zutoTztit aolya Rxtognition froT tolonostoay ITzgxs izsxK on izg of Visuzl WorKs

CSME /
: 69-81
DOI:10.6703/IJASE.201906_16(1).069

zutoTztit aolya Rxtognition froT tolonostoay ITzgxs izsxK on izg of Visuzl WorKs

ZAx Guo^a, Xin ZAu^a, Qin Li^a, DaiTi NxPoto^b, DaiPuTx TaTayanagi^b, PaPato Aizawa^b, NoriyuTi IPoAata^b, TxniZAi Utano^b, TxnPuTx TuPaPoto^b, PAungo xndo^b and TazutoPo TogaPAi^b
^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.

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*Corresponding author; e-mail: zhuxin@u-aizu.ac.jp

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