels segmentation in retina image using mathematical morphology and the STFT analysis.  Inf Commun Technol. 2006;  2 1130-1134
• 17 Kovesi P. Image features from phase congruency.  Videre J Comput Vis Res. 1999;  1(3) 1-27
• 18 Sethian J A. A fast marching level set method for monotonically advancing fronts.  Proc Natl Acad Sci USA. 1996;  93 1591-1595
• 19 Cortes C, Vapnik V. Support vector networks.  Machine Learning. 1995;  20(3) 273-297
• 20 Vapnik V. Statistical learning theory. New York; Wiley 1998
• 21 Christianini N, Shawe-Taylor J. An introduction to support vector machines and other kernel-based methods. Cambridge; Cambridge University Press 2000
• 22 Martens H A, Dardenne P. Validation and verification of regression in small data sets.  Chemometrics Intelligent Lab Syst. 1998;  44 99-121
• 23 Atkin W S, Morson B C, Cuzick J. Long-term risk of colorectal cancer after excision of rectosigmoid adenomas.  N Engl J Med. 1992;  326 652-662
• 24 Lieberman D A, Weiss D G, Harford W V. et al . Five-year colon surveillance after screening colonoscopy.  Gastroenterology. 2007;  33 1077-1085
• 25 Gross S, Kennel M, Stehle T. et al . Polyp segmentation in NBI colonoscopy.  Bildverarbeitung für die Medizin. 2009;  2009 252-256