摘要
目的长期感染溃疡性结肠炎(ulcerative colitis,UC)的患者罹患结肠癌的风险显著提升,因此早期进行结肠镜检测十分必要,但内窥镜图像数量巨大且伴有噪声干扰,需要找到精确的图像特征,为医师提供计算机辅助诊断。为解决UC图像与正常肠道图像的分类问题,提出了一种基于压缩感知和空间金字塔池化结合的图像特征提取方法。方法使用块递归最小二乘(block recursive least squares,BRLS)进行初始字典训练。提出基于先验知识进行观测矩阵与稀疏字典的交替优化算法,并利用压缩感知框架获得图像的稀疏表示,该框架改善了原来基于稀疏编码的图像分类方法无法精确表示图像的问题,然后结合最大空间金字塔池化方法提取压缩感知空间金字塔池化(compressed sensing spatial pyramid pooling,CSSPP)图像特征,由于压缩感知的引入,获得的图像特征比稀疏编码更加丰富和精确。最后使用线性核支持向量机(support vector machine,SVM)进行图像分类。结果对Kvasir数据集中的2000幅真实肠道图像的分类结果表明,该特征的准确率比特征袋(bag of features,Bo F)、稀疏编码空间金字塔匹配(sparse coding spatial pyramid matching,SCSPM)和局部约束线性编码(locality-constrained linear coding,LLC)分别提升了12.35%、3.99%和2.27%。结论本文提出的溃疡性结肠炎辅助诊断模型,综合了压缩感知和空间金字塔池化的优点,获得了较对比方法更加精确的识别感染图像检测结果。
Objective Patients with chronic ulcerative colitis(UC)have a significantly increased risk of developing colon cancer,and an early detection via colonoscopy is necessary.Assisted diagnosis of UC is among the computer-assisted diagnostic topics in medical gastrointestinal endoscopy research that aims to find reliable features for identifying lesions in intestinal images.The typical endoscopic findings from UC images include loss of vascular patterns,granularity,bleeding mucous membranes,and ulcers.UC often repeats recurrence and remission cycles during its course and may be accompanied by parenteral complications.The risk of colon cancer increases when UC extensively affects the large intestine for long periods.However,endoscopes take a large amount of intestinal images,and noise interference,such as shadows,is often observed in these images.Therefore,accurate image features must be identified,and computer-aided diagnosis must be provided to physicians.The extant convolutional neural networks have demonstrated extraordinary capabilities in image classification but requires the support of large datasets and a very long training process.In sparsely coded image recognition,sparse coding pyramid matching(SCSPM)applies selective sparse coding to extract the significant characteristics of scaleinvariant feature transform(SIFT)descriptors for local image blocks.Sparse coding also maximizes pooling on multiple spatial scales to combine translation and scale invariance instead of averaging the pools in the histogram.Locally constrained linear coding(LLC)refers to the rapid implementation of local coordinate coding,where local constraints are used to project each descriptor onto a local coordinate system.However,sparse coding cannot obtain an accurate image representation.The theory of compressed sensing is based on the sparseness of the signal and uses an underdetermined random observation matrix for sampling such signal.Each measurement value is not the signal itself but a linear combination function of multiple signals,th
作者
杨海清
孙道洋
Yang Haiqing;Sun Daoyang(College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China)
出处
《中国图象图形学报》
CSCD
北大核心
2020年第8期1684-1694,共11页
Journal of Image and Graphics
基金
浙江省自然科学基金项目(LY13F010008)
浙江省科技计划项目(2015F50009)。
关键词
溃疡性结肠炎
计算机辅助诊断
压缩感知
交替优化
空间金字塔池化
ulcerative colitis(UC)
computer aided diagnosis
compressed sensing(CS)
alternate optimization
spatial pyramid pooling(SPP)
