# Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM

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## Abstract

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## 1. Introduction

## 2. Related Work

## 3. Method

#### 3.1. Preprocessing

#### 3.2. Adaptive Multi-Threshold Segmentation

Algorithm 1: K-means clustering algorithm. |

Input: number of clusters N; image I(m, n) |

Output: Threshold of N clusters. |

1. Step = max (max (I(m, n) − min (I(m, n)))/Clusters |

2. Interval = step |

3. For I = 1: N |

4. K(I).centroid = interval |

5. Interval = interval + step |

6. End |

7. I(m, n )→K(I).centroid |

8. Repeat |

9. While K(I).centroid ≠ update (I) i. K(I).centroid = update (I) ii. I(m, n )→K(I).centroid |

10. End |

11. Until K(I).centroid ≠ update (I) |

12. Where K(I).centroid is the cluster center. |

#### 3.3. Feature Extraction

Algorithm 2: LBP feature algorithm steps. |

Input: image I(m, n) |

Output: the feature vector |

1. Dividing block 16*16 |

2. If I_{x, y}≥I_{x±1, y±1} |

3. I_{x, y} = 0 |

4. Else |

5. I_{x, y} = 1 |

6. Binary→decimal |

7. Normalization of the frequency histogram |

_{x}), i

_{p}and s are coordinates of the center pixel with gray values, the gray value of the surrounding P Pixel and a symbol function, respectively. The symbol function is defined as:

