Citation: | Tong Ying, Yan Yu. The grade classification algorithm of breast tumor based on ultrasound RF signals[J]. Opto-Electronic Engineering, 2019, 46(1): 180368. doi: 10.12086/oee.2019.180368 |
[1] | 周世崇, 曾炜, 范亦武, 等.乳腺超声分级方法应用的初步探讨[J].中国超声医学杂志, 2008, 24(6): 19-23. Zhou S C, Zeng W, Fan Y W, et al. Discussing of using breast grades in ultrasound[J]. Chinese Journal of Ultrasound in Medicine, 2008, 24(6): 19-23. |
[2] | 侯新燕, 高宇, 黄晓玲, 等.乳腺影像报告数据系统在乳腺超声中的应用价值[J].中华医学超声杂志(电子版), 2011, 8(6): 1227-1233. doi: 10.3877/cma.j.issn.1672-6448.2011.06.008 Hou X Y, Gao Y, Huang X L, et al. Application value of breast image report data system for ultrasonography in mamary gland[J]. Chinese Journal of Medical Ultrasound (Electronic Edition), 2011, 8(6): 1227-1233. doi: 10.3877/cma.j.issn.1672-6448.2011.06.008 |
[3] | Suzuki K. A review of computer-aided diagnosis in thoracic and colonic imaging[J]. Quantitative Imaging in Medicine and Surgery, 2012, 2(3): 163-176. |
[4] | He J T, Chen M H, Jia W Y, et al. Segmentation of diabetic macular edema in Oct retinal images[J]. Opto-Electronic Engineering, 2018, 45(7): 170605. doi: 10.12086/oee.2018.170605 |
[5] | 吕卫, 翟庆伟, 褚晶辉, 等.彩色眼底图像糖网渗出物的自动检测[J].光电工程, 2016, 43(12): 183-192, 199. doi: 10.3969/j.issn.1003-501X.2016.12.028 Lv W, Zhai Q W, Chu J H, et al. Automated detection of diabetic retinopathy exudates in color fundus images[J]. Opto-Electronic Engineering, 2016, 43(12): 183-192, 199. doi: 10.3969/j.issn.1003-501X.2016.12.028 |
[6] | Wang X W, Li L H, Liu W, et al. An interactive system for computer-aided diagnosis of breast masses[J]. Journal of Digital Imaging, 2012, 25(5): 570-579. doi: 10.1007/s10278-012-9451-0 |
[7] | Jen C C, Yu S S. Automatic detection of abnormal mammograms in mammographic images[J]. Expert Systems with Applications, 2015, 42(6): 3048-3055. doi: 10.1016/j.eswa.2014.11.061 |
[8] | Sharma S, Khanna P. Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM[J]. Journal of Digital Imaging, 2015, 28(1): 77-90. doi: 10.1007/s10278-014-9719-7 |
[9] | 谭婉嫦, 王金花, 蔡洪明, 等.基于微钙化检测的计算机辅助诊断系统对于乳腺导管原位癌的诊断价值[J].临床放射学杂志, 2016, 35(9): 1352-1356. Tan W C, Wang J H, Cai H M, et al. The performance of computer-aided diagnosis for DCIS based on classification of clustered microcalcifications[J]. Journal of Clinical Radiology, 2016, 35(9): 1352-1356. |
[10] | Moon W K, Lo C M, Huang C S, et al. Computer-aided diagnosis based on speckle patterns in ultrasound images[J]. Ultrasound in Medicine & Biology, 2012, 38(7): 1251-1261. |
[11] | Alam S K, Feleppa E J, Rondeau M, et al. Computer-Aided diagnosis of solid breast lesions using an ultrasonic multi-feature analysis procedure[J]. Bangladesh Journal of Medical Physics, 2013, 4(1): 1-10. |
[12] | Huang Q H, Yang F B, Liu L Z, et al. Automatic segmentation of breast lesions for interaction in ultrasonic computer-aided diagnosis[J]. Information Sciences, 2015, 314: 293-310. doi: 10.1016/j.ins.2014.08.021 |
[13] | Cheng J Z, Ni D, Chou Y H, et al. Computer-Aided diagnosis with deep learning architecture: Applications to breast lesions in us images and pulmonary nodules in CT scans[J]. Scientific Reports, 2016, 6: 24454. doi: 10.1038/srep24454 |
[14] |
Masotti L, Biagi E, Granchi S, et al. Tissue differentiation based on radiofrequency echographic signal local spectral content[C]//Proceedings of the IEEE Symposium on Ultrasonics, 2003, 1: 1030-1033. |
