The grade classification algorithm of breast tumor based on ultrasound RF signals

Tong Ying; Yan Yu

doi:10.12086/oee.2019.180368

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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

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

The grade classification algorithm of breast tumor based on ultrasound RF signals

Tong Ying^1,,
Yan Yu^2,3, ,

1.
Department of Communication Engineering, Nanjing Institute of Technology, Nanjing, Jiangsu 211167, China
2.
School of Computer Science & Engineering, Nanjing University of Science & Technology, Nanjing, Jiangsu 210094, China
3.
Medical Equipment Department, Affiliated Hospital Nanjing University of TCM (Jiangsu Province Hospital of TCM), Nanjing, Jiangsu 210029, China

Fund Project: Supported by National Natural Science Foundation of China (61703201), NSF of Jiangsu Province (BK20170765), and NIT fund for Young Scholar (CKJB201602)

More Information

Corresponding author: Yan Yu, E-mail:yanyucan@126.com

Received Date 11 July 2018

Revised Date 08 October 2018

Published Date 25 January 2019

Abstract

Abstract

A novel efficient method based on the ultrasound radio frequency (RF) signals is proposed to distinguish the breast tumors grades. First, we utilize the multi-scale geometric characteristic of Shearlet transformation to extract the multi-scale and multi-directional features of ultrasound RF signal, and then reduce the high-dimensional Shearlet features by multi-scale directional binary pattern which can effectively preserve the sufficient discriminated information. At last, we draw on the feature difference between different grades of breast tumors to design a cascade binary tree SVM classifier which not only overcome the problem of sample quantity disequilibrium but also conform to the subjective diagnosis rule of sonographer. Extensive experiments on 928 breast ultrasound RF signals collected from the hospital demonstrate the effectiveness of the new 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.
- CAD /
- ultrasound RF signal /
- support vector machine /
- Shearlet transformation

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References

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Overview

Overview

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.