基于新型特征和特征袋模型的内窥镜大肠病变辅助诊断

doi:10.3969/j.issn.0258-8021.2020.04.003

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (6330 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Polyps and ulcerative colitis (UC) are common diseases of the large intestine. A large number of images generated during the endoscopy. To improve the diagnosis efficiency and accuracy,it is necessary to investigate the computer aided diagnosis system for the detection of colonscopy diseases. Considering the characteristics of endoscopy image,a novel color texture feature called histogram of local color difference was proposed in this paper,and used as the endoscopic image description by extracting local color difference histogram (LCDH) feature for each image patch in the feature extraction step. Combining with the bag-of-features model,local features were transformed into a higher-level image representation by using local-constrained linear coding and spatial pyramid matching. At last,SVM was used for classification. public Kvasir datasets were analyzed,and inferior images were deleted from original data and 5-fold cross validation was adopted. In the first experiment,the classification accuracy,sensitivity and specificity reached 97.88%,98.00% and 97.75% respectively for 800 normal samples and 800 disease samples;in the second experiment,1000 normal samples,770 polyp samples and 780 UC samples were adopted for multiple classification,the recognition rate of polyp and UC was 92.34% and 93.08% respectively. Experimental results showed that the proposed method possessed advantages both in accuracy and efficiency compared with the traditional method,which would be helpful for clinical diagnosis of intestinal diseases.

Key words： endoscopy image polyps ulcerative colitis feature extraction bag-of-features

Received: 24 December 2019

PACS:

R318

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Yang Jianjun
	Chang Liping
	Li Sheng
	Zhu Tingwei
	He Xiongxiong

Cite this article:

Yang Jianjun,Chang Liping,Li Sheng, et al. Assisted Diagnosis of Endoscopy Large Intestine Disease Based on Novel Feature and Bag of Feature Model[J]. Chinese Journal of Biomedical Engineering, 2020, 39(4): 404-412.

URL:

http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2020.04.003 OR http://cjbme.csbme.org/EN/Y2020/V39/I4/404

