基于随机森林特征选择算法的鼻咽肿瘤分割
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摘要
针对医学图像中存在的灰度对比度低、器官组织边界模糊等问题,提出一种新的随机森林(RF)特征选择算法用于鼻咽肿瘤MR图像的分割。首先,充分提取图像的灰度、纹理、几何等特征信息用于构建一个初始的随机森林分类器;随后,结合随机森林特征重要性度量,将改进的特征选择方法应用于原始手工特征集;最终,以得到的最优特征子集构建新的随机森林分类器对测试图像进行分割。实验结果表明,该算法对鼻咽肿瘤的分割精度为:Dice系数79.197%,Acc准确率97.702%,Sen敏感度72.191%,Sp特异性99.502%。通过与基于传统随机森林和基于深度卷积神经网络(DCNN)的分割算法对比可知,所提特征选择算法能有效提取鼻咽肿瘤MR图像中的有用信息,并较大程度地提升小样本情况下鼻咽肿瘤的分割精度。
Due to the low grey-level contrast and blurred boundaries of organs in medical images, a Random Forest(RF) feature selection algorithm was proposed to segment nasopharyngeal neoplasms MR images. Firstly, gray-level, texture and geometry information was extracted from nasopharyngeal neoplasms images to construct a random forest classifier. Then, feature importances were measured by the random forest, and the proposed feature selection method was applied to the original handcrafted feature set. Finally, the optimal feature subset obtained from the feature selection process was used to construct a new random forest classifier to make the final segmentation of the images. Experimental results show that the performances of the proposed algorithm are: dice coefficient 79.197%, accuracy 97.702%, sensitivity 72.191%, and specificity 99.502%. By comparing with the conventional random forest based and Deep Convolution Neural Network(DCNN) based segmentation algorithms, it is clearly that the proposed feature selection algorithm can effectively extract useful information from the nasopharyngeal neoplasms MR images and improve the segmentation accuracy of nasopharyngeal neoplasms under small sample circumstance.
Due to the low grey-level contrast and blurred boundaries of organs in medical images, a Random Forest(RF) feature selection algorithm was proposed to segment nasopharyngeal neoplasms MR images. Firstly, gray-level, texture and geometry information was extracted from nasopharyngeal neoplasms images to construct a random forest classifier. Then, feature importances were measured by the random forest, and the proposed feature selection method was applied to the original handcrafted feature set. Finally, the optimal feature subset obtained from the feature selection process was used to construct a new random forest classifier to make the final segmentation of the images. Experimental results show that the performances of the proposed algorithm are: dice coefficient 79.197%, accuracy 97.702%, sensitivity 72.191%, and specificity 99.502%. By comparing with the conventional random forest based and Deep Convolution Neural Network(DCNN) based segmentation algorithms, it is clearly that the proposed feature selection algorithm can effectively extract useful information from the nasopharyngeal neoplasms MR images and improve the segmentation accuracy of nasopharyngeal neoplasms under small sample circumstance.
引文
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[2] CHU E A,WU J M,TUNKEL D E,et al.Nasopharyngeal carcinoma:the role of the Epstein-Barr virus [J].Medscape Journal of Medicine,2008,10(7):165.
[3] KLEIN G,KASHUBA E.Nasopharyngeal carcinoma[J].Brenners Encyclopedia of Genetics,2013,13(1):4-5.
[4] CHUA M L K,WEE J T S,HUI E P,et al.Nasopharyngeal carcinoma[J].Lancet,2015,387(10022):1012.
[5] ZHOU J,LIM T K,CHONG V.Tumor volume measurement for nasopharyngeal carcinoma using knowledge-based fuzzy clustering MRI segmentation[J].Proceedings of SPIE,2002,4684:1698-1708.
[6] ZHOU J,LIM T K,CHONG V,et al.A texture combined multispectral magnetic resonance imaging segmentation for nasopharyngeal carcinoma[J].Optical Review,2003,10(5):405-410.
[7] ZHOU J,CHAN K L,XU P,et al.Nasopharyngeal carcinoma lesion segmentation from MR images by support vector machine[C]// Proceedings of the 3rd IEEE International Symposium on Biomedical Imaging:Nano to Macro.Piscataway,NJ:IEEE Press,2006:1364-1367.
[8] LEE F K,YEUNG D K,KING A D,et al.Segmentation of NasoPharyngeal Carcinoma (NPC) lesions in MR images[J].International Journal of Radiation Oncology Biology Physics,2005,61(2):608-620.
[9] CHANAPAI W.Nasopharyngeal carcinoma segmentation using a region growing technique[J].International Journal of Computer Assisted Radiology and Surgery,2012,7(3):413-422.
[10] HUANG W-C,LIU C-L.A hybrid supervised learning nasal tumor discrimination system for DMRI[J].Journal of the Chinese Institute of Engineers,2012,35(6):723-733.
[11] 洪容容,叶少珍.基于改进的区域生长鼻咽癌MR医学图像分割[J].福州大学学报(自然科学版),2014,42(5):683-687.(HONG R R,YE S Z.Segmentation of nasopharyngeal MR medical image based on improved region growing[J].Journal of Fuzhou University (Natural Science Edition),2014,42(5):683-687.)
[12] HUANG K W,ZHAO Z Y,GONG Q,et al.Nasopharyngeal carcinoma segmentation via HMRF-EM with maximum entropy[C]// Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.Piscataway NJ:IEEE,2015:2968-2972.
[13] FENG A,CHEN Z H,WU X,et al.From convolutional to recurrent:case study in nasopharyngeal carcinoma segmentation[C]// Proceedings of the 2017 International Conference on the Frontiers and Advances in Data Science.Piscataway NJ:IEEE,2017:18-22.
[14] MEN K,CHEN X Y,ZHANG Y,et al.Deep deconvolutional neural network for target segmentation of nasopharyngeal cancer in planning computed tomography images[J].Frontiers in Oncology,2017,7:315.
[15] WANG Y,ZU C,HU G,et al.Automatic tumor segmentation with deep convolutional neural networks for radiotherapy applications[J].Neural Processing Letters,2018,48(3):1323-1334.
[16] BREIMAN L.Random forest[J].Machine Learning,2001,45(1):5-32.
[17] BREIMAN L.Bagging predictors[J].Machine Learning,1996,24(2):123-140.
[18] BREIMAN L.Manual on setting up,using,and understanding random forests V3.1[EB/OL].[2012- 05- 05].http://oz.berkeley.edu/users/breiman/Using_random_forests_V3.1.pdf.
[19] UWE H,RALF M,MICHAELl K B,et al.A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data[J].BMC Bioinformatics,2009,10(1):1-16.
[20] ALTMANN A,TOLOSI L,SANDER O,et al.Permutation importance:a corrected feature importance measure[J].Bioinformatics,2010,26(10):1340-1347.
[21] CAROLIN S,ANNE-LAURE B,THOMAS K,et al.Conditional variable importance for random forests [J].BMC Bioinformatics,2008,9(1):307-307.
[22] 姚登举.面向医学数据的随机森林特征选择及分类方法研究[D].哈尔滨:哈尔滨工程大学,2016:71-88.(YAO D J.Research on feature selection and classification method based on random forest for medical datasets[D].Harbin:Harbin Engineering University,2016:71-88.)