Recognition study on positioning line transection of nerve slice image

doi:10.3778/j.issn.1002-8331.2010.22.059

Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (22): 203-205.DOI: 10.3778/j.issn.1002-8331.2010.22.059

• 图形、图像、模式识别 • Previous Articles Next Articles

Recognition study on positioning line transection of nerve slice image

LI Fang¹，ZHONG Ying-chun²，ZHANG Yi³，QI Jian³，LIU Xiao-lin³

1.Information College，Guangdong University of Business Studies，Guangzhou 510320，China
2.Automatic College，Guangdong University of Technology，Guangzhou 510090，China
3.Department of Plastic and Reconstructive Surgery，The First Affiliated Hospital of Sun Yat-sen University，Guangzhou 510080，China

Received:2009-01-08 Revised:2009-03-26 Online:2010-08-01 Published:2010-08-01
Contact: LI Fang

神经切片图像中识别定位线断面的研究

李芳¹，钟映春²，张毅³，戚剑³，刘小林³

1.广东商学院信息学院，广州 510320
2.广东工业大学自动化学院，广州 510090
3.中山大学附属第一医院显微修复外科，广州 510080

通讯作者: 李芳

Abstract

Abstract: It is very important basic of nerve slice image registration to recognize the positioning line transaction in the slice image.On the basis of the feature analyzing of the positioning line transection，this paper obtains the feature set of position line transaction and calculates the prior probability and probability density function.Then the binary Bayes classification is designed and employed to recognize the position line transaction in the slice image.The result of experiment shows that recognizing accuracy reaches 90.5% while employing the method to recognize 200 nerve slice images.The recognition speed of binary Bayes classification is faster than the probabilistic neural network obviously while they have the same recognizing accuracy.

Key words: nerve slice imge, Bayes, positioning line transection

摘要： 识别在神经切片图像中的定位线断面是实现神经切片图像配准的前提。在分析了定位线断面特征的基础上，提取了定位线断面的特征集合并计算了先验概率和类概率密度函数，设计了二值数据的贝叶斯分类器，再用该分类器识别神经切片图像中的定位线断面，得到定位线断面中心像素坐标。实验表明，采用二值数据的贝叶斯分类器识别准确率可以达到90.5%，其识别精度与神经网络识别方法相同，但是识别速度显著快于神经网络识别方法。这为后续进行神经切片图像的配准奠定了基础。

关键词: 神经切片图像, 贝叶斯, 定位线断面

CLC Number:

TP391

LI Fang¹，ZHONG Ying-chun²，ZHANG Yi³，QI Jian³，LIU Xiao-lin³. Recognition study on positioning line transection of nerve slice image[J]. Computer Engineering and Applications, 2010, 46(22): 203-205.

李芳¹，钟映春²，张毅³，戚剑³，刘小林³. 神经切片图像中识别定位线断面的研究[J]. 计算机工程与应用, 2010, 46(22): 203-205.

[1]	ZHAO Yu, ZHU Yi, YU Qiao, CHEN Xiaoying. Cross Project Defect Prediction Method Based on Hierarchical Data Screening [J]. Computer Engineering and Applications, 2021, 57(20): 279-286.
[2]	LI Jie, LI Miao, YUAN Xiguo. Detection Algorithm?of Pathogenic Microbes from Next-Generation Sequencing Data [J]. Computer Engineering and Applications, 2021, 57(19): 282-289.
[3]	GUO Xuekun, QIN Yuanhui, DU Liang, LUO Yongliang, WANG Hongan. Flight Deck Aviation Support Job Detection Based on Multi-entity Bayesian Networks [J]. Computer Engineering and Applications, 2021, 57(1): 242-253.
[4]	CHEN Haiyang, LIU Xiqing, HUAN Xiaomin. One-step Prediction SVDDBN Missing Data Interpolation Algorithm [J]. Computer Engineering and Applications, 2020, 56(7): 81-87.
[5]	AN Weipeng, CHENG Xiaobo, LIU Yu. Application of Fleiss’ Kappa Coefficient in Bayesian Decision Tree Algorithm [J]. Computer Engineering and Applications, 2020, 56(7): 137-140.
[6]	ZHANG Suisui, HUANG Lixia, WANG Jie, ZHANG Xueying. Localization of Sound Source with Classification of Cross-Correlation Function Within Microphone Array [J]. Computer Engineering and Applications, 2020, 56(4): 128-133.
[7]	DU Jing, TENG Jing, MA Bian, ZHANG Aoxin. Breast Cancer Patients Prognostic Analysis Using Bayesian Method [J]. Computer Engineering and Applications, 2020, 56(3): 146-151.
[8]	ZHANG Songhui, XIONG Hanjiang. Point-of-Interest Recommendation Algorithm Based on Poisson Factorization and Neural Network [J]. Computer Engineering and Applications, 2020, 56(21): 176-186.
[9]	HAN Suqing, CHENG Huiwen, WANG Baoli. Novel Naïve Bayesian Incremental Learning Algorithm Based on Three-Way Decisions [J]. Computer Engineering and Applications, 2020, 56(18): 42-49.
[10]	YU Qiancheng, YU Zhiwen, WANG Zhu. Estimating Model Posterior Inference Quality by Using Adversarial Samples to Train Graph Classifier [J]. Computer Engineering and Applications, 2020, 56(17): 142-149.
[11]	DING Na, ZHONG Baojiang. Handwritten Transistor Numerals and Recognition [J]. Computer Engineering and Applications, 2020, 56(16): 97-104.
[12]	DING Qiliang, SHI Zezhi, LI Jianhua. Drug-Target Interaction Prediction Based on Grouped Bayesian Ranking Approach [J]. Computer Engineering and Applications, 2020, 56(15): 185-190.
[13]	ZHENG Wei, HOU Hongxu, BAN Zhijie. Expert Finding Method Using Baysian Network on Query Semantic Extension [J]. Computer Engineering and Applications, 2020, 56(13): 194-198.
[14]	CHANG Zhen, DUAN Xianhua, LU Wenchao, PENG Yuan. Multi-scale Saliency Detection Based on Bayesian Framework [J]. Computer Engineering and Applications, 2020, 56(11): 207-213.
[15]	LV Xuezhi1，2, HU Xiaofeng1, WU Lin1, HE Xiaoyuan1. Identification Method of Mission Community Based on Bayesian Network [J]. Computer Engineering and Applications, 2019, 55(5): 251-257.

Recognition study on positioning line transection of nerve slice image

神经切片图像中识别定位线断面的研究

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics