浏览全部资源
扫码关注微信
收稿：2020-01-03，
修回：2020-3-4，
录用：2020-3-11，
纸质出版：2020-08-16
移动端阅览
目的
2
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
2
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
2
对Kvasir数据集中的2 000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，BoF）、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
2
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
2
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 scale-invariant 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
that is
each measurement contains a small amount of information for all signals. The sparse reconstruction algorithm is used to achieve an accurate reconstruction of signals or an approximate reconstruction with certain errors. To solve the problems in UC and normal intestinal image classification
an image feature based on a combination of compressed sensing and spatial pyramid pooling is proposed in this paper.
Method
2
This paper proposes compressed perceptual spatial pyramid pooling (CSSPP) image features. First
block recursive least squares (BRLS) and K-singular value decomposition(K-SVD) dictionary learning are used to train some positive and negative datasets to obtain two initial dictionaries for comparing the two algorithms. Second
an alternating optimization algorithm based on the UC image prior perception matrix and sparse dictionary is developed. When finding the optimal perception matrix
the Gram matrix of the equivalent dictionary is considered
the sparse representation error is designed as prior knowledge
and the performance of the system is tested at different sampling rates. In dictionary optimization
the influence of the perception matrix on dictionary errors is considered
and the impact of different sparsity on classification accuracy is tested. Using the obtained sensing matrix and sparse dictionary to obtain sparse representations of all datasets in a compressed sensing framework can address the problem where the original image classification method based on sparse coding cannot accurately represent images. The maximum and average spatial pyramid pooling methods are then used to divide the image into 21 regions on multiple scales to extract CSSPP features. By introducing compressed sensing
the information of sparse representation is richer and more accurate than that of sparse coding. A linear kernel support vector machine (SVM) is used for image classification.
Result
2
The proposed algorithm was trained on 100 UC and 100 normal images taken from the Kvasir dataset. During the training phase
the mean square error of the dictionary obtained by BRLS training was slightly lower than that of K-SVD. The highest image restoration accuracy was obtained at a sampling rate of 31.25%. The performance of the two pooling features was examined under different sparsity. The maximum pooling in training accuracy was significantly higher than the average. The quantitative evaluation indicators include sensitivity
specificity
accuracy
receiver operating characferistic(ROC) curve
area under curve(AUC) value
and time efficiency. Tests were then performed on all 2 000 datasets
and the classification results show that the features can improve the auxiliary diagnosis of UC images. Compared with bag of features (BOF)
SCSPM
and LLC
using the SVM classification results increases the AUC values by 0.092
0.035
and 0.015
the sensitivity by 9.19%
2.84%
and -0.24%
the specificity by 15.52%
5.15%
and 4.77%
and the accuracy by 12.35%
3.99%
and 2.27%
respectively. Compared with BOF and SCSPM
the running time was reduced by 2 164.5 and 1 455.6 seconds
respectively.
Conclusion
2
The auxiliary diagnosis model of UC proposed in this paper combines the advantages of compressed sensing and spatial pyramid pooling to obtain highly accurate detection results for identifying infected images. Compressed sensing was utilized instead of sparse coding to obtain the ideal classification results. Similar to neural networks
sufficient information is needed to train the system to obtain highly accurate results. Medical-image-assisted diagnosis requires highly comprehensive and rich image information as criterion. Using highly comprehensive image features to replace human subjective features presents a promising development trend. Therefore
the next goal is to provide doctors with auxiliary diagnosis of UC in real time. Accordingly
how to increase the complexity of the algorithm and the real-time nature of the system needs to be studied in the future.
