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

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摘要息肉和溃疡性结肠炎(溃结)是常见的大肠疾病。然而在进行内窥镜检查时,会产生大量图像。为了提高诊断效率和准确率,研究用于内窥镜大肠病变自动检测的计算机辅助诊断系统是十分必要的。考虑到内窥镜图像的特点,提出一种新型的颜色纹理特征,即局部颜色差异直方图(LCDH),并在特征提取阶段提取图像块的LCDH特征,对内窥镜图像进行表示;然后结合特征袋(Bof)模型,使用局部约束线性编码(LLC)和空间金字塔匹配(SPM)方法,将局部特征转化为更高层级的图像表示;最后,使用支持向量机(SVM)进行分类。对公开的Kvasir数据集进行实验,从原始数据中剔除部分劣质图像并进行5折交叉验证:实验1对数据集中800例正常样本和800例病变样本进行二分类,分类准确性、灵敏性和特异性分别达到97.88%,98.00%和97.75%;实验2对数据集中的1000例正常样本、770例息肉和780例UC样本进行多分类,其中对息肉和UC的识别率分别达到92.34%和93.08%。实验结果表明,所提出的方法在准确率和运行效率上均优于传统方法,能够为大肠疾病的辅助诊断提供有价值的帮助。

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	杨建军
	常丽萍
	李胜
	朱霆威
	何熊熊

关键词 ：内窥镜图像, 息肉, 溃疡性结肠炎, 特征提取, 特征袋

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

收稿日期: 2019-12-24

PACS:

R318

基金资助:浙江省自然科学基金(LY18F010023);浙江省重点研发计划项目(2020C03074);国家自然科学基金(61675183)

通讯作者: ^*E-mail:clping@zjut.edu.cn

引用本文:

杨建军, 常丽萍, 李胜, 朱霆威, 何熊熊. 基于新型特征和特征袋模型的内窥镜大肠病变辅助诊断[J]. 中国生物医学工程学报, 2020, 39(4): 404-412.
Yang Jianjun, Chang Liping, Li Sheng, Zhu Tingwei, He Xiongxiong. Assisted Diagnosis of Endoscopy Large Intestine Disease Based on Novel Feature and Bag of Feature Model. Chinese Journal of Biomedical Engineering, 2020, 39(4): 404-412.

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

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