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√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
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