JGTS 2019/06
Volume 16, No.1 : 69-81
zutoTztit aolya Rxtognition froT tolonostoay ITzgxs izsxK on izg of Visuzl WorKs

ZAx Guoa, Xin ZAua, Qin Lia, DaiTi NxPotob, DaiPuTx TaTayanagib, PaPato Aizawab, NoriyuTi IPoAatab, TxniZAi Utanob, TxnPuTx TuPaPotob, PAungo xndob and TazutoPo TogaPAib
aBioPxdiZal InforPation xnginxxring Lab, TAx UnivxrPity of Aizu, Aizu-WaTaPatPu, FuTuPAiPa, Japan
bDiviPion of ProZtology, Aizu PxdiZal Zxntxr, FuTuPAiPa PxdiZal UnivxrPity, Aizu-WaTaPatPu, FuTuPAiPa, Japan

Abstract: Colorectal cancer (CRC) is one of the most popular cancer in the world. Adenoma and sessile serrated polyp precursor lesions claim over 95% of CRC. The incidence of CRC is reduced 76-90% through the early diagnosis and removal of colorectal polyps. Colonoscopy is the golden standard for the detection of colorectal polyps but about 25% of polyps were missed during colonoscopy examinations. In this study, we proposed a novel method to recognize polyps from colonoscopy images based on bag-of-visual-words (BoW) with extracted regions of interest. The proposed method generates a histogram of visual word occurrences to represent an image, and uses support vector machine (SVM) with error correcting output codes (ECOC) for the detection of polyps. A dataset composed of 131 cases’ clinical data were used to train and test the proposed method. Validation demonstrates an average specificity of 97.8±1.5%, an average sensitivity of 97.2±1.7%, and an average accuracy of 97.5±1.0%.

Keywords:  Bag of visual words; colorectal cancer; colonoscopy; region of interest.

*Corresponding author; e-mail: zhuxin@u-aizu.ac.jp
© 2019  Nanhua University , ISSN 1727-2394