Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (3): 15-23.doi: 10.6040/j.issn.1672-3961.0.2019.305

• Machine Learning & Data Mining • Previous Articles     Next Articles

MR image classification and recognition model of breast cancer based onGabor feature

Gaoteng YUAN1(),Yihui LIU1,*(),Wei HUANG2,Bing HU3   

  1. 1. School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 205353, Shandong, China
    2. Department of Radiation Oncology, Shandong Cancer Hospitaland Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, Shandong, China
    3. School of Medicine and Life Sciences, University of Jinan, Jinan 250022, Shandong, China
  • Received:2019-06-13 Online:2020-06-20 Published:2020-06-16
  • Contact: Yihui LIU E-mail:yuangaoteng@163.com;yxl@qlu.edu.cn
  • Supported by:
    国家自然科学基金(81773232);国家自然科学基金(81530060);国家自然科学基金(81402538);国家自然科学基金(61375013);山东省自然科学基金(ZR2013FM20)

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Abstract:

To investigate the clinical value of breast tumor magnetic resonance (MR) images in differentiating fibroadenoma of breast (FB), invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC), the region of interest of MR image was selected and the MR image was decomposed by wavelet transform, and the region of tumor was segmented by K-means algorithm. Gabor wavelet was used to filter the region of interest from 8 directions and 5 scales, and the mean value of the tumor location was taken as the feature. The extracted features were analyzed and the key features were obtained. Different classification algorithms were compared in machine learning, such as support vector machine, Bayesian, and neural network, to classify and predict the key features, and calculate the accuracy, sensitivity and specificity of classification, so as to get the most suitable parameter settings for classification model. Texture analysis of breast MR images could distinguish three kinds of common breast tumors, and the prediction accuracy was 77.36%, which showed that MR image had important clinical value in differentiating FB, IDC and ILC.

Key words: breast tumor, MR images, Gabor, texture analysis, machine learning

CLC Number: 

  • TP391

Fig.1

MR imaging of different types of breast tumors"

Fig.2

MR image ROI area of breast tumor"

Fig.3

Decomposition process of DWT"

Fig.4

Subgraphs after DWT decomposition"

Fig.5

Segmentation process of tumor"

Fig.6

Two-dimensional Gabor wavelet function in different directions"

Fig.7

Gabor wavelet filtered image with five scales and eight directions"

Fig.8

Comparison of the mean values of three tumor features when Gabor wavelet direction is 0"

Fig.9

The feature values of three kinds of tumors when the direction is 0, and the scale is $2\sqrt 2$"

Table 1

Predictive results of FB and ILC %"

关键特征个数 支持向量机 BP神经网络 贝叶斯
正确率 特异性 灵敏度 正确率 特异性 灵敏度 正确率 特异性 灵敏度
3 72.22 88.89 55.56 61.11 44.44 77.78 72.22 55.56 88.88
4 72.22 77.78 66.67 72.22 66.66 77.78 61.10 44.44 77.78
5 83.33 100.00 66.67 61.11 66.67 66.66 72.22 44.44 66.66
6 61.10 33.33 88.88 72.22 88.88 55.56 72.22 77.78 66.66
7 61.10 88.88 22.22 72.22 77.78 66.67 77.77 55.56 100.00
8 44.44 44.44 44.44 66.67 77.78 44.44 44.55 22.22 66.66

Table 2

Predictive results of FB and IDC %"

关键特征个数 支持向量机 BP神经网络 贝叶斯
正确率 特异性 灵敏度 正确率 特异性 灵敏度 正确率 特异性 灵敏度
3 97.22 100.00 94.44 100.00 100.00 100.00 100.00 100.00 100.00
4 94.44 100.00 88.89 97.22 100.00 94.44 100.00 100.00 100.00
5 100.00 100.00 100.00 83.33 66.67 100.00 100.00 100.00 100.00
6 100.00 100.00 100.00 97.22 100.00 94.44 100.00 100.00 100.00
7 97.22 100.00 94.44 97.22 100.00 94.44 100.00 100.00 100.00
8 100.00 100.00 100.00 97.22 100.00 94.44 97.22 100.00 94.44

Table 3

Three types of breast tumor prediction results %"