#### 3.4. Adaptive Support Vector Machine

## 4. System Performance Evaluation Method

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Rebecca, L.; Kimberly, D.; Jemal, A. Cancer Statistics. CA Cancer J. Clin.
**2019**, 69, 7–34. [Google Scholar] - Yang, F.; Kong, X. Breast density and risk of breast cancer. Cancer Imaging
**2013**, 22, 143–145. [Google Scholar] - Gao, Y.; Wang, P. Progress in MRI diagnosis of breast cancer. Chin. J. Med. Imaging Technol.
**2018**, 34, 306–309. [Google Scholar] - Liu, Y.; Lu, C. Comparison of the accuracy of digital mammography and digital mammography in evaluating the size of breast ductal carcinoma in situ. Chin. J. Med. Imaging Technol.
**2017**, 33, 1349–1352. [Google Scholar] - Zheng, Y.; Wu, C.; Zhang, M. Prevalence and disease characteristics of breast cancer in China. Chin. J. Cancer
**2013**, 23, 561–569. [Google Scholar] - Cai, S.; Tang, J.N.; Liu, P.Z.; Cai, Y.L.; Luo, Y.M.; Du, Y.Z.; Peng, Y.; Li, P. Breast Density Classification Based on Wavelet Transform. J. Med. Imaging Health Inf.
**2018**, 8, 1157–1163. [Google Scholar] [CrossRef] - Bonfiglio, R.; Scimeca, M.; Urbano, N.; Bonanno, E.; Schillaci, O. Breast microcalcifications: Biological and diagnostic perspectives. Future Oncol.
**2018**, 14, 3097–3099. [Google Scholar] [CrossRef] [PubMed] - Bonfiglio, R.; Scimeca, M.; Toschi, N.; Pistolese, C.A.; Giannini, E.; Antonacci, C.; Ciuffa, S.; Tancredi, V.; Tarantino, U.; Albonici, L. Radiological, histological and chemical analysis of breast microcalcifications: Diagnostic value and biological significance. J. Mammary Gland Biol. Neoplasia
**2018**, 23, 89–99. [Google Scholar] [CrossRef] - Scimeca, M.; Giannini, E.; Antonacci, C.; Pistolese, C.A.; Spagnoli, L.G.; Bonanno, E. Microcalcifications in breast cancer: An active phenomenon mediated by epithelial cells with mesenchymal characteristics. BMC Cancer
**2014**, 14, 286. [Google Scholar] [CrossRef] - Scimeca, M.; Urbano, N.; Bonfiglio, R.; Schillaci, O.; Bonanno, E. Management of oncological patients in the digital era: Anatomic pathology and nuclear medicine teamwork. Future Oncol.
**2018**, 14, 1013–1015. [Google Scholar] [CrossRef] - Zhang, L.; Hao, C.; Wu, Y.; Zhu, Y.; Ren, Y.; Tong, Z. Microcalcification and BMP-2 in breast cancer: Correlation with clinicopathological features and outcomes. Oncotargets Ther.
**2019**, 12, 2023. [Google Scholar] [CrossRef] [PubMed] - McLoughlin, K.J.; Bones, P.J.; Karssemeijer, N. Noise equalization for detection of microcalcification clusters in direct digital mammogram images. IEEE Trans. Med. Imaging
**2004**, 23, 313–320. [Google Scholar] [CrossRef] [PubMed] - Cole, L.E.; Vargogogola, T.; Roeder, R.K. Contrast-enhanced X-ray detection of breast microcalcifications in a murine model using targeted gold nanoparticles. ACS Nano
**2014**, 8, 7486–7496. [Google Scholar] [CrossRef] [PubMed] - Wang, R.; Wan, B.; Ma, Z.; Cao, X. Computer-aided detection of microcalcifications in digital mammograms using a synthetic technique. In Proceedings of the Second International Conference on Image and Graphics, Hefei, China, 6–18 August 2002; pp. 639–645. [Google Scholar]
- Yoshida, H.; Zhang, W.; Cai, W.; Doi, k.; Nishikawa, R.M.; Giger, M.L. Optimizing wavelet transform based on supervised learning for detection of microcalcifications in digital mammograms. In Proceedings of the International Conference on Image Processing, Washington, DC, USA, 23–26 October 1995; pp. 152–155. [Google Scholar]
- Lee, M.S.; Park, S.W.; Lee, S.Y.; Kang, M.G. Motion-adaptive 3D nonlocal means filter based on stochastic distance for low-dose X-ray fluoroscopy. Biomed. Signal Process. Control
**2017**, 38, 74–85. [Google Scholar] [CrossRef] - Bousbia, N.; Labat, J.M.; Balla, A.; Rebai, I. Supervised classification on navigational behaviours in web-based learning systems to identify learning styles. Int. J. Learn. Technol.
**2011**, 6, 24–45. [Google Scholar] [CrossRef] - Valvano, G.; Santini, G.; Martini, N.; Ripoli, A.; Iacconi, C.; Chiappino, D.; Latta, D.L. Convolutional Neural Networks for the segmentation of microcalcification in Mammography Imaging. J. Healthc. Eng.
**2019**, 9, 9360941. [Google Scholar] [CrossRef] - Duggento, A.; Aiello, M.; Cavaliere, C.; Cascella, G.L.; Cascella, D.; Conte, G.; Guerrisi, M.; Toschi, N. An Ad Hoc Random Initialization Deep Neural Network Architecture for Discriminating Malignant Breast Cancer Lesions in Mammographic Images. Contrast Media Mol. Imaging
**2019**, 9, 5982834. [Google Scholar] [CrossRef] - Panachakel, J.T. Contourlet transform and iterative noise free filtering based bilayer filter for enhancing echocardiogram. In Proceedings of the 2012 International Conference on Green Technologies (ICGT), Trivandrum, India, 18–20 December 2012. [Google Scholar]
- Fu, Y.; Wang, Y. An Algorithm for Edge Detection of Gray Image Based on Mathematical Morphology. J. Harbin Eng. Univ.
**2005**, 26, 685–687. [Google Scholar] - Liu, P.; Guo, J.M.; Wu, C.Y.; Cai, D. Fusion of deep learning and compressed domain features for content-based image retrieval. IEEE Trans. Image Process.
**2017**, 26, 5706–5717. [Google Scholar] [CrossRef] - Khanmohammadi, S.; Adibeig, N.; Shanehbandy, S. An Improved Overlapping k-Means Clustering Method for Medical Applications. Expert Syst. Appl.
**2017**, 67, 12–18. [Google Scholar] [CrossRef] - Gadelmawla, E.S. A vision system for surface roughness characterization using the gray level co-occurrence matrix. NDT E Int.
**2004**, 37, 577–588. [Google Scholar] [CrossRef] - Liu, P.; Guo, J.M.; Chamnongthai, K.; Prasetyo, H. Fusion of color histogram and LBP-based features for texture image retrieval and classification. Inf. Sci.
**2017**, 390, 95–111. [Google Scholar] [CrossRef] - Naqa, I.; Yang, Y.; Wernick, M.N.; Galatsanos, N.P.; Nishikawa, R.M. A support vector machine approach for detection of microcalcifications. IEEE Trans. Med. Imaging
**2002**, 21, 1552–1563. [Google Scholar] [CrossRef] [PubMed] - Yao, D.; Yang, J.; Zhan, X. Feature Selection Algorithm Based on Random Forest. J. Jilin Univ. (Eng. Sci.)
**2014**, 44, 137–141. [Google Scholar] - Trzcinski, T.; Christoudias, M.; Lepetit, V. Learning Image Descriptors with Boosting. IEEE Trans. Pattern Anal. Mach. Intell.
**2014**, 37, 597–610. [Google Scholar] [CrossRef] - Hou, L.; Samaras, D.; Kurc, T.M.; Gao, Y.; Davis, J.E.; Saltz, J.H. Patch-based convolutional neural network for whole slide tissue image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 26 June–1July 2016; pp. 2424–2433. [Google Scholar]
- Oliver, A.; Torrent, A.; Lladó, X.; Tortajada, M.; Tortajada, L. Automatic microcalcification and cluster detection for digital and digitised mammograms. Knowl.-Based Syst.
**2012**, 28, 68–75. [Google Scholar] [CrossRef] - Rubio, Y.; Montiel, O.; Sepúlveda, R. Quantum inspired algorithm for microcalcification detection in mammograms. Inf. Sci.
**2019**, 480, 305–323. [Google Scholar] [CrossRef] - Liu, X.; Mei, M.; Liu, J.; Hu, W. Microcalcification detection in full-field digital mammograms with PFCM clustering and weighted SVM-based method. EURASIP J. Adv. Signal Process.
**2015**, 2015, 73. [Google Scholar] [CrossRef] [Green Version]