[15] | Moradi M, Mahdavi S S, Nir G, et al. Ultrasound RF time series for tissue typing: first in vivo clinical results[J]. Proceedings of SPIE, 2013, 8670: 86701I. doi: 10.1117/12.2007672 |
[16] | Moradi M, Abolmaesumi P, Mousavi P. Tissue typing using ultrasound RF time series: experiments with animal tissue samples[J]. Medical Physics, 2010, 37(8): 4401-4413. doi: 10.1118/1.3457710 |
[17] | 刘志东, 罗燕, 林江莉, 等.基于超声射频RF信号的脂肪肝分级量化方法[J].四川大学学报(工程科学版), 2011, 43(S1): 160-164. Liu Z D, Luo Y, Lin J L, et al. Quantizing and grading of fatty liver based on ultrasonic RF signals[J]. Journal of Sichuan University (Engineering Science Edition), 2011, 43(S1): 160-164. |
[18] | 庄淑莲, 周建华, 王建伟, 等.基于超声射频流的RF时间序列信号在乳腺病变良恶性鉴别中的价值[J].中华医学超声杂志(电子版), 2016, 13(5): 393-397. doi: 10.3877/cma.j.issn.1672-6448.2016.05.014 Zhuang S L, Zhou J H, Wang J W, et al. Differential diagnosis of breast lesions with RF time-series signal based on ultrasonic radio-frequency flow[J]. Chinese Journal of Medical Ultrasound (Electronic Edition), 2016, 13(5): 393-397. doi: 10.3877/cma.j.issn.1672-6448.2016.05.014 |
[19] | 严郁, 方舸, 蔡润秋, 等.基于乳腺肿块的超声射频信号特征算法识别[J].中国医学装备, 2016, 13(9): 20-22. doi: 10.3969/J.ISSN.1672-8270.2016.09.006 Yan Y, Fang G, Cai R Q, et al. Ultrasound radio-frequency characteristic signal processing for breast lump recognition[J]. China Medical Equipment, 2016, 13(9): 20-22. doi: 10.3969/J.ISSN.1672-8270.2016.09.006 |
[20] | Uniyal N, Eskandari H, Abolmaesumi P, et al. Ultrasound RF time series for classification of breast lesions[J]. IEEE Transactions on Medical Imaging, 2015, 34(2): 652-661. doi: 10.1109/TMI.2014.2365030 |
[21] | Guo K H, Labate D. Optimally sparse multidimensional representation using shearlets[J]. SIAM Journal on Mathematical Analysis, 2007, 39(1): 298-318. doi: 10.1137/060649781 |
[22] | Gao G R, Xu L P, Feng D Z. Multi-focus image fusion based on non-subsampled shearlet transform[J]. IET Image Processing, 2013, 7(6): 633-639. doi: 10.1049/iet-ipr.2012.0558 |
[23] | 常莉红.基于剪切波变换和稀疏表示理论的图像融合方法[J].中山大学学报(自然科学版), 2017, 56(4): 16-19. Chang L H. Fusion method based on shearlet transform and sparse representation[J]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2017, 56(4): 16-19. |
[24] | 周飞, 贾振红, 杨杰, 等.基于剪切波域改进Gamma校正的医学图像增强算法[J].光电子·激光, 2017, 28(5): 566-572. Zhou F, Jia Z H, Yang J, et al. Medical image enhancement method based on improved Gamma correction in shearlet domain[J]. Journal of Optoelectronics·Laser, 2017, 28(5): 566-572. |
[25] | 徐畅, 陈晓, 季仟亿.基于稀疏编码的Shearlet域图像去噪[J].激光杂志, 2017, 38(10): 96-100. Xu C, Chen X, Ji Q Y. Shearlet domian image denoising via sparse coding[J]. Laser Journal, 2017, 38(10): 96-100. |
[26] | Easley G R, Labate D, Colonna F. Shearlet-based total variation diffusion for denoising[J]. IEEE Transactions on Image Processing, 2009, 18(2): 260-268. doi: 10.1109/TIP.2008.2008070 |
[27] | Vagharshakyan S, Bregovic R, Gotchev A. Light field reconstruction using Shearlet transform[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(1): 133-147. doi: 10.1109/TPAMI.2017.2653101 |
[28] | 许志良, 邓承志, 张运生.非局域自相似约束的Shearlet稀疏正则化图像恢复[J].电子科技大学学报, 2016, 45(1): 43-47, 101. Xu Z L, Deng C Z, Zhang Y S. Shearlet sparsity regularized image reconstruction based on nonlocal self-similarity[J]. Journal of University of Electronic Science and Technology of China, 2016, 45(1): 43-47, 101. |