[1] 于晓娜.大肠息肉癌变相关危险因素分析[D].重庆:第三军医大学,2015.
[2] 李景南.溃疡性结肠炎相关性结肠癌临床特点和癌变机制[C]//中华医学会第九次全国消化系统疾病学术会议.广州:中华医学会,2010:65-67.
[3] Siegel RL,Miller KD,Jemal A.Cancer statistics,2019[J].CA:A Cancer Journal for Clinicians,2019,69(1):7-34.
[4] Leufkens AM,Van Oijen MGH,Vleggaar FP,et al.Factors influencing the miss rate of polyps in a back-to-back colonoscopy study[J].Endoscopy,2012,44(05):470-475.
[5] 王莉君,高孝忠.结肠镜检查中结肠息肉漏检率的临床研究[J].中华胃肠内镜电子杂志,2017,4(1):26-28.
[6] 杨红,张慧敏,金梦,等.溃疡性结肠炎诊断与鉴别诊断要点解析[J].临床荟萃,2016,31(8):813-816.
[7] Charfi S,Ansari ME.Computer-aided diagnosis system for colon abnormalities detection in wireless capsule endoscopy images[J].Multimedia Tools and Applications,2017,77(3):4047-4064.
[8] 邹月娴,霍佳森,刘吉,等.基于特征融合的WCE图像检索[J].计算机科学,2013,40(11A):320-324.
[9] Cong Yang,Wang Shuai,Liu Ji,et al.Deep sparse feature selection for computer aided endoscopy diagnosis[J].Pattern Recognition,2015,48(3):907-917.
[10] Liaqat A,Khan MA,Shah JH,et al.Automated ulcer and bleeding classification from wce images using multiple features fusion and selection[J].Journal of Mechanics in Medicine and Biology,2018,18(4):1850038.
[11] Bernal J,Sanchez J,Vilarino F,et al.Towards automatic polyp detection with a polyp appearance model[J].Pattern Recognition,2012,45(9):3166-3182.
[12] Bernal J,Sanchez J,Fernandez-Esparrach G,et al.WM-DOVA maps for accurate polyp highlighting in colonoscopy:validation vs.saliency maps from physicians[J].Computerized Medical Imaging and Graphics,2015,43:99-111.
[13] Bernal J,Tajbakhsh N,Sanchez FJ,et al.Comparative validation of polyp detection methods in video colonoscopy:results from the miccai 2015 endoscopic vision challenge[J].IEEE Transactions on Medical Imaging,2017,36(6):1231-1249.
[14] Shin Y,Balasingham I.Comparison of hand-craft feature based SVM and CNN based deep learning framework for automatic polyp classification[C]//2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).Jeju Island:IEEE,2017:3277-3280.
[15] Shin Y,Qadir HA,Aabakken L,et al.Automatic colon polyp detection using region based deep cnn and post learning approaches[J].IEEE Access,2018,6:40950-40962.
[16] 范姗慧,刘士臣,曹鹗,等.无线胶囊内窥镜图像小肠息肉的自动识别[J].中国生物医学工程学报,2019,38(5):522-532.
[17] Lazebnik S,Schmid C,Ponce J.Beyond bags of features:spatial pyramid matching for recognizing natural scene categories[C]//2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR′06).New York:IEEE,2006:2169-2178.
[18] 汪宇玲,黎明,李军华,等.基于BoF模型的多特征融合纹理图像分类[J].北京航空航天大学学报,2018,44(9):1869-1877.
[19] Yuan Yixuan,Li Baopu,Meng MQH.Improved bag of feature for automatic polyp detection in wireless capsule endoscopy images[J].IEEE Transactions on Automation Science and Engineering,2015,13(2):529-535.
[20] Zhang Ruijie,Shen Jian,Wei Fushan,et al.Medical image classification based on multi-scale non-negative sparse coding[J].Artificial Intelligence in Medicine,2017,83:44-51.
[21] Wang Jiangang,Li Jun,Yau WY,et al.Boosting dense SIFT descriptors and shape contexts of face images for gender recognition[C]//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.San Francisco:IEEE,2010:96-102.
[22] Dalal N,Triggs B.Histograms of oriented gradients for human detection[C]//2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR′05).San Diego:IEEE,2005:886-893.
[23] Csurka G,Dance C,Fan L,et al.Visual categorization with bags of keypoints[C]//8th European Conference on Computer Vision,ECCV.Prague:Springer,2004:1-2.
[24] Yang Jianchao,Yu Kai,Gong Yihong,et al.Linear spatial pyramid matching using sparse coding for image classification[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition.Miami:IEEE,2009:1794-1801.
[25] Yu Kai,Zhang Tong,Gong Yihong.Nonlinear learning using local coordinate coding[C]//Advances in Neural Information Processing Systems.Vancouver:NIPS,2009:2223-2231.
[26] Wang Jinjun,Yang Jianchao,Yu Kai,et al.Locality-constrained linear coding for image classification[C]//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.San Francisco:IEEE,2010:3360-3367.
[27] 刘培娜,刘国军,郭茂祖,等.非负局部约束线性编码图像分类算法[J].自动化学报,2015,41(7):1235-1243.
[28] Yuan Yixuan,Li Baopu,Meng MQH.WCE abnormality detection based on saliency and adaptive locality-constrained linear coding[J].IEEE Transactions on Automation Science and Engineering,2017,14(1):149-159.
[29] Bernal J,Sánchez J,Vilarino F.Impact of image preprocessing methods on polyp localization in colonoscopy frames[C]//2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).Osaka:IEEE,2013:7350-7354.
[30] Malviya AV,Ladhake SA.Pixel based image forensic technique for copy-move forgery detection using auto color correlogram[J].Procedia Computer Science,2016,79:383-390.