Feature | Definition | Description |
---|---|---|

Grayscale feature (4) | Maximum value Minimum value Mean variance | The distance from the pixel circle is 5 |

Shape feature (13) | area Convex area perimeter Long axis length Short axis length Aspect ratio Equivalent diameter Convexity | The number of pixels in the area; the smallest convex polygon of the region; The number of pixels in the area boundary; The length of the long axis of the ellipse; The short axis length of the ellipse; Length to width ratio; The diameter of a circle having the same area; The area ratio of the area to its smallest convex polygon; |

Angular Second Moment Entropy Relativity Contrast | Formulas (2)–(5) shows four features in detail | |

LBP | The following is a detailed introduction | |

HOG feature (200) | Histogram of Oriented Gradient | The following is a detailed introduction |

Iteration | Eval Result | Objective | Objective Runtime | Best So Far (Observed) | Best So Far (Estimate) | Box Constraint | Kernel Scale |
---|---|---|---|---|---|---|---|

1 | Best | 0.160320 | 42.134 | 0.160320 | 0.160320 | 9.316300 | 0.003238 |

2 | Best | 0.012698 | 12.585 | 0.012698 | 0.023562 | 348.9300 | 1.372200 |

3 | Accept | 0.488890 | 35.127 | 0.012698 | 0.012986 | 1.824900 | 211.9800 |

4 | Accept | 0.022222 | 37.999 | 0.012698 | 0.012721 | 0.006857 | 0.006085 |

5 | Best | 0.011111 | 50.035 | 0.011111 | 0.011097 | 0.002820 | 0.013532 |

6 | Accept | 0.012698 | 12.439 | 0.011111 | 0.011072 | 1.766600 | 0.265770 |

7 | Accept | 0.012698 | 12.643 | 0.011111 | 0.011052 | 991.8700 | 0.282060 |

8 | Accept | 0.055556 | 35.645 | 0.011111 | 0.011028 | 0.001008 | 0.001060 |

Method | Sensitivity | Specificity | Classification Accuracy |
---|---|---|---|

SVM | 0.8360 | 0.9110 | 0.9000 |

RF | 0.9240 | 0.8600 | 0.9150 |

AB | 0.7930 | 0.8700 | 0.8750 |

radiologists 1 | 0. 5000 | 0.8000 | 0.8000 |

radiologists 2 | 0.5200 | 0.8889 | 0.8500 |

ASVM | 0.8540 | 0.9383 | 0.9410 |

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**MDPI and ACS Style**

Cai, S.; Liu, P.-Z.; Luo, Y.-M.; Du, Y.-Z.; Tang, J.-N.
Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM. *Algorithms* **2019**, *12*, 135.
https://doi.org/10.3390/a12070135

**AMA Style**

Cai S, Liu P-Z, Luo Y-M, Du Y-Z, Tang J-N.
Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM. *Algorithms*. 2019; 12(7):135.
https://doi.org/10.3390/a12070135

**Chicago/Turabian Style**

Cai, Sheng, Pei-Zhong Liu, Yan-Min Luo, Yong-Zhao Du, and Jia-Neng Tang.
2019. "Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM" *Algorithms* 12, no. 7: 135.
https://doi.org/10.3390/a12070135