Blood cell image segmentation on color and GVF Snake for Leukocyte classification on SVM

WANG Wei-xing; SU Pei-yin

doi:10.3788/OPE.20122012.2781

您当前的位置：

首页 >

文章列表页 >

Blood cell image segmentation on color and GVF Snake for Leukocyte classification on SVM

Article | 更新时间：2020-08-13

- Blood cell image segmentation on color and GVF Snake for Leukocyte classification on SVM
- Optics and Precision Engineering Vol. 20, Issue 12, Pages: 2781-2790(2012)
- 作者机构：
  
  1. 福州大学物理与信息工程学院,福建福州 350000
  2. 瑞典皇家工学院,斯德哥尔摩,瑞典
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20122012.2781
  CLC： TP391.4
- Received：17 July 2012，
  
  Revised：30 August 2012，
  
  Published：10 December 2012
- 稿件说明：
移动端阅览
王卫星, 苏培垠. 基于颜色、梯度矢量流活动轮廓及支持向量机实现白细胞的提取和分类[J]. 光学精密工程, 2012,20(12): 2781-2790

WANG Wei-xing, SU Pei-yin. Blood cell image segmentation on color and GVF Snake for Leukocyte classification on SVM[J]. Editorial Office of Optics and Precision Engineering, 2012,20(12): 2781-2790
王卫星, 苏培垠. 基于颜色、梯度矢量流活动轮廓及支持向量机实现白细胞的提取和分类[J]. 光学精密工程, 2012,20(12): 2781-2790 DOI： 10.3788/OPE.20122012.2781.

WANG Wei-xing, SU Pei-yin. Blood cell image segmentation on color and GVF Snake for Leukocyte classification on SVM[J]. Editorial Office of Optics and Precision Engineering, 2012,20(12): 2781-2790 DOI： 10.3788/OPE.20122012.2781.

摘要

提出了一种基于图像技术实现白细胞分类的方法。首先

利用彩色图像的信息转换、距离变换和梯度矢量流活动轮廓(GVF Snake)等方法从血液细胞图像中提取出白细胞;然后

利用细胞核在图像中具有较高颜色饱和度的特点

结合数学形态学和GVF Snake方法从白细胞中精确地提取出细胞核。最后

根据细胞的形态、颜色及纹理特征用支持向量机(SVM)对白细胞进行分类。实验结果表明:在上述图像分割的基础上

基于支持向量机分类器的方法对白细胞进行分类

分类准确度能够达到89.6%。与其他传统的分割和分类的方法相比

本文提出的方法具有一定的优越性。

Abstract

A leukocyte classification method was proposed by using image technologies.Firstly

based on image color information

image distance transformation and the Snake of Gradient Vector Flow (GVF Snake)

the leukocytes were extracted in a blood cell image

and then the high saturation trait of the leukocyte nuclei was combined the morphological mathematics and GVF Snake to detect the nuclei in the leukocyte image. According to the features of morphometry

color and texture for cells

the Support Vector Machines (SVMs) were taken to classify the leukocytes. The results show that the proposed image segmentation method and the classifier to classify the leukocytes can achieve the accuracy by 89.6%. Compared to other traditional cell image segmentation and analysis methods

the proposed method is satisfactory.

