首页> 外文期刊>Journal of intelligent & fuzzy systems: Applications in Engineering and Technology >Evolutionary intelligence for breast lesion detection in ultrasound images: A wavelet modulus maxima and SVM based approach
【24h】

Evolutionary intelligence for breast lesion detection in ultrasound images: A wavelet modulus maxima and SVM based approach

机译:超声图像中乳房病变检测的进化综合:基于小波模量的Maxima和SVM方法

获取原文
获取原文并翻译 | 示例
获取外文期刊封面目录资料

摘要

Intelligent lesion detection system for medical ultrasound images are aimed at reducing physicians' effort during cancer diagnosis process. Automatic separation and classification of tumours in ultrasound images is challenging owing to the low contrast and noisy behavior of the image. A Computer aided detection (CAD) system that automatically segment and classify breast tumours in ultrasound (US) images is proposed in this paper. The proposed method is invariant to scale changes and does not require an operator defined initial region of interest. Wavelet modulus maxima points of the US image are analyzed to extract the tumour seed point. The lesions segmented using a region-based approach are classified using a support vector machine (SVM) classifier. Evaluation of various performance measures show that the performance of the proposed CAD system is promising.
机译:医疗超声图像智能病变检测系统旨在减少癌症诊断过程中的医生努力。 由于图像的低对比度和嘈杂行为,超声图像中肿瘤的自动分离和分类是挑战。 本文提出了一种计算机辅助检测(CAD)系统,在超声(US)图像中自动分割和分类乳腺肿瘤。 所提出的方法是不变的缩放变化,并且不需要操作员定义的初始感兴趣区域。 分析了美国图像的小波模量最大点以提取肿瘤种子点。 使用基于区域的方法分割的病变使用支持向量机(SVM)分类器进行分类。 各种绩效措施的评价表明,拟议的CAD系统的表现是有前途的。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号