Aharon M, Elad M and Bruckstein A. 2006. K-SVD:an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 54(11):4311-4322[DOI:10.1109/TSP.2006.881199]
Bai H, Li G, Li S, Li Q W, Jiang Q R and Chang L P. 2015. Alternating optimization of sensing matrix and sparsifying dictionary for compressed sensing. IEEE Transactions on Signal Processing, 63(6):1581-1594[DOI:10.1109/TSP.2015.2399864]
Candes E J and Wakin M B. 2008. An introduction to compressive sampling. IEEE Signal Processing Magazine, 25(2):21-30[DOI:10.1109/MSP.2007.914731]
Chi J N, Yu X S and Zhang Y F. 2018. Thyroid nodule malignantrisk detection in ultrasound image by fusing deep and texture features. Journal of Image and Graphics, 23(10):1582-1593
迟剑宁, 于晓升, 张艺菲. 2018.融合深度网络和浅层纹理特征的甲状腺结节癌变超声图像诊断.中国图象图形学报, 23(10):1582-1593[DOI:10.11834/jig.180232]
Deeba F, Islam M, Bui F M and Wahid K A. 2018. Performance assessment of a bleeding detection algorithm for endoscopic video based on classifier fusion method and exhaustive feature selection. Biomedical Signal Processing and Control, 40:415-424[DOI:10.1016/j.bspc.2017.10.011]
D'Haens G, Sandborn W J, Feagan B G, Geboes K, Hanauer S B, Irvine E J, Lémann M, Marteau P, Rutgeerts P, Schölmerich J and Sutherland L R. 2007. A review of activity indices and efficacy end points for clinical trials of medical therapy in adults with ulcerative colitis. Gastroenterology, 132(2):763-786[DOI:10.1053/j.gastro.2006.12.038]
Donoho D L. 2006. Compressed sensing. IEEE Transactions on Information Theory, 52(4):1289-1306[DOI:10.1109/TIT.2006.871582]
Duarte M F and Eldar Y C. 2011. Structured compressed sensing:from theory to applications. IEEE Transactions on Signal Processing, 59(9):4053-4085[DOI:10.1109/TSP.2011.2161982]
Elad M. 2007. Optimized projections for compressed sensing. IEEE Transactions on Signal Processing, 55(12):5695-5702[DOI:10.1109/TSP.2007.900760]
Engan K, Aase S O and Husoy J H. 2002. Method of optimal directions for frame design//Proceedings of 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Phoenix, AZ: IEEE: 2443-2446[ DOI: 10.1109/ICASSP.1999.760624 http://dx.doi.org/10.1109/ICASSP.1999.760624 ]
Fickus M, Mixon D G and Tremain J C. 2012. Steiner equiangular tight frames. Linear Algebra and its Applications, 436(5):1014-1027[DOI:10.1016/j.laa.2011.06.027]
Guo K R, Chai R F, Candra H, Guo Y, Song R, Nguyen H and Su S. 2019. A hybrid fuzzy cognitive map/support vector machine approach for EEG-based emotion classification using compressed sensing. International Journal of Fuzzy Systems, 21(1):264-273[DOI:10.1007/s40815-018-0567-3]
Jiang Q R, Li S, Lu Z R and Sun B B. 2016. Block recursive least squares dictionary learning algorithm//Proceedings of 2016 Chinese Control and Decision Conference. Yinchuan, China: IEEE: 1961-1964
Lazebnik S, Schmid C and Ponce J. 2006. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories//Proceedings of 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE: 2169-2178[ DOI: 10.1109/CVPR.2006.68 http://dx.doi.org/10.1109/CVPR.2006.68 ]
Li B P, Qi L, Meng M Q H and Fan Y C. 2009. Using ensemble classifier for small bowel ulcer detection in wireless capsule endoscopy images//Proceedings of 2009 IEEE International Conference on Robotics and Biomimetics. Guilin, China: IEEE: 2326-2331[ DOI: 10.1109/ROBIO.2009.5420455 http://dx.doi.org/10.1109/ROBIO.2009.5420455 ]
Li B P, Xu G Q, Zhou R and Wang T F. 2015a. Computer aided wireless capsule endoscopy video segmentation. Medical Physics, 42(2):645-652[DOI:10.1118/1.4905164]
Li F F and Perona P. 2005. A Bayesian hierarchical model for learning natural scene categories//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA: IEEE: 524-531[ DOI: 10.1109/CVPR.2005.16 http://dx.doi.org/10.1109/CVPR.2005.16 ]
Li G, Li X, Li S, Bai H, Jiang Q R and He X X. 2015b. Designing robust sensing matrix for image compression. IEEE Transactions on Image Processing, 24(12):5389-5400[DOI:10.1109/TIP.2015.2479474]
