Adaptive segmentation of digital mammograms through reinforcement learning

LIU Xin-yue; FANG Xiao-xuan; HUANG Lian-qing

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Adaptive segmentation of digital mammograms through reinforcement learning

更新时间：2020-08-12

- Adaptive segmentation of digital mammograms through reinforcement learning
- Adaptive segmentation of digital mammograms through reinforcement learning
- 光学精密工程 2005年13卷第5期页码：575-583
- 作者机构：
  
  1. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences Changchun,China,130033
  2. Japan Union Hosptial of Jilin University,,China Changchun,China,130033
- 作者简介：
- 基金信息：
- DOI：
  中图分类号：
- 收稿日期：2005-06-24，
  
  修回日期：2005-07-02，
  
  网络出版日期：2005-10-30，
  
  纸质出版日期：2005-10-30
- 稿件说明：
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LIU Xin-yue, FANG Xiao-xuan, HUANG Lian-qing. Adaptive segmentation of digital mammograms through reinforcement learning[J]. 光学精密工程, 2005,13(5):575-583.

LIU Xin-yue, FANG Xiao-xuan, HUANG Lian-qing. Adaptive segmentation of digital mammograms through reinforcement learning[J]. Optics and precision engineering, 2005, 13(5): 575-583.
LIU Xin-yue, FANG Xiao-xuan, HUANG Lian-qing. Adaptive segmentation of digital mammograms through reinforcement learning[J]. 光学精密工程, 2005,13(5):575-583. DOI：

LIU Xin-yue, FANG Xiao-xuan, HUANG Lian-qing. Adaptive segmentation of digital mammograms through reinforcement learning[J]. Optics and precision engineering, 2005, 13(5): 575-583. DOI：

摘要

An approach based on reinfocement learning for the automated segmentation is presented. The approach consists of two modules:segmentation module and learning module. The segmentation module uses the region-growing algorithm combined with the smooth filtering and the morphological filtering to segment mammograms. The learning module uses the segmentation output as the feedback to learn to select the optimal parameter settings of the segmentation algorithm according to the image properties using reinforcement learning techniques. The approach can adapt itself to various kinds of mammograms through training and therefore obviates the tedious and error-prone tuning of parameter settings manually. Quantitative test results show that the approach is accurate for several kinds of mammograms. Compared to previously proposed approaches

the approach is more adaptable to different mammograms.

Abstract

the approach is more adaptable to different mammograms.

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references

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