Fast recognition model for cerebrospinal fluid images based on sparse coding

doi:10.11772/j.issn.1001-9081.2014.07.2040

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (7): 2040-2043.DOI: 10.11772/j.issn.1001-9081.2014.07.2040

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Fast recognition model for cerebrospinal fluid images based on sparse coding

HUANG Wenming,CAI Wenzheng,DENG Zhenrong

School of Computer Science and Engineering, Guilin University of Electronic Technology, Guilin Guangxi 541004, China

Received:2014-01-10 Revised:2014-03-07 Online:2014-08-01 Published:2014-07-01
Contact: CAI Wenzheng

基于稀疏编码的脑脊液图像快速识别模型

黄文明,蔡文正,邓珍荣

桂林电子科技大学计算机科学与工程学院，广西桂林 541004

通讯作者: 蔡文正
作者简介:黄文明(1963-),男,江苏苏州人,教授,主要研究方向：网格计算、图形图像处理、软件工程、信息安全；蔡文正(1988-),男,上海人,硕士研究生,主要研究方向：图像识别、机器学习；邓珍荣(1977-),女,广西桂林人,副教授,硕士,主要研究方向：协议安全、软件架构。
基金资助:
广西自然科学基金资助项目

Abstract

Abstract:

Considering the traditional image segmentation algorithm was difficult to segment cerebrospinal fluid cell images accurately, a fast recognition model based on sparse coding for cerebrospinal fluid cell images was presented in this paper. First in this model local features and feature descriptors from the image were extracted by sparse coding. Then the feature descriptors were transformed into linear Spatial Pyramid Matching (SPM) structure. Finally, the calculated result was input into the linear Support Vector Machine (SVM) for training and prediction. In this paper, a test was made for recognizing abnormal cerebrospinal fluid cell images and classification, and the abnormal recognition accuracy rate of the experimental results was up to 89.4±0.9%, and the average recognition time of each 760×570 image is just 1.3 seconds. Therefore, the presented model can effectively and quickly distinguish normal and abnormal cerebrospinal fluid cell images.

摘要：

考虑到采用传统的图像分割算法很难准确地分割脑脊液(CSF)细胞图像，提出了一种基于稀疏编码的脑脊液图像快速识别模型。该模型首先利用稀疏编码提取图像中的局部特征以及特征描述子，然后将特征描述子转换成线性空间金字塔匹配(SPM)结构，最后将计算结果输入到线性支持向量机(SVM)中进行训练和预测。对脑脊液细胞图像做了异常识别和分类测试，其中异常识别准确率达到了89.4±0.9%，且对每张760×570的图像平均识别时间只需1.3s, 由此可以表明所提出的模型能够有效快速地区分脑脊液细胞是否异常。

CLC Number:

TP391.4

HUANG Wenming CAI Wenzheng DENG Zhenrong. Fast recognition model for cerebrospinal fluid images based on sparse coding[J]. Journal of Computer Applications, 2014, 34(7): 2040-2043.

黄文明蔡文正邓珍荣. 基于稀疏编码的脑脊液图像快速识别模型[J]. 计算机应用, 2014, 34(7): 2040-2043.

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Fast recognition model for cerebrospinal fluid images based on sparse coding

基于稀疏编码的脑脊液图像快速识别模型

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