分析超声图像乳腺肿瘤自动检测及良恶性判别

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英文篇名：Analysis of Ultrasonic Breast Tumor Image Automatic Detection and Benign and Malignancy 作者：刘雅楠 ; 李靖宇 ; 唐丽 ; 张春辉 ; 邹鹤 ; 赵添羽 ; 黄程程 英文作者：LIU Ya-nan;LI Jing-yu;TANG Li;ZHANG Chun-hui;ZOU He;ZHAO Tian-yu;HUANG Cheng-cheng;Qiqihar Medical University Medical technology school;Qiqihar Jianhua Hospital Mammography room; 关键词：超声图像 ; 自动检测 ; 乳腺肿瘤 ; 良恶性判别 英文关键词：Ultrasonic images;;Automatic detection;;Breast tumor;;Benign and malignant discriminant 中文刊名：ZHJK 英文刊名：Smart Healthcare 机构：齐齐哈尔医学院医学技术学院;齐齐哈尔建华医院乳腺彩超室; 出版日期：2019-03-05 出版单位：智慧健康 年：2019 期：v.5 语种：中文; 页：ZHJK201907001 页数：3 CN：07 ISSN：10-1365/TN 分类号：6-7+9

摘要

目的探讨超声图像乳腺肿瘤自动检测与良恶性判别方法,进而为疾病临床治疗提供更可靠依据。方法选择2016.4-2017.8经术后病理检查确诊为乳腺肿瘤的60例患者,利用超声图像获取特征参数,对比恶性(组)与良性(组)主要参数检测情况比较。结果恶性组PSV、RI、灰度均值、扭曲度、边缘不规则参数均高于良性组(P<0.05);两组患者在RI>0.7、血流Ⅱ级、Ⅲ级、PSV>15诊断上均存在较明显差异(P<0.05)。结论超声图像自动检测能够为肿瘤定性判断提供更为客观的量化参数,联合检测能为肿瘤性质判断提供更精确信息。
        Objective To study the ultrasonic image automatic detecting and assessment method of benign and malignant breast tumor and provide more reliable basis for clinical treatment of disease. Methods 2016.4-2017.8 by postoperative pathological examination diagnosis of breast tumor of 60 patients, using the characteristic parameters of ultrasonic images contrast malignant(group) and benign situation is main parameters(group) detection. Results Malignant group PSV, RI, grayscale average, distortion and irregular edge of parameters were higher than in benign group(P<0.05); Two groups of patients in the RI>0.7, blood flow Ⅱ, Ⅲ, PSV>15 diagnosis are more obvious differences(P<0.05). Conclusion The ultrasonic image automatic detection to tumor qualitative judgment provides more objective quantitative parameters, joint detection can offer more accurate information for tumor nature judgment.

引文

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