[29] | Zhou S C, Shi J, Zhu J, et al. Shearlet-based texture feature extraction for classification of breast tumor in ultrasound image[J]. Biomedical Signal Processing and Control, 2013, 8(6): 688-696. doi: 10.1016/j.bspc.2013.06.011 |
Overview: According to the statistics published by the American Cancer Society (ACS) in 2015, it is estimated that breast cancer is one of the most common types of cancer in women' patients accounting for 29% of all cancer cases. Early detection and better diagnosis methods play a significant role in reducing the number of fatalities induced by breast cancer. Current sonography has become one of the common methods for early screening breast cancer which are widely used to evaluate doubtful masses based on breast imaging-reporting and data system (BI-RADS). However, this method is limited by low contrast of B-mode images and high subjectivity of sonographers which may make the diagnosis results inaccurate and inconsistent. To address these limitations, ultrasound-based computer aided diagnosis (CAD) system is proposed to assist sonographers in breast tumor diagnosis for achieving higher accuracy and consistency. Since most of the existing CAD systems only can distinguish benign tumors and malignant tumors, and their processing data are all B-mode images which are obtained by ultrasound radio frequency signals, the existing CAD systems still need further researches and improvements. In view of this, we present a new method for distinguishing the grades of breast tumors based on the original ultrasound radio frequency signals which have richer tumor lesion information compared to B-mode images. First, we utilize the multi-scale geometric characteristic of Shearlet transformation to extract the multi-scale and multi-directional features of ultrasound RF signal. Second, multi-scale directional binary pattern (MDBP) is designed to code the texture information of high-frequency Shearlet features in different directions and different scales, which can not only reduce the dimension of Shearlet features but also preserve the sufficient discriminated information of breast tumors for the subsequent grade detection. At last, we draw on the feature difference between different grades of breast tumors to put forward a cascade binary tree SVM classifier, which not only overcome the problem of unbalance samples but also conform to the diagnosis rule of sonographer. Extensive experiments on 928 breast ultrasound RF signals collected from the hospital demonstrate the effectiveness of the proposed method and its precision, sensitivity, specificity, PPV, NPV and MCC are 89.29%, 75.62%, 94.54%, 97%, 98.3% and 81.01%, respectively. A point worth emphasizing that the higher values of PPV and NPV further show that the diagnosis results of the proposed method are close to the biopsy gold standard.
The flow diagram of Shearlet transformation
The sketch map of MDBP neighborhood
The flowchat of the proposed feature extraction method
The structure of cascade binary tree SVM classifier
The different grades of breast tumors of ultrasound RF signal. (a) 3 grade; (b) 4A grade; (c) 4B grade; (d) 4C grade; (e) 5 grade
The structure of directed acyclic graph SVM classifier
The ROC and AUC of DAG-SVM classifier
The ROC and AUC of KNN classifier
The ROC and AUC of random forest classifier
The ROC and AUC of CBT-SVM classifier