Abstract

Unlike urine or blood samples with a single background, human fecal samples contain large amounts of food debris, amorphous particles, and undigested plant cells. It is difficult to segment such impurities when mixed with leukocytes. Cell degradation results in ambiguous nuclei, incompleteness of the cell membrane, and a changeable cell morphology, which are difficult to recognize. Aiming at the segmentation problem, a threshold segmentation method combining an inscribed circle and circumscribed circle is proposed to effectively remove the adhesion impurities with a segmentation accuracy reaching 97.6%. For the identification problem, five texture features (i.e., LBP-uniform, Gabor, HOG, GLCM, and Haar) were extracted and classified using four kinds of classifiers (support vector machine (SVM), artificial neural network, AdaBoost, and random forest). The experimental results show that using a histogram of oriented gradient features with an SVM classifier can achieve precision of 88.46% and recall of 88.72%.

© 2018 Optical Society of America

Full Article  |  PDF Article
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References

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    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref]
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  10. L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” in Eleventh International Conference of the Center for Nonlinear Studies on Experimental Mathematics: Computational Issues in Nonlinear Science: Computational Issues in Nonlinear Science (Elsevier North-Holland, Inc., 1992), pp. 259–268.
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  12. J. Zhang, Y. Zhong, X. Wang, G. Ni, X. Du, J. Liu, L. Liu, and Y. Liu, “Computerized detection of leukocytes in microscopic leukorrhea images,” Med. Phys. 44, 4620–4629 (2017).
    [Crossref]
  13. T. Ojala, M. Pietikäinen, and T. Mäenpää, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002).
    [Crossref]
  14. R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. SMC-3, 610–621 (1973).
    [Crossref]
  15. N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in IEEE Computer Society Conference on Computer Vision & Pattern Recognition (IEEE Computer Society, 2005), pp. 886–893.
  16. O. M. Papageorgiou and T. Poggio, “A general framework for object detection,” in International Conference on Computer Vision (IEEE, 2002), pp. 555–562.
  17. P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2003), Vol. 1, pp. I-511–I-518.
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    [Crossref]

2017 (6)

J. Zhao, M. Zhang, Z. Zhou, J. Chu, and F. Cao, “Automatic detection and classification of leukocytes using convolutional neural networks,” Med. Biol. Eng. Comput. 55, 1287–1301 (2017).
[Crossref]

J. W. Choi, Y. Ku, B. W. Yoo, J. A. Kim, D. S. Lee, Y. J. Chai, H. J. Kong, and H. C. Kim, “White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks,” Plos One 12, e0189259 (2017).
[Crossref]

O. Sarrafzade, A. M. Dehnavi, H. Y. Banaem, A. Talebi, and A. Gharibi, “The best texture features for leukocytes recognition,” J. Med. Signals Sens. 7, 220–227 (2017).

X. Gao, H. Xue, X. Pan, J. Xinhua, Z. Yangqing, and L. Xiaoling, “Somatic cells recognition by application of Gabor Features-Based (2D)2 PCA,” Int. J. Pattern Recognit. Artif. Intell. 31, 1757009 (2017).
[Crossref]

J. Zhang, Y. Zhong, X. Wang, G. Ni, X. Du, J. Liu, L. Liu, and Y. Liu, “Computerized detection of leukocytes in microscopic leukorrhea images,” Med. Phys. 44, 4620–4629 (2017).
[Crossref]

E. Purwanti and E. Calista, “Detection of acute lymphocyte leukemia using k-nearest neighbor algorithm based on shape and histogram features,” J. Phys. Conf. Ser. 853, 012011 (2017).
[Crossref]

2016 (1)

X. Li, J. Liu, Z. Liu, X. He, C. Zhang, H. Yuan, F. Liu, and C. Zheng, “Automatic detection of leukocytes for cytometry with color decomposition,” Optik 127, 11901–11910 (2016).
[Crossref]

2012 (1)

C. T. Vu, T. D. Phan, and D. M. Chandler, “S3: a spectral and spatial measure of local perceived sharpness in natural images,” IEEE Trans. Image Process 21, 934–945 (2012).
[Crossref]

2002 (1)

T. Ojala, M. Pietikäinen, and T. Mäenpää, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002).
[Crossref]

1973 (1)

R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. SMC-3, 610–621 (1973).
[Crossref]

Banaem, H. Y.

O. Sarrafzade, A. M. Dehnavi, H. Y. Banaem, A. Talebi, and A. Gharibi, “The best texture features for leukocytes recognition,” J. Med. Signals Sens. 7, 220–227 (2017).

Calista, E.

E. Purwanti and E. Calista, “Detection of acute lymphocyte leukemia using k-nearest neighbor algorithm based on shape and histogram features,” J. Phys. Conf. Ser. 853, 012011 (2017).
[Crossref]

Cao, F.

J. Zhao, M. Zhang, Z. Zhou, J. Chu, and F. Cao, “Automatic detection and classification of leukocytes using convolutional neural networks,” Med. Biol. Eng. Comput. 55, 1287–1301 (2017).
[Crossref]

Chai, Y. J.

J. W. Choi, Y. Ku, B. W. Yoo, J. A. Kim, D. S. Lee, Y. J. Chai, H. J. Kong, and H. C. Kim, “White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks,” Plos One 12, e0189259 (2017).
[Crossref]

Chandler, D. M.

C. T. Vu, T. D. Phan, and D. M. Chandler, “S3: a spectral and spatial measure of local perceived sharpness in natural images,” IEEE Trans. Image Process 21, 934–945 (2012).
[Crossref]

Chen, F.

W. Wang, M. Si, F. Chen, H. Liu, and X. Jiang, “Study on recognition method of label-free red and white cell using fecal microscopic image,” in Proceedings of the 2018 6th International Conference on Bioinformatics and Computational Biology (ACM, 2018), pp. 95–100.

Choi, J. W.

J. W. Choi, Y. Ku, B. W. Yoo, J. A. Kim, D. S. Lee, Y. J. Chai, H. J. Kong, and H. C. Kim, “White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks,” Plos One 12, e0189259 (2017).
[Crossref]

Chu, J.

J. Zhao, M. Zhang, Z. Zhou, J. Chu, and F. Cao, “Automatic detection and classification of leukocytes using convolutional neural networks,” Med. Biol. Eng. Comput. 55, 1287–1301 (2017).
[Crossref]

Dalal, N.

N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in IEEE Computer Society Conference on Computer Vision & Pattern Recognition (IEEE Computer Society, 2005), pp. 886–893.

Dehnavi, A. M.

O. Sarrafzade, A. M. Dehnavi, H. Y. Banaem, A. Talebi, and A. Gharibi, “The best texture features for leukocytes recognition,” J. Med. Signals Sens. 7, 220–227 (2017).

Dinstein, I.

R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. SMC-3, 610–621 (1973).
[Crossref]

Dong, A.

H. Tan, H. Jiang, A. Dong, B. Yang, and L. Zhang, “C-V level set based cell image segmentation using color filter and morphology,” in International Conference on Information Science, Electronics and Electrical Engineering (IEEE, 2014, pp. 1073–1077.

Du, X.

J. Zhang, Y. Zhong, X. Wang, G. Ni, X. Du, J. Liu, L. Liu, and Y. Liu, “Computerized detection of leukocytes in microscopic leukorrhea images,” Med. Phys. 44, 4620–4629 (2017).
[Crossref]

Fatemi, E.

L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” in Eleventh International Conference of the Center for Nonlinear Studies on Experimental Mathematics: Computational Issues in Nonlinear Science: Computational Issues in Nonlinear Science (Elsevier North-Holland, Inc., 1992), pp. 259–268.

Fevens, T.

M. Habibzadeh, A. Krzyżak, and T. Fevens, “White blood cell differential counts using convolutional neural networks for low resolution images,” in International Conference on Artificial Intelligence and Soft Computing (2013).

Gao, X.

