• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>2012 Third International Conference on Computing Communication amp; Networking Technologies. >Multiple classification system for fracture detection in human bone x-ray images
【24h】

Multiple classification system for fracture detection in human bone x-ray images

机译：用于人骨X射线图像中骨折检测的多分类系统

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

X-Ray is one the oldest and frequently used devices, that makes images of any bone in the body, including the hand, wrist, arm, elbow, shoulder, foot, ankle, leg (shin), knee, thigh, hip, pelvis or spine. A typical bone ailment is the fracture, which occurs when bone cannot withstand outside force like direct blows, twisting injuries and falls. Automatic detection of fractures in bone x-ray images is considered important, as humans are prone to miss-diagnosis. The main focus of this paper is to automatically detect fractures in long bones and in particular, leg bone (often referred as Tibia), from plain diagnostic X-rays using a multiple classification system. Two types of features (texture and shape) with three types of classifiers (Back Propagation Neural Network, K-Nearest Neighbour, Support Vector Machine) are used during the design of multiple classifiers. A total of 12 ensemble models are proposed. Experiments proved that ensemble models significantly improve the quality of fracture identification.
机译：X射线是最古老且经常使用的设备之一，它可以对人体任何骨骼进行成像，包括手，腕，臂，肘，肩膀，脚，脚踝，腿（胫），膝盖，大腿，臀部，骨盆或脊椎。典型的骨骼疾病是骨折，当骨骼无法承受直接打击，扭伤和跌倒等外力时会发生骨折。自动检测骨骼X射线图像中的骨折很重要，因为人类易于误诊。本文的主要重点是使用多分类系统从普通X射线诊断中自动检测长骨尤其是腿骨（通常称为胫骨）的骨折。在多个分类器的设计中，使用具有三种分类器的两种类型的特征（纹理和形状）（反向传播神经网络，K最近邻，支持向量机）。总共提出了12种集成模型。实验证明，集成模型显着提高了裂缝识别的质量。

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