Liu J X, Han G, Sun N, Li X F, Gong Z G and Sun Q S. 2019. Generalized compressed sensing with QR-based vision matrix learning for face recognition under natural scenes. Signal Processing:Image Communication, 77:11-19[DOI:10.1016/j.image.2019.05.009]
Maeda Y, Kudo S E, Mori Y, Misawa M, Ogata N, Sasanuma S, Wakamura K, Oda M, Mori K and Ohtsuka K. 2019. Fully automated diagnostic system with artificial intelligence using endocytoscopy to identify the presence of histologic inflammation associated with ulcerative colitis (with video). Gastrointestinal Endoscopy, 89(2):408-415[DOI:10.1016/j.gie.2018.09.024]
Matsuoka K, Kobayashi T, Ueno F, Matsui T, Hirai F, Inoue N, Kato J, Kobayashi K, Kobayashi K, Koganei K, Kunisaki R, Motoya S, Nagahori M, Nakase H, Omata F, Saruta M, Watanabe T, Tanaka T, Kanai T, Noguchi Y, Takahashi K I, Watanabe K, Hibi T, Suzuki Y, Watanabe M, Sugano K and Shimosegawa T. 2018. Evidence-based clinical practice guidelines for inflammatory bowel disease. Journal of Gastroenterology, 53(3):305-353[DOI:10.1007/s00535-018-1439-1]
Ozawa T, Ishihara S, Fujishiro M, Saito H, Kumagai Y, Shichijo S, Aoyama K and Tada T. 2019. Novel computer-assisted diagnosis system for endoscopic disease activity in patients with ulcerative colitis. Gastrointestinal Endoscopy, 89(2):416-421[DOI:10.1016/j.gie.2018.10.020]
Travis P L S, Schnell D, Krzeski P, Abreu M T, Altman D G, Colombel J F, Feagan B G, Hanauer S B, Lichtenstein G R, Marteau P R, Reinisch W, Sands B E, Yacyshyn B R, Schnell P, Bernhardt C A, Mary J and Sandborn W J. 2013. Reliability and initial validation of the ulcerative colitis endoscopic index of severity. Gastroenterology, 145(5):987-995[DOI:10.1053/j.gastro.2013.07.024]
Tropp J A. 2004. Greed is good:algorithmic results for sparse approximation. IEEE Transactions on Information Theory, 50(10):2231-2242[DOI:10.1109/TIT.2004.834793]
Wang J J, Yang J C, Yu K, Lv F J, Huang T and Gong Y H. 2010. Locality-constrained linear coding for image classification//Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, CA, USA: IEEE: 13-18[ DOI: 10.1109/CVPR.2010.5540018 http://dx.doi.org/10.1109/CVPR.2010.5540018 ]
Wang J M, Ye S P, Xu Z Y, Chen C X and Jiang Y J. 2017. Smooth l0-norm minimization algorithm for compressed sensing with semi-tensor product. Journal of Image and Graphics, 22(1):9-19
王金铭, 叶时平, 徐振宇, 陈超祥, 蒋燕君. 2017.半张量积压缩感知模型的l0-范数解.中国图象图形学报, 22(1):9-19[DOI:10.11834/jig.20170102]
Xie D, Peng H P, Li L X and Yang Y X. 2016. Semi-tensor compressed sensing. Digital Signal Processing, 58:85-92[DOI:10.1016/j.dsp.2016.07.003]
Yang J C, Yu K, Gong Y H and Huang T, 2009. Linear spatial pyramid matching using sparse coding for image classification//Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL, USA: IEEE: 20-25[ DOI: 10.1109/CVPR.2009.5206757 http://dx.doi.org/10.1109/CVPR.2009.5206757 ]
Yang Y J and Bang C S. 2019. Application of artificial intelligence in gastroenterology. World Journal of Gastroenterology, 25(14):1666-1683[DOI:10.3748/wjg.v25.i14.1666]
Yuan Y X, Li B P and Meng M Q H. 2016. Improved bag of feature for automatic polyp detection in wireless capsule endoscopy images. IEEE Transactions on Automation Science and Engineering, 13(2):529-535[DOI:10.1109/TASE.2015.2395429]
Yuan Y X, Li B P and Meng M Q H. 2017. WCE abnormality detection based on saliency and adaptive locality-constrained linear coding. IEEE Transactions on Automation Science and Engineering, 14(1):149-159[DOI:10.1109/TASE.2016.2610579]
Zhang S K, Sun F R, Wang N S, Zhang C C, Yu Q L, Zhang M Q, Babyn P and Zhong H. 2019. Computer-aided diagnosis (CAD) of pulmonary nodule of thoracic CT image using transfer learning. Journal of Digital Imaging, 32(6):995-1007[DOI:10.1007/s10278-019-00204-4]
相关作者
相关机构
京公网安备11010802024621
