基于点特异度和自适应分类策略的眼底图像分割方法

姜平; 窦全胜

doi:10.11959/j.issn.1000-436x.2015229

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基于点特异度和自适应分类策略的眼底图像分割方法

学术论文 | 更新时间：2024-06-05

- 基于点特异度和自适应分类策略的眼底图像分割方法
- Vessel segmentation of retinal image based on pixel specificity and self-adaptive classification strategy
- 通信学报 2015年36卷第8期页码：161-170
- 作者机构：
  
  1. 中央财经大学信息学院，北京 100081
  2. 河南工程学院管理工程学院，河南郑州 451191
- 作者简介：
  
  [ "姜平（1979-），男，山东烟台人，山东工商学院讲师，主要研究方向为医学图像处理、机器学习等。" ]
  [ "窦全胜（1971-），男，山东烟台人，博士，山东工商学院教授，主要研究方向为智能计算和数据挖掘、复杂自适应系统及其应用等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61272244);国家自然科学基金资助项目(61373079);国家自然科学基金资助项目(61175023);国家自然科学基金资助项目(61175053);国家自然科学基金资助项目(61272430);山东省自然科学基金资助项目(ZR2013FL022);教育部科学技术研究重点基金资助项目(2012101)
- DOI：10.11959/j.issn.1000-436x.2015229
  中图分类号： TP391
- 网络首发：2015-08，
  
  纸质出版：2015-08-25
- 稿件说明：
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姜平, 窦全胜. 基于点特异度和自适应分类策略的眼底图像分割方法[J]. 通信学报, 2015,36(8):161-170.

Ping JIANG, Quan-sheng DOU. Vessel segmentation of retinal image based on pixel specificity and self-adaptive classification strategy[J]. Journal on Communications, 2015, 36(8): 161-170.
姜平, 窦全胜. 基于点特异度和自适应分类策略的眼底图像分割方法[J]. 通信学报, 2015,36(8):161-170. DOI： 10.11959/j.issn.1000-436x.2015229.

Ping JIANG, Quan-sheng DOU. Vessel segmentation of retinal image based on pixel specificity and self-adaptive classification strategy[J]. Journal on Communications, 2015, 36(8): 161-170. DOI： 10.11959/j.issn.1000-436x.2015229.

摘要

提出基于点特异度和自适应分类策略的血管分割方法（SSVD

specificity and self-adaptive vessel detection），首先给出点特异度的定义，通过设置高点特异度阈值，实现主血管的提取，然后由多主体进行自适应像素分类，将每个未确定像素作为一个Agent，在多尺度点特异度阈值范围内，根据邻域Agent状态修订自身状态，逐步完成对像素的分类，最后通过多窗口去噪对噪音进行滤除完成对图像血管结构的分割。将SSVD方法应用到DRIVE数据库眼底图像的血管分割中，实验结果表明该方法要比现有其他方法具有更高的准确度和效率。

Abstract

A new vessel segmentation method called specificity and self-adaptive vessel detection(SSVD)was proposed based on pixel specificity and self-adaptive classification strategy

in the beginning pixel specificity was defined

by setting a higher pixel specificity threshold

the main vessel skeleton was extracted; then self-adaptive classification process was implemented

and each of the remaining undetermined pixels acted as an Agent

within a multi-scale threshold range

Agent revised its own status according to the status of its neighbor

so as to complete the classification of the pixels; finally the noise was removed by multi-window noise filtering method.By testing SSVD on DRIVE database

the experiment shows that it is more accurate and efficient than state-of-the-art methods.

关键词

Keywords

references

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