首页> 外文会议>2010 IEEE Students' Technology Symposium (TechSym) >Automated characterization of sub-epithelial connective tissue cells of normal oral mucosa: Bayesian approach
【24h】

Automated characterization of sub-epithelial connective tissue cells of normal oral mucosa: Bayesian approach

机译:正常口腔黏膜上皮下结缔组织细胞的自动表征:贝叶斯方法

获取原文
获取原文并翻译 | 示例

摘要

The objective of this paper is to develop an automated cell classification system based on Bayesian classifier followed by segmentation using color deconvolution and feature extraction for characterizing various types of sub-epithelial connective tissue (SECT) cells from histological images. In the histological sections of oral mucosa, SECT layer mainly consists of three types of cells - inflammatory, fibroblast and endothelial cells; out of which only first two play significant role pertaining to precancerous changes in oral mucosa. In order to discriminate inflammatory and fibroblast cells, a set of mathematical features viz., area, perimeter, eccentricity, compactness, Zernike moments and Fourier descriptors are extracted followed by cell segmentation using color deconvolution method. The features are statiatically analysed to show its significance in cell discrimination. Thereafter, Bayesian classifier is implemented based on the defined feature space for characterizing inflammatory and fibroblast cells in order to observe the cell distribution in healthy state. The performance of this proposed system is evaluated with 97.19% overall classification accuracy.
机译:本文的目的是开发一种基于贝叶斯分类器的自动化细胞分类系统,然后使用颜色反卷积和特征提取进行分割,以从组织学图像中表征各种类型的上皮下结缔组织(SECT)细胞。在口腔粘膜的组织学切片中,SECT层主要由三种类型的细胞组成:炎性细胞,成纤维细胞和内皮细胞。其中只有前两个在口腔粘膜癌前变化中起重要作用。为了区分炎性和成纤维细胞,提取了一组数学特征,即面积,周长,偏心率,紧密度,Zernike矩和傅立叶描述子,然后使用颜色反卷积方法进行了细胞分割。对这些特征进行静态分析,以显示其在细胞识别中的重要性。此后,基于定义的特征空间实施贝叶斯分类器以表征炎性和成纤维细胞,以便观察健康状态下的细胞分布。以97.19%的整体分类精度评估了该提议系统的性能。

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