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Simple Landscapes Analysis for Relevant Regions Detection in Breast Carcinoma Histopathological Images

机译:乳腺癌组织病理学图像相关区域检测的简单景观分析

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摘要

Breast carcinoma represents a huge global health problem among women in both developed and developing countries. It is estimated that over 508,000 women worldwide died in 2011 due to breast carcinoma. Nottingham Histological Grading (NHG) system is recognized as the gold standard to provide overall grade for breast carcinoma. One of the breast carcinoma criteria considered in the grading system is tubule formation. The assessment of tubule formation starts with visual inspection on breast histopathological image using 10x magnification. However, not all regions in the image provide meaningful information. Histopathological image with score 3 in tubule formation usually has a small tubule size. Thus, a visual inspection at a higher magnification is required. A continuous inspection at a higher magnification is time consuming. By eliminating the irrelevant regions in the histopathological image, histopathologist can focus on the relevant region for further examination. This study proposed a simple method to detect relevant region on the breast histopathological images using landscape analysis. The proposed method was tested using three groups of histopathological images: Group 1: relevant and irrelevant regions, Group 2: relevant regions only and Group 3: irrelevant regions only. The proposed method is found to be effective in eliminating irrelevant regions as the overall accuracy for Groups 1, 2 and 3 are 86.6%, 100.0% and 100.0%, respectively.
机译:乳腺癌在发达国家和发展中国家的妇女中都代表着巨大的全球健康问题。据估计,2011年全世界有508,000多名妇女死于乳腺癌。诺丁汉组织学分级(NHG)系统被认为是提供乳腺癌总体评分的金标准。在分级系统中考虑的乳腺癌标准之一是小管形成。肾小管形成的评估始于使用10倍放大倍数对乳房组织病理学图像进行目视检查。但是,并非图像中的所有区域都提供有意义的信息。在肾小管形成中得分为3的组织病理学图像通常具有较小的肾小管尺寸。因此,需要以更高的倍率进行目视检查。以更高的放大倍率进行连续检查非常耗时。通过消除组织病理学图像中不相关的区域,组织病理学家可以专注于相关区域以进行进一步检查。这项研究提出了一种简单的方法,可以使用景观分析来检测乳房组织病理学图像上的相关区域。使用三组组织病理学图像对提出的方法进行了测试:第一组:相关区域和不相关区域;第二组:仅相关区域;第三组:仅无关区域。由于第1、2和3组的整体准确度分别为86.6%,100.0%和100.0%,发现该方法可有效消除无关区域。

著录项

  • 来源
    《》|2018年|1-5|共5页
  • 会议地点 Kuching(MY)
  • 作者单位

    University Malaysia Perlis (UniMAP), School of Mechatronic Engineering, Arau, Perlis, 02600, Malaysia;

    University Malaysia Perlis (UniMAP), School of Mechatronic Engineering, Arau, Perlis, 02600, Malaysia;

    University Malaysia Perlis (UniMAP), School of Mechatronic Engineering, Arau, Perlis, 02600, Malaysia;

    Hospital Tuanku Fauziah, Clinical Research Centre, Kangar, Perlis, 01000, Malaysia;

    Department of Pathology, Hospital Tuanku Fauziah, Kangar, Perlis, 01000, Malaysia;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Breast; Image color analysis; Standards; Inspection; Visualization; Pathology;

    机译:乳房;图像颜色分析;标准;检查;可视化;病理学;;

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