Segmentation Method to Adhesion Macrophage Image Based on Seeding Point

WANG Ping; ZHANG Li; ZHOU Chang-qi

doi:10.3788/YJYXS20122706.0808

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Segmentation Method to Adhesion Macrophage Image Based on Seeding Point

Imaging Technology & Image Processing | 更新时间：2020-08-12

- Segmentation Method to Adhesion Macrophage Image Based on Seeding Point
- Chinese Journal of Liquid Crystals and Displays Vol. 27, Issue 6, Pages: 808-813(2012)
- 作者机构：
  
  1. 南昌大学信息工程学院电子信息工程系,江西南昌,330031
  2. 南昌大学信息工程学院自动化系, 江西南昌 330031
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20122706.0808
  CLC： TP391.41
- Received：16 July 2012，
  
  Revised：12 August 2012，
  
  Published：15 December 2012
- 稿件说明：
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王平, 张力, 周长其. 基于种子点的粘连巨噬细胞图像的分割方法[J]. 液晶与显示, 2012,(6): 808-813

WANG Ping, ZHANG Li, ZHOU Chang-qi. Segmentation Method to Adhesion Macrophage Image Based on Seeding Point[J]. , 2012,(6): 808-813
王平, 张力, 周长其. 基于种子点的粘连巨噬细胞图像的分割方法[J]. 液晶与显示, 2012,(6): 808-813 DOI： 10.3788/YJYXS20122706.0808.

WANG Ping, ZHANG Li, ZHOU Chang-qi. Segmentation Method to Adhesion Macrophage Image Based on Seeding Point[J]. , 2012,(6): 808-813 DOI： 10.3788/YJYXS20122706.0808.

摘要

粘连细胞的分割是医学细胞图像处理中的关键

随着形态学在图像分割中的应用

分水岭分割成为粘连细胞分割中最有效的方法之一。然而

由于巨噬细胞图像中的噪声和形态的不规则性

传统分水岭算法容易产生过分割。若在经距离变换得到的距离图中提取"种子点"

并将其中距离小于某一阈值的两两"种子点"合并得到新的"种子点"

在新"种子点"重新分布的距离图基础上使用分水岭分割。实验结果表明:该算法虽然要比传统分水岭算法多花费些时间

但可以有效地分割粘连巨噬细胞并抑制过分割现象。分割后

对细胞进行标记并提取细胞的特征参数

如周长、面积、圆度因子、灰度均值等。

Abstract

Segmentation to adhesion cells is critical in image segmentation realm

with the morphology being used in image segmentation

watershed becomes one of the most efficient skills in solving adhesion cells. However

the traditional watershed algorithm is apt to cause over-segmentation for the noise and irregular shape of macrophages.If extracting the "seeding points" from the distance map

two "seeding points" is combined into one "seeding point" if the distance between them is under threshold after count the distances between each two "seeding points". The redistribution distanee map depending on the new seeding points is segmented by watershed. The simulation results show that the algorithm can effectively segment adhesion macrophages as well as restrain the over-segmentation phenomena with a little more time compared with the traditional method.After segmentation

we can mark the numbers to the cells and abstract the characteristic parameters such as circumference

area

roundness factor

mean gray and so on.

关键词

Keywords

references

Vincent L,Soille P.Watersheds in digital spaces:An eficient algorithm based on immersion simulations [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1991,13(6):583-598.[2] Kim J B,Kim H J.Multiresolution-based watersheds for efficient image segmentation [J].Pattern Recognition Letters,2003,24(1):473-488.[3] 卢中宁,强赞霞.基于梯度修正和区域合并的分水岭分割算法 [J]. 计算机工程与设计, 2009,30(8):2075-2077.[4] 范小聪,李大海,王琼华,等.基于背景重构的视频分割技术及应用 [J]. 液晶与显示, 2011,26(2):229-233.[5] 韩希珍,陈朝东,赵建.基于PDE的非线性图像去噪与增强 [J]. 液晶与显示, 2011,26(1):111-115.[6] Otsu N.A threshold selection method from gray-level histograms [J]. IEEE Transactions on Systems,Man and Cybernetics, 1979,9(1):62-66.[7] Gonzalez R C,Woods R E.Digital Image Processing [M].北京:电子工业出版社,2010:688-692.[8] 史国凯,王琼华,李大海,等.基于分割的离焦图像深度图提取方法 [J]. 液晶与显示, 2012,27(2):229-234.[9] Borgefors G.Transformations in arbitrary dimensions [J].Computer Vision, Graphics and Image Processing, 1984, 27:321-345.[10] Smet P D,Pires R L.Implementation and Analysis of an optimized rain falling watershed algorithm [C]//Proc. of SPIE'00. San Diego, USA:SPIE, 2000: 759-766.

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