X. Gao, H. Xue, X. Pan, J. Xinhua, Z. Yangqing, and L. Xiaoling, “Somatic cells recognition by application of Gabor Features-Based (2D)2 PCA,” Int. J. Pattern Recognit. Artif. Intell. 31, 1757009 (2017).
[Crossref]

Gharibi, A.

O. Sarrafzade, A. M. Dehnavi, H. Y. Banaem, A. Talebi, and A. Gharibi, “The best texture features for leukocytes recognition,” J. Med. Signals Sens. 7, 220–227 (2017).

Habibzadeh, M.

M. Habibzadeh, A. Krzyżak, and T. Fevens, “White blood cell differential counts using convolutional neural networks for low resolution images,” in International Conference on Artificial Intelligence and Soft Computing (2013).

Haralick, R. M.

R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. SMC-3, 610–621 (1973).
[Crossref]

He, X.

X. Li, J. Liu, Z. Liu, X. He, C. Zhang, H. Yuan, F. Liu, and C. Zheng, “Automatic detection of leukocytes for cytometry with color decomposition,” Optik 127, 11901–11910 (2016).
[Crossref]

Jiang, H.

H. Tan, H. Jiang, A. Dong, B. Yang, and L. Zhang, “C-V level set based cell image segmentation using color filter and morphology,” in International Conference on Information Science, Electronics and Electrical Engineering (IEEE, 2014, pp. 1073–1077.

Jiang, X.

W. Wang, M. Si, F. Chen, H. Liu, and X. Jiang, “Study on recognition method of label-free red and white cell using fecal microscopic image,” in Proceedings of the 2018 6th International Conference on Bioinformatics and Computational Biology (ACM, 2018), pp. 95–100.

Jones, M.

P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2003), Vol. 1, pp. I-511–I-518.

Kim, H. C.

J. W. Choi, Y. Ku, B. W. Yoo, J. A. Kim, D. S. Lee, Y. J. Chai, H. J. Kong, and H. C. Kim, “White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks,” Plos One 12, e0189259 (2017).
[Crossref]

Kim, J. A.

J. W. Choi, Y. Ku, B. W. Yoo, J. A. Kim, D. S. Lee, Y. J. Chai, H. J. Kong, and H. C. Kim, “White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks,” Plos One 12, e0189259 (2017).
[Crossref]

Kong, H. J.

J. W. Choi, Y. Ku, B. W. Yoo, J. A. Kim, D. S. Lee, Y. J. Chai, H. J. Kong, and H. C. Kim, “White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks,” Plos One 12, e0189259 (2017).
[Crossref]

Krzyzak, A.

M. Habibzadeh, A. Krzyżak, and T. Fevens, “White blood cell differential counts using convolutional neural networks for low resolution images,” in International Conference on Artificial Intelligence and Soft Computing (2013).

Ku, Y.

J. W. Choi, Y. Ku, B. W. Yoo, J. A. Kim, D. S. Lee, Y. J. Chai, H. J. Kong, and H. C. Kim, “White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks,” Plos One 12, e0189259 (2017).
[Crossref]

Lee, D. S.

J. W. Choi, Y. Ku, B. W. Yoo, J. A. Kim, D. S. Lee, Y. J. Chai, H. J. Kong, and H. C. Kim, “White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks,” Plos One 12, e0189259 (2017).
[Crossref]

Li, X.

X. Li, J. Liu, Z. Liu, X. He, C. Zhang, H. Yuan, F. Liu, and C. Zheng, “Automatic detection of leukocytes for cytometry with color decomposition,” Optik 127, 11901–11910 (2016).
[Crossref]

Liu, F.

X. Li, J. Liu, Z. Liu, X. He, C. Zhang, H. Yuan, F. Liu, and C. Zheng, “Automatic detection of leukocytes for cytometry with color decomposition,” Optik 127, 11901–11910 (2016).
[Crossref]

Liu, H.

W. Wang, M. Si, F. Chen, H. Liu, and X. Jiang, “Study on recognition method of label-free red and white cell using fecal microscopic image,” in Proceedings of the 2018 6th International Conference on Bioinformatics and Computational Biology (ACM, 2018), pp. 95–100.

