Superpixels Based Segmentation and SVM Based Classification Method to Distinguish Five Diseases from Normal Regions in Wireless Capsule Endoscopy

Wireless Capsule Endoscopy (WCE) is relatively a new technology to examine the entire GI trace. During an examination, it captures more than 55,000 frames. Reviewing all these images is time-consuming and prone to human error. It has been a challenge to develop intelligent methods assisting physicians to review the frames. The WCE frames are captured in 8-bit color depths which provides enough a color range to detect abnormalities. Here, superpixel based methods are proposed to segment five diseases including: bleeding, Crohn's disease, Lymphangiectasia, Xanthoma, and Lymphoid hyperplasia. Two superpixels methods are compared to provide semantic segmentation of these prolific diseases: simple linear iterative clustering (SLIC) and quick shift (QS). The segmented superpixels were classified into two classes (normal and abnormal) by support vector machine (SVM) using texture and color features. For both superpixel methods, the accuracy, specificity, sensitivity, and precision (SLIC, QS) were around 92%, 93%, 93%, and 88%, respectively. However, SLIC was dramatically faster than QS.

[1]  Hamid Soltanian-Zadeh,et al.  Segmentation of Small Bowel Tumors in Wireless Capsule Endoscopy Using Level Set Method , 2014, 2014 IEEE 27th International Symposium on Computer-Based Medical Systems.

[2]  Jordi Vitrià,et al.  Generic Feature Learning for Wireless Capsule Endoscopy Analysis , 2016, Comput. Biol. Medicine.

[3]  Aymeric Histace,et al.  Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer , 2014, International Journal of Computer Assisted Radiology and Surgery.

[4]  智一 吉田,et al.  Efficient Graph-Based Image Segmentationを用いた圃場図自動作成手法の検討 , 2014 .

[5]  J. Barkin,et al.  Small-Bowel Tumors Detected by Wireless Capsule Endoscopy , 2007, Digestive Diseases and Sciences.

[6]  Nikolaos G. Bourbakis,et al.  Detection of Small Bowel Polyps and Ulcers in Wireless Capsule Endoscopy Videos , 2011, IEEE Transactions on Biomedical Engineering.

[7]  Jitendra Malik,et al.  Recovering human body configurations: combining segmentation and recognition , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[8]  Shih-Fu Chang,et al.  Segmentation using superpixels: A bipartite graph partitioning approach , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Alireza Talebpour,et al.  Segmentation of Crohn, Lymphangiectasia, Xanthoma, Lymphoid Hyperplasia and Stenosis diseases in WCE , 2012, MIE.

[10]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  O. Haji Maghsoudi,et al.  A Rodent Paw Tracker Using Support Vector Machine , 2016, 2016 IEEE Signal Processing in Medicine and Biology Symposium (SPMB).

[12]  Jitendra Malik,et al.  Learning a classification model for segmentation , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[13]  Yixuan Yuan,et al.  Deep learning for polyp recognition in wireless capsule endoscopy images , 2017, Medical physics.

[14]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[15]  Jung-Hwan Oh,et al.  Abnormal image detection in endoscopy videos using a filter bank and local binary patterns , 2014, Neurocomputing.

[16]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Guobing Pan,et al.  A Novel Algorithm for Color Similarity Measurement and the Application for Bleeding Detection in WCE , 2011 .

[18]  Max Q.-H. Meng,et al.  Computer-Aided Detection of Bleeding Regions for Capsule Endoscopy Images , 2009, IEEE Transactions on Biomedical Engineering.

[19]  Hamid Soltanian-Zadeh,et al.  Detection of abnormalities in wireless capsule endoscopy frames using local fuzzy patterns , 2013, 2013 20th Iranian Conference on Biomedical Engineering (ICBME).

[20]  Omid Haji Maghsoudi,et al.  A computer aided method to detect bleeding, tumor, and disease regions in Wireless Capsule Endoscopy , 2016, 2016 IEEE Signal Processing in Medicine and Biology Symposium (SPMB).

[21]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Max Q.-H. Meng,et al.  Detection of lymphangiectasia disease from wireless capsule endoscopy images with adaptive threshold , 2010, 2010 8th World Congress on Intelligent Control and Automation.

[23]  Deng Cai,et al.  Laplacian Score for Feature Selection , 2005, NIPS.

[24]  Max Q.-H. Meng,et al.  Improved Bag of Feature for Automatic Polyp Detection in Wireless Capsule Endoscopy Images , 2016, IEEE Transactions on Automation Science and Engineering.

[25]  Rami Eliakim.  Video capsule endoscopy of the small bowel , 2008 .

[26]  Stefano Soatto,et al.  Quick Shift and Kernel Methods for Mode Seeking , 2008, ECCV.

[27]  Sven J. Dickinson,et al.  TurboPixels: Fast Superpixels Using Geometric Flows , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Rozhin Penjweini,et al.  Investigating the effect of poly‐l‐lactic acid nanoparticles carrying hypericin on the flow‐biased diffusive motion of HeLa cell organelles , 2019, The Journal of pharmacy and pharmacology.

[29]  Guozheng Yan,et al.  Bleeding Detection in Wireless Capsule Endoscopy Based on Probabilistic Neural Network , 2011, Journal of Medical Systems.

[30]  Max Q.-H. Meng,et al.  Computer-aided small bowel tumor detection for capsule endoscopy , 2011, Artif. Intell. Medicine.

[31]  Jitendra Malik,et al.  Recovering human body configurations: combining segmentation and recognition , 2004, CVPR 2004.

[32]  Max Q.-H. Meng,et al.  Tumor Recognition in Wireless Capsule Endoscopy Images Using Textural Features and SVM-Based Feature Selection , 2012, IEEE Transactions on Information Technology in Biomedicine.