首页> 外文期刊>SID International Symposium: Digest of Technology Papers >A Study of CRT (5-Mpixel) vs. LCD (9-Mpixel) Displays for Breast Lesion Detection in Full-Field Digital Mammography and Ultrasound (FFDMUS) Data Sets via Image-Enhancement Algorithms
【24h】

A Study of CRT (5-Mpixel) vs. LCD (9-Mpixel) Displays for Breast Lesion Detection in Full-Field Digital Mammography and Ultrasound (FFDMUS) Data Sets via Image-Enhancement Algorithms

机译:CRT(5 Mpixel)vs LCD(9 Mpixel)显示器通过图像增强算法在全场数字乳房X线照相术和超声(FFDMUS)数据集中检测乳房病变的研究

获取原文
获取原文并翻译 | 示例
           

摘要

The latest technological changes are fast replacing the conventional cathode ray tube (CRT) displays with liquid crystal displays (LCDs). It is thus important to understand and evaluate them. The novelty of our evaluation strategy comes from the usage of computer aided diagnostics-based on pixel intensities. This evaluation system combines both lesion segmentation and quantification. Hence it is an integrated approach. The FFDMUS ultrasound data was acquired and then displayed on LCD and CRT displays. The FFDMUS ultrasound images were segmented using the signal-to-noise ratio (SNR) algorithm. We use Hausdoff distance measure (HDM) and polyline distance metric (PDM) for performance evaluation. Our results using the HDM method on FFDMUS ultrasound images show that lesions quantified from LCD images show a 29% improvement compared to lesions quantified from CRT images. A similar behavior was observed using PDM method. Hence we conclude that use of LCD displays for mammography applications with image enhancement techniques will have a greater diagnostic accuracy compared to the CRT displays.
机译:最新的技术变化正在迅速用液晶显示器(LCD)代替传统的阴极射线管(CRT)显示器。因此,理解和评估它们很重要。我们评估策略的新颖性来自基于像素强度的计算机辅助诊断的使用。该评估系统结合了病变分割和量化功能。因此,它是一种集成方法。采集FFDMUS超声数据,然后将其显示在LCD和CRT显示器上。使用信噪比(SNR)算法对FFDMUS超声图像进行分割。我们使用Hausdoff距离测度(HDM)和折线距离测度(PDM)进行性能评估。我们对FFDMUS超声图像使用HDM方法的结果表明,与从CRT图像量化的病变相比,从LCD图像量化的病变显示出29%的改善。使用PDM方法观察到类似的行为。因此,我们得出结论,与CRT显示器相比,将LCD显示器用于具有图像增强技术的乳腺摄影应用程序将具有更高的诊断准确性。

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