Liu, J.

J. Zhang, Y. Zhong, X. Wang, G. Ni, X. Du, J. Liu, L. Liu, and Y. Liu, “Computerized detection of leukocytes in microscopic leukorrhea images,” Med. Phys. 44, 4620–4629 (2017).
[Crossref]

X. Li, J. Liu, Z. Liu, X. He, C. Zhang, H. Yuan, F. Liu, and C. Zheng, “Automatic detection of leukocytes for cytometry with color decomposition,” Optik 127, 11901–11910 (2016).
[Crossref]

Liu, L.

J. Zhang, Y. Zhong, X. Wang, G. Ni, X. Du, J. Liu, L. Liu, and Y. Liu, “Computerized detection of leukocytes in microscopic leukorrhea images,” Med. Phys. 44, 4620–4629 (2017).
[Crossref]

Liu, Y.

J. Zhang, Y. Zhong, X. Wang, G. Ni, X. Du, J. Liu, L. Liu, and Y. Liu, “Computerized detection of leukocytes in microscopic leukorrhea images,” Med. Phys. 44, 4620–4629 (2017).
[Crossref]

Liu, Z.

X. Li, J. Liu, Z. Liu, X. He, C. Zhang, H. Yuan, F. Liu, and C. Zheng, “Automatic detection of leukocytes for cytometry with color decomposition,” Optik 127, 11901–11910 (2016).
[Crossref]

Mäenpää, T.

T. Ojala, M. Pietikäinen, and T. Mäenpää, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002).
[Crossref]

Ni, G.

J. Zhang, Y. Zhong, X. Wang, G. Ni, X. Du, J. Liu, L. Liu, and Y. Liu, “Computerized detection of leukocytes in microscopic leukorrhea images,” Med. Phys. 44, 4620–4629 (2017).
[Crossref]

Ojala, T.

T. Ojala, M. Pietikäinen, and T. Mäenpää, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002).
[Crossref]

Osher, S.

L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” in Eleventh International Conference of the Center for Nonlinear Studies on Experimental Mathematics: Computational Issues in Nonlinear Science: Computational Issues in Nonlinear Science (Elsevier North-Holland, Inc., 1992), pp. 259–268.

Pan, X.

X. Gao, H. Xue, X. Pan, J. Xinhua, Z. Yangqing, and L. Xiaoling, “Somatic cells recognition by application of Gabor Features-Based (2D)2 PCA,” Int. J. Pattern Recognit. Artif. Intell. 31, 1757009 (2017).
[Crossref]

Papageorgiou, O. M.

O. M. Papageorgiou and T. Poggio, “A general framework for object detection,” in International Conference on Computer Vision (IEEE, 2002), pp. 555–562.

Phan, T. D.

C. T. Vu, T. D. Phan, and D. M. Chandler, “S3: a spectral and spatial measure of local perceived sharpness in natural images,” IEEE Trans. Image Process 21, 934–945 (2012).
[Crossref]

Pietikäinen, M.

T. Ojala, M. Pietikäinen, and T. Mäenpää, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002).
[Crossref]

Poggio, T.

O. M. Papageorgiou and T. Poggio, “A general framework for object detection,” in International Conference on Computer Vision (IEEE, 2002), pp. 555–562.

Purwanti, E.

E. Purwanti and E. Calista, “Detection of acute lymphocyte leukemia using k-nearest neighbor algorithm based on shape and histogram features,” J. Phys. Conf. Ser. 853, 012011 (2017).
[Crossref]

Rudin, L. I.

L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” in Eleventh International Conference of the Center for Nonlinear Studies on Experimental Mathematics: Computational Issues in Nonlinear Science: Computational Issues in Nonlinear Science (Elsevier North-Holland, Inc., 1992), pp. 259–268.

Sarrafzade, O.

O. Sarrafzade, A. M. Dehnavi, H. Y. Banaem, A. Talebi, and A. Gharibi, “The best texture features for leukocytes recognition,” J. Med. Signals Sens. 7, 220–227 (2017).