关键词

Keywords

references

曾明,孟庆浩,张建勋,等. 基于形态特征和 SVM的血细胞核自动分析[J]. 计算机工程, 2008, 34(2):14-19. ZENG M, MENG Q H, ZHANG J X, et al.. Automatic analysis system of blood nuclei based on morphological features and support vector machines [J]. Computer Engineering, 2008, 34(2):14-19. (in Chinese)[2] HERBERT R, VINCENT L, HORST B,et al..Leukocyte segmentation and classification in blood smear images. Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. China, IEEE, 2005:3371-3374.[3] MADHUMALA G, DEVKUMAR D, SUBHODIP M, et al.. Statistical pattern analysis of white blood cell nuclei morphometry. Proceedings of the 2010 IEEE Students' Technology Symposium. India,2010:51-66.[4] XIE E,MCGINNITY T,WU Q X. Automatic extraction of shape features for classification of leukocyte. 2010 International Conference on Artificial Intelligence and Computational Intelligence, China, 2010:220-224.[5] 印勇,王云,刘丹平. 血细胞图像分割的改MEANSHIFH方法[J]. 计算机工程与应用, 2010, 46(6):178-180. YING Y, WANG Y, LIU D P. Improved MEANSHIFT method for blood cell image segmetation [J]. Computer Engineering and Applications, 2010, 46(6):178-180. (in Chinese)[6] SEZGIN M, SANKUR B. Survey over image thresholding techniques and quantitative performance evaluation [J]. Journal of Electronic Imaging, 2004, 13(1):146-165.[7] RAFAEL C G,RICHARD E W. Digital Image Processing[M]. 2nd ed. BeiJing: Publishing House of Electronics Industry, 2009.(in Chinese)[8] 张荣国,刘小君,王蓉,等. 自适应梯度矢量流轮廓提取方法[J]. 模式识别与人工智能, 2008, 21(6):799-805. ZHANG R G, LIU X J,WANG R, et al.. Adaptive gradient vector flow algorithm for boundary extraction [J]. Pattern Recognition and Artificial Intelligence, 2008, 21(6):799-805. (in Chinese)[9] XU C, PRINCE J L. Snakes, shapes and gradient vector flow[J]. IEEE Transactions On Image Processing, 1998, 7(3): 359-369.[10] ARTHORN S,WORANUT I,CHUCHART P, et al.. White blood cell segmentation by distance mapping active contour. 2008 International Symposium on Communications and Information Technologies, Thailand, 2008:251-255.[11] THEERAPATTANAKUL J,POLDPAI J,PINTAVIOOJ C. An efficient method for segmentation step of automated white blood cell classifications. 2004 IEEE Region 10 Conference. China,2004:191-194.[12] 周志宇,杨卫成,汪亚明,等. 应用梯度矢量流 Snake 和灰预测的人脸跟踪[J]. 光学精密工程,2011,19(11):2744-2751. ZHOU Z Y, YANG W C, WANG Y M, et al.. Realization of face contour tracking by GVF Snake and grey prediction[J]. Opt. Precision Eng., 2011,19(11): 2744-2751.(in Chinese)[13] SUBRAJEET M,SUSHANTA S S,DIPTI P, et al.. Fuzzy based blood image segmentation for automated leukemia detection. 2011 International Conference on Devices and Communications, India,2011:1-5.[14] COMANICIU D,MEER P. Mean shift: a robust approach toward feature space analysis [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(5):603-619. [15] 颜佳,昊敏渊,陈淑珍,等. 跟踪窗口自适应的 Mean Shift 跟踪[J]. 光学精密工程, 2009,17(10): 2606-2611. YAN J,WU M Y,CHEN SH ZH, et al.. Mean shift tracking with adaptive tracking window[J]. Opt. Precision Eng., 2009, 17(10): 2606-2611. (in Chinese)[16] 薛陈,朱明,陈爱华. 鲁棒的基于改进 Mean-shift 的目标跟踪[J]. 光学精密工程, 2010,18(1): 234-239. XUE C, ZHU M, CHEN A H. Robust object tracking based on improved Mean-shift algorithm[J]. Opt. Precision Eng.,2010,18(1):234-239. (in Chinese)[17] CORTES C, VAPNIK V N. Support vector network [J]. Machine Learning, 1995, 20(3): 273-297.[18] 邓乃扬, 田英杰. 支持向量机理论、算法与拓展[M]. 北京:科学出版社, 2009. DENG N Y,TIAN Y J. Support Vector Machines(Theory, Algorithm and Development)[M]. Beijing: Science Press, 2009.(in Chinese)[19] 白鹏,张喜斌,张斌,等.支持向量机理论及工程应用实例[M]. 西安:西安电子科技大学出版社, 2008. BAI P, ZHANG X B, ZHANG B, et al..Support Vector Machine and its Application in Mixed Gas Infrared Spectrum Analysis[M]. XI AN: XiDian University Press,2008.(in Chinese)[20] 高恒振, 万建伟, 粘永健,等. 组合核函数支持向量机高光谱图像融合分类[J]. 光学精密工程, 2011,19(4): 878-883. GAO H Z,WANG J W, NIAN Y J et al.. Fusion classification of hyperspectral image by composite kernels support vector machine[J]. Opt. Precision Eng.,2011,19(4):878-883. (in Chinese)

Views

312

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Dermoscopic image classification based on multi-scale and three-dimensional interaction feature optimization

A lightweight deep learning model for TFT-LCD circuits defect classification based on swin transformer

Compressive sensing fast classification for handwritten digital images

Co-training generative adversarial networks for semi-supervised classification method

Automatic fault recognition algorithm for key parts of train based on sparse coding based spatial pyramid matching and GA-SVM

Related Author

LI Jing

LÜ Xiaoqi

WANG Di

LUO Chen

XIA Yan

ZHOU Yijun

JIA Lei

Ru-peng FENG

Related Institution

School of Information Engineering， Inner Mongolia University of Technology

School of Digital and Intelligence Industry， Inner Mongolia University of Science and Technology

Wuxi Shangshi-finevision Technology Co.， Ltd

School of Mechanical Engineering， Southeast University

Key Laboratory of Space-based Dynamic & Rapid Optical Imaging Technology， Chinese Academy of Sciences

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