Polyp Shape Recovery using Vascular Border from Single Colonoscopy Image

The shape and size of a colonic polyp is a biomarker that correlates with its risk of malignancy and guides its clinical management. It is the most accurate method for detecting polyps of all sizes, and it allows biopsy of lesions and resection of most polyps, and it is considered nowadays as the gold standard for colon screening. However, there are still open challenges to overcome, such as the reduction of the missing rate. Colonoscopy images usually consist of nonrigid objects such as a polyp, and no approaches have been proposed to recovery shape and absolute size from a single image. Hence, it is a challenging topic to reconstruct polyp shape using computer vision technique. This paper proposes a polyp shape retrieval method based on Shape from Shading (SFS), and this research contributes to mitigating constraint for applying SFS to the single colonoscopy image using vascular border information. Experiments confirmed that the proposed method recovered approximate polyp shapes.

[1]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[2]  S. Tchoulack,et al.  A video stream processor for real-time detection and correction of specular reflections in endoscopic images , 2008, 2008 Joint 6th International IEEE Northeast Workshop on Circuits and Systems and TAISA Conference.

[3]  Yuji Iwahori,et al.  3D Shape Recovery from Endoscope Image Based on Both Photometric and Geometric Constraints , 2015 .

[4]  C. Mulrow,et al.  Colorectal cancer screening: clinical guidelines and rationale. , 1997, Gastroenterology.

[5]  T. W. Allen,et al.  Guide to clinical preventive services. Report of the US Preventive Services Task Force. , 1991, The Journal of the American Osteopathic Association.

[6]  J. Bond,et al.  Polyp Guideline: Diagnosis, Treatment, and Surveillance for Patients with Nonfamilial Colorectal Polyps* , 1993, Annals of Internal Medicine.

[7]  Narendra Ahuja,et al.  Efficient and Robust Specular Highlight Removal , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Yuji Iwahori,et al.  3D shape recovery of polyp using two light sources endoscope , 2016, 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS).

[9]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  T. Byers,et al.  American Cancer Society guidelines for screening and surveillance for early detection of colorectal polyps and cancer: Update 1997 , 1997 .

[11]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[12]  Steven A. Shafer,et al.  Using color to separate reflection components , 1985 .

[13]  Koen E. A. van de Sande,et al.  Selective Search for Object Recognition , 2013, International Journal of Computer Vision.

[14]  Cristian Sminchisescu,et al.  CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Janne Heikkilä,et al.  A four-step camera calibration procedure with implicit image correction , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Narendra Ahuja,et al.  Real-Time Specular Highlight Removal Using Bilateral Filtering , 2010, ECCV.

[17]  F. E. Grubbs.  Sample Criteria for Testing Outlying Observations , 1950 .

[18]  Berthold K. P. Horn.  Obtaining shape from shading information , 1989 .

[19]  Branislav Jaramaz,et al.  A Multi-Image Shape-from-Shading Framework for Near-Lighting Perspective Endoscopes , 2009, International Journal of Computer Vision.