Shanmugam, K.

R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. SMC-3, 610–621 (1973).
[Crossref]

Si, M.

W. Wang, M. Si, F. Chen, H. Liu, and X. Jiang, “Study on recognition method of label-free red and white cell using fecal microscopic image,” in Proceedings of the 2018 6th International Conference on Bioinformatics and Computational Biology (ACM, 2018), pp. 95–100.

Talebi, A.

O. Sarrafzade, A. M. Dehnavi, H. Y. Banaem, A. Talebi, and A. Gharibi, “The best texture features for leukocytes recognition,” J. Med. Signals Sens. 7, 220–227 (2017).

Tan, H.

H. Tan, H. Jiang, A. Dong, B. Yang, and L. Zhang, “C-V level set based cell image segmentation using color filter and morphology,” in International Conference on Information Science, Electronics and Electrical Engineering (IEEE, 2014, pp. 1073–1077.

Triggs, B.

N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in IEEE Computer Society Conference on Computer Vision & Pattern Recognition (IEEE Computer Society, 2005), pp. 886–893.

Viola, P.

P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2003), Vol. 1, pp. I-511–I-518.

Vu, C. T.

C. T. Vu, T. D. Phan, and D. M. Chandler, “S3: a spectral and spatial measure of local perceived sharpness in natural images,” IEEE Trans. Image Process 21, 934–945 (2012).
[Crossref]

Wang, W.

W. Wang, M. Si, F. Chen, H. Liu, and X. Jiang, “Study on recognition method of label-free red and white cell using fecal microscopic image,” in Proceedings of the 2018 6th International Conference on Bioinformatics and Computational Biology (ACM, 2018), pp. 95–100.

Wang, X.

J. Zhang, Y. Zhong, X. Wang, G. Ni, X. Du, J. Liu, L. Liu, and Y. Liu, “Computerized detection of leukocytes in microscopic leukorrhea images,” Med. Phys. 44, 4620–4629 (2017).
[Crossref]

Xiangang, J.

J. Xiangang, H. E. Xiaoling, and F. A. N. Zizhu, “Methods of the segmentation and recognition for red and white cells in stool microscopy images,” in Computer Engineering and Applications (2017).

Xiaoling, H. E.

J. Xiangang, H. E. Xiaoling, and F. A. N. Zizhu, “Methods of the segmentation and recognition for red and white cells in stool microscopy images,” in Computer Engineering and Applications (2017).

Xiaoling, L.

X. Gao, H. Xue, X. Pan, J. Xinhua, Z. Yangqing, and L. Xiaoling, “Somatic cells recognition by application of Gabor Features-Based (2D)2 PCA,” Int. J. Pattern Recognit. Artif. Intell. 31, 1757009 (2017).
[Crossref]

Xinhua, J.

X. Gao, H. Xue, X. Pan, J. Xinhua, Z. Yangqing, and L. Xiaoling, “Somatic cells recognition by application of Gabor Features-Based (2D)2 PCA,” Int. J. Pattern Recognit. Artif. Intell. 31, 1757009 (2017).
[Crossref]

Xue, H.

X. Gao, H. Xue, X. Pan, J. Xinhua, Z. Yangqing, and L. Xiaoling, “Somatic cells recognition by application of Gabor Features-Based (2D)2 PCA,” Int. J. Pattern Recognit. Artif. Intell. 31, 1757009 (2017).
[Crossref]

Yang, B.

H. Tan, H. Jiang, A. Dong, B. Yang, and L. Zhang, “C-V level set based cell image segmentation using color filter and morphology,” in International Conference on Information Science, Electronics and Electrical Engineering (IEEE, 2014, pp. 1073–1077.

Yangqing, Z.

X. Gao, H. Xue, X. Pan, J. Xinhua, Z. Yangqing, and L. Xiaoling, “Somatic cells recognition by application of Gabor Features-Based (2D)2 PCA,” Int. J. Pattern Recognit. Artif. Intell. 31, 1757009 (2017).
[Crossref]

Yoo, B. W.

