摘要
为解决超声乳腺肿瘤分级检测问题,从超声射频(RF)信号的角度提出了一种有效的乳腺肿瘤分级检测方法。首先,采用Shearlet变换提取乳腺超声RF信号的多尺度、多方向特征;其次,考虑Shearlet特征的高维冗余性,采用多尺度方向二值模式(MDBP)对其进行编码,在不损失特征信息的条件下降低特征维度;最后,依据医生阅片经验以及不同分级乳腺肿瘤的特征差异性,设计出适合乳腺病变分级检测的层级二叉树SVM分类器(CBT-SVM)。在928个乳腺肿瘤患者的超声RF信号上进行验证,大量结果表明,提出方法可以有效实现3级、4A级～4C级、5级乳腺肿瘤的分级检测,准确度、敏感度、特异度、PPV、NPV以及MCC分别达到89.29%、75.62%、94.54%、97%、98.3%和81.01%。
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.
作者
童莹
严郁
Tong Ying;Yan Yu(Department of Communication Engineering,Nanjing Institute of Technology,Nanjing,Jiangsu 211167,China;School of Computer Science & Engineering,Nanjing University of Science & Technology,Nanjing,Jiangsu 210094,China;Medical Equipment Department,Affiliated Hospital Nanjing University of TCM (Jiangsu Province Hospital of TCM),Nanjing,Jiangsu 210029,China)
出处
《光电工程》
CAS
CSCD
北大核心
2019年第1期37-50,共14页
Opto-Electronic Engineering
基金
国家自然科学基金项目(61703201)
江苏省自然科学基金项目(BK20170765)
南京工程学院青年基金面上项目(CKJB201602)资助~~
作者简介
童莹(1979-),女,副教授,主要从事信号与信息处理的研究。E-mail:tongying@njit.edu.cn;通信作者:严郁(1979-),男,高级工程师,主要从事生物医学信息处理的研究。E-mail:yanyucan@126.com