关键特征个数 支持向量机 BP神经网络 贝叶斯
Q1 QFB QIDC QILC Q1 QFB QIDC QILC Q1 QFB QIDC QILC
3 71.70 66.67 82.14 62.50 69.80 52.94 78.57 75.00 67.92 58.82 71.43 75.00
4 73.58 66.67 85.71 62.50 67.92 47.06 85.71 50.00 71.70 70.59 71.43 75.00
5 77.36 66.57 92.86 62.50 75.47 66.67 85.71 75.00 77.36 70.59 82.14 75.00
6 69.81 64.70 78.57 50.00 67.92 52.94 78.57 62.50 67.92 58.82 75.00 62.50
7 73.58 64.70 85.71 50.00 73.58 58.82 85.71 62.50 71.69 64.71 78.57 62.50
8 71.69 66.67 85.71 50.00 67.92 52.94 78.57 62.50 71.69 58.82 78.57 75.00
1 MCGRATH S E , RING A . Chemotherapy for breast cancer in pregnancy: evidence and guidance for oncologists[J]. Therapeutic Advances in Medical Oncology, 2011, 3 (2): 73- 83.
2 郑莹, 吴春晓, 张敏璐. 乳腺癌在中国的流行状况和疾病特征[J]. 中国癌症杂志, 2013, 23 (8): 561- 569.
ZHENG Ying , WU Chunxiao , ZHANG Minlu . The epidemic and characteristics of female breast cancer in China[J]. China Oncology, 2013, 23 (8): 561- 569.
3 李明慧, 柳莉莎. 超声弹性成像评分标准对乳腺良恶性肿块的诊断价值[J]. 肿瘤, 2011, 31 (5): 453- 456.
LI Minghui , LIU Lisha . The diagnostic value of ultrasonic elastography in identifying malignancies of breast diseases[J]. Tumor, 2011, 31 (5): 453- 456.
4 唐玮, 刘剑仑, 杨华伟, 等. 整形保乳术与常规保乳术在早期乳腺癌治疗中的比较分析[J]. 中国肿瘤临床, 2016, 43 (6): 235- 239.
TANG Wei , LIU Jianlun , YANG Huawei , et al. Clinical comparative study of oncoplastic and standard breast-conserving surgery in the treatment of early breast cancer[J]. Chinese Journal of Clinical Oncology, 2016, 43 (06): 235- 239.
5 O'SULLIVAN T D , LEPROUX A , CHEN J H , et al. Optical imaging correlates with magnetic resonance imaging breast density and reveals composition changes during neoadjuvant chemotherapy[J]. Breast Cancer Research, 2013, 15 (1): 14.
6 SUTTON E J , DASHEVSKY B Z , OH J H , et al. Breast cancer molecular subtype classifier that incorporates MRI features[J]. Journal of Magnetic Resonance Imaging, 2016, 44 (1): 122- 129.
7 刘丽, 赵凌君, 郭承玉, 等. 图像纹理分类方法研究进展和展望[J]. 自动化学报, 2018, 44 (4): 584- 607.
LIU Li , ZHAO Lingjun , GUO Chengyu , et al. Texture classification: state-of-the-art methods and prospects[J]. Acta Automatica Sinica, 2018, 44 (4): 584- 607.
8 ZACHARAKI EI , WANG S , CHAWLA S , et al. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme[J]. Magnetic Resonance in Medicine, 2010, 62 (6): 1609- 1618.
9 LIU Yihui , MUFTAH M , DAS T , et al. Classification of MR tumor images based on Gabor wavelet analysis[J]. Journal of Medical & Biological Engineering, 2012, 32 (1): 22- 28.
10 PAN Y, HUANG W, LIN Z, et al. Brain tumor grading based onneural networks and convolutional neural networks[C]// Conf Proc IEEE Eng Med Biol Soc, (2015).[S.l.]: [s.n.], 2015: 699-702.
11 KOOI T , LITJENS G , GINNEKEN B V , et al. Large scale deep learning for computer aided detection of mammographic lesions[J]. Medical Image Analysis, 2017, 35, 303- 312.
12 MERCKEL L G , VERKOOIJEN H M , PETERS N H G M , et al. Theadded diagnostic value of dynamic contrast-enhanced MRI at 3.0 T in nonpalpable breast lesions[J]. Plos One, 2014, 9 (4): e94233.
13 MICHAUT M , CHIN S F , MAJEWSKI I , et al. Integration of genomic, transcriptomic and proteomic data identifies two biologically distinct subtypes ofinvasive lobular breast cancer[J]. Sci Rep, 2016, 6, 18517.
14 ULANER G A , GOLDMAN D , GONEN M , et al. Initial results of a prospective clinical trial of 18F-Fluciclovine PET/CT in newly diagnosed invasive ductal and invasive lobular breast cancers[J]. Journal of Nuclear Medicine, 2016, 57 (9): 1350.
15 陈波, 张磊. 2017年乳腺癌新辅助治疗进展[J]. 山东大学学报(医学版), 2018, 56 (1): 12- 16.
CHEN Bo , ZHANG Lei . Current perspectives of neoadjuvant therapy for breast cancer in 2017[J]. Journal of Shandong University (Health Sciences), 2018, 56 (1): 12- 16.
16 CHEN Yunmei , YE Xiaojing , HUANG Feng , et al. A novel method and fast algorithm for MR image reconstruction with significantly under-sampled data[J]. Inverse Problems & Imaging, 2017, 4 (2): 223- 240.
17 ACHARYA U R , MOOKIAH M R , KOH J E , et al. Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features[J]. Computers in Biology & Medicine, 2016, 73 (C): 131- 140.
18 刘慧, 王小宜, 龙学颖. 基于CT图像纹理分析肿瘤异质性的研究进展及应用[J]. 国际医学放射学杂志, 2016, 39 (5): 543- 548.
LIU Hui , WANG Xiaoyi , LONG Xueying . Research progress and clinical application of tumor heterogeneity based on CT texture analysis[J]. International Journal of Medical Radiology, 2016, 39 (5): 543- 548.
19 HUANG Xiuchang, SU Wei. An improved K-means clustering algorithm[C]// World Automation Congress. Hawaii, USA: Journal of Networks, 2014, 9(1): 161-167.
20 范春年, 张福炎. Gabor相位特征的人脸光照不变量提取[J]. 中国图象图形学报, 2012, 17 (5): 676- 681.
FAN Chunnian , ZHANG Fuyan . Illumination invariant extraction on Gabor phase[J]. Journal of Image and Graphics, 2012, 17 (5): 676- 681.
21 KAY S , QUAN D , BO T , et al. Probability density function estimation using the EEF With application to subset/feature selection[J]. IEEE Transactions on Signal Processing, 2016, 64 (3): 641- 651.
22 侯霄雄, 许新征, 朱炯, 等. 基于AlexNet和集成分类器的乳腺癌计算机辅助诊断方法[J]. 山东大学学报(工学版), 2019, 49 (2): 74- 79.
HOU Xiaoxiong , XU Xinzheng , ZHU Jiong , et al. Computer aided diagnosis method for breast cancer based on AlexNet and ensemble classifiers[J]. Journal of Shandong University(Engineering Science), 2019, 49 (2): 74- 79.
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