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基于专家乘积系统的组织病理图像分类算法

Histopathological Image Classification Algorithm Based on Product of Experts

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摘要

组织病理图像的自动分类是医学图像处理领域的重要问题, 有效特征提取方法是实现准确诊断的关键。为了实现组织病理图像的特征表示, 提出一种基于专家乘积系统(PoE)的特征提取算法, 利用最大似然和蒙特卡罗随机采样方法训练对应不同图像类别的PoE模型, 将图像样本在所有模型下的响应相连作为其特征向量。根据训练图像样本的特征向量建立支持向量机分类模型。实验测试了宾夕法尼亚州立大学诊断实验室公开的组织病理图像数据库中的肾、肺和脾的健康及患病器官的组织病理图像, 结果显示, 所提算法在3种器官图像分类中均具有较高的准确性。

Abstract

Automatic classification of histopathological image is vital in medical image processing field, and the effective feature extraction plays an key role to realize accurate diagnosis. A feature extraction algorithm based on Product of Experts (PoE) is proposed to realize the feature representation of the histopathological image. The maximum likelihood and Monte Carlo random sampling methods are used to train PoE models corresponding to different kinds of images, and the responses of image samples in the two models are concatenated as their eigenvectors. Finally, a support vector machine (SVM) classification model is built based on the eigenvectors of the trained image samples. The experiments are carried out to classify histopathological images of healthy and inflammatory organs of kidney, lung, and spleen, which are provided by the Animal Diagnostics Lab at Pennsylvania State University. The experimental results show that the proposed algorithm can achieve high accuracy in three organ image classifications.

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中图分类号:TP37

DOI:10.3788/lop55.021008

所属栏目:图像处理

基金项目:深圳市互联网产业发展专项(ZDSY20120613125016389)

收稿日期:2017-07-19

修改稿日期:2017-09-01

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郭琳琳:天津大学电气自动化与信息工程学院, 天津 300072
李岳楠:天津大学电气自动化与信息工程学院, 天津 300072

联系人作者:郭琳琳(lilian_guolinlin@163.com)

备注:郭琳琳(1992—), 女, 硕士研究生, 主要从事图像处理方面的研究。E-mail: lilian_guolinlin@163.com

【1】Chen X T, Zhang F, Zhang Y F, et al. Research on stenosis detection and quantification of coronary artery in CT angiography[J]. Laser & Optoelectronics Progress, 2015, 52(8): 080006.
陈相廷, 张帆, 张一凡, 等. CT造影冠状动脉狭窄检测与量化的相关研究[J]. 激光与光电子学进展, 2015, 52(8): 080006.

【2】Li J W, Chen X D, Jia Z W, et al. A coronary artery lumen segmentation algorithm based on ray casting[J]. Chinese Journal of Lasers, 2015, 42(8): 0804002.
李俊威, 陈晓冬, 贾忠伟, 等. 基于光线投射法的冠脉血管腔壁分割算法[J]. 中国激光, 2015, 42(8): 0804002.

【3】Zhao C, Chen X D, Zhang J C, et al. Coronary lesion detection method based on one-class support vector machine[J]. Chinese Journal of Lasers, 2017, 44(5): 0504006.
赵聪, 陈晓冬, 张佳琛, 等. 基于一类支持向量机的冠脉病变检测方法[J]. 中国激光, 2017, 44(5): 0504006.

【4】Gurcan M N, Boucheron L E, Can A, et al. Histopathological image analysis: A review[J]. IEEE Reviews in Biomedical Engineering, 2009, 2: 147-171.

【5】Madabhushi A. Digital pathology image analysis: Opportunities and challenges[J]. Imaging in Medicine, 2009, 1(1): 7-10.

【6】Gavrilovic M, Azar J C, Lindblad J, et al. Blind color decomposition of histological images[J]. IEEE Transactions on Medical Imaging, 2013, 32(6): 983-994.

【7】Lexe G, Monaco J, Doyle S, et al. Towards improved cancer diagnosis and prognosis using analysis of gene expression data and computer aided imaging[J]. Experimental Biology & Medicine, 2009, 234(8): 860-879.

【8】Dundar M M, Badve S, Bilgin G, et al. Computerized classification of intraductal breast lesions using histopathological images[J]. IEEE Transactions on Biomedical Engineering, 2011, 58(7): 1977-1984.

【9】Tabesh A, Teverovskiy M, Pang H Y, et al. Multifeature prostate cancer diagnosis and gleason grading of histological images[J]. IEEE Transactions on Medical Imaging, 2007, 26(10): 1366-1378.

【10】Srinivas U, Mousavi H S, Monga V, et al. Simultaneous sparsity model for histopathological image representation and classification[J]. IEEE Transactions on Medical Imaging, 2014, 33(5): 1163-1179.

【11】Fukuma K, Prasath V B S, Kawanaka H, et al. A study on feature extraction and disease stage classification for glioma pathology images[C]. IEEE International Conference on Fuzzy Systems, 2016: 16450006.

【12】Cao J J, Qin Z C, Jing J, et al. An automatic breast cancer grading method in histopathological images based on pixel-, object-, and semantic-level features[C]. IEEE International Symposium on Biomedical Imaging, 2016: 1151-1154.

【13】Doyle S, Agner S, Madabhushi A, et al. Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features[C]. IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008: 10054638.

【14】Orlov N, Shamir L, Macura T, et al. WND-CHARM: Multi-purpose image classification using compound image transforms[J]. Pattern Recognition Letters, 2008, 29(11): 1684-1693.

【15】Shamir L, Orlov N, Eckley D M, et al. Wndchrm: An open source utility for biological image analysis[J]. Source Code for Biology and Medicine, 2008, 3: 13.

【16】Batool N. Detection and spatial analysis of hepatic steatosis in histopathology images using sparse linear models[C]. IEEE International Conference on Image Processing Theory Tools and Applications, 2016: 16602172.

【17】Hinton G E. Training products of experts by minimizing contrastive divergence[J]. Neural Computation, 2002, 14(8): 1771-1800.

【18】Welling M, Osindero S, Hinton G E. Learning sparse topographic representations with products of student-t distributions[C]. Advances in Neural Information Processing Systems, 2003: 1383-1390.

【19】Naik S, Doyle S, Agner S, et al. Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology[C]. IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008: 10054585.

【20】Hinton G, Osindero S, Welling M, et al. Unsupervised discovery of nonlinear structure using contrastive backpropagation[J]. Cognitive Science, 2006, 30(4): 725-731.

【21】Andrieu C, Freitas N D, Doucet A, et al. An introduction to MCMC for machine learning[J]. Machine Learning, 2003, 50(1/2): 5-43.

【22】Wright J, Yang A Y, Sastry S S, et al. Robust face recognition via sparse representation[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2009, 31(2): 210-227.

【23】Monga V. Histopathological image data sets[DB/OL]. [2017-07-19]. http: //signal.ee.psu.edu/medical_imaging.html.

引用该论文

Guo Linlin,Li Yuenan. Histopathological Image Classification Algorithm Based on Product of Experts[J]. Laser & Optoelectronics Progress, 2018, 55(2): 021008

郭琳琳,李岳楠. 基于专家乘积系统的组织病理图像分类算法[J]. 激光与光电子学进展, 2018, 55(2): 021008

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