J. W. Choi, Y. Ku, B. W. Yoo, J. A. Kim, D. S. Lee, Y. J. Chai, H. J. Kong, and H. C. Kim, “White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks,” Plos One 12, e0189259 (2017).
[Crossref]

Yuan, H.

X. Li, J. Liu, Z. Liu, X. He, C. Zhang, H. Yuan, F. Liu, and C. Zheng, “Automatic detection of leukocytes for cytometry with color decomposition,” Optik 127, 11901–11910 (2016).
[Crossref]

Zhang, C.

X. Li, J. Liu, Z. Liu, X. He, C. Zhang, H. Yuan, F. Liu, and C. Zheng, “Automatic detection of leukocytes for cytometry with color decomposition,” Optik 127, 11901–11910 (2016).
[Crossref]

Zhang, J.

J. Zhang, Y. Zhong, X. Wang, G. Ni, X. Du, J. Liu, L. Liu, and Y. Liu, “Computerized detection of leukocytes in microscopic leukorrhea images,” Med. Phys. 44, 4620–4629 (2017).
[Crossref]

Zhang, L.

H. Tan, H. Jiang, A. Dong, B. Yang, and L. Zhang, “C-V level set based cell image segmentation using color filter and morphology,” in International Conference on Information Science, Electronics and Electrical Engineering (IEEE, 2014, pp. 1073–1077.

Zhang, M.

J. Zhao, M. Zhang, Z. Zhou, J. Chu, and F. Cao, “Automatic detection and classification of leukocytes using convolutional neural networks,” Med. Biol. Eng. Comput. 55, 1287–1301 (2017).
[Crossref]

Zhao, J.

J. Zhao, M. Zhang, Z. Zhou, J. Chu, and F. Cao, “Automatic detection and classification of leukocytes using convolutional neural networks,” Med. Biol. Eng. Comput. 55, 1287–1301 (2017).
[Crossref]

Zheng, C.

X. Li, J. Liu, Z. Liu, X. He, C. Zhang, H. Yuan, F. Liu, and C. Zheng, “Automatic detection of leukocytes for cytometry with color decomposition,” Optik 127, 11901–11910 (2016).
[Crossref]

Zhong, Y.

J. Zhang, Y. Zhong, X. Wang, G. Ni, X. Du, J. Liu, L. Liu, and Y. Liu, “Computerized detection of leukocytes in microscopic leukorrhea images,” Med. Phys. 44, 4620–4629 (2017).
[Crossref]

Zhou, Z.

J. Zhao, M. Zhang, Z. Zhou, J. Chu, and F. Cao, “Automatic detection and classification of leukocytes using convolutional neural networks,” Med. Biol. Eng. Comput. 55, 1287–1301 (2017).
[Crossref]

Zizhu, F. A. N.

J. Xiangang, H. E. Xiaoling, and F. A. N. Zizhu, “Methods of the segmentation and recognition for red and white cells in stool microscopy images,” in Computer Engineering and Applications (2017).

IEEE Trans. Image Process (1)

C. T. Vu, T. D. Phan, and D. M. Chandler, “S3: a spectral and spatial measure of local perceived sharpness in natural images,” IEEE Trans. Image Process 21, 934–945 (2012).
[Crossref]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

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Figures (13)

Fig. 1.
Fig. 1. Example of leukocytes in a fecal sample. (a) Normal leukocytes. (b) Degenerating leukocytes.
Fig. 2.
Fig. 2. Leukocyte adheres to a large impurity. (a) Original image. (b) Without considering the proportion. (c) Considering the proportion.
Fig. 3.
Fig. 3. Leukocyte adhesion to impurity. (a) Original image. (b) Binary image. (c) Reserved region after inscribed circle extracted. (d) The circumscribed circle of reserved regions: the first ratio is 0.957; the second ratio is 0.924.
Fig. 4.
Fig. 4. Impurity after processing, and the ratio is 0.881. (a)–(d) Same as in Fig. 2.
Fig. 5.
Fig. 5. Segmentation algorithm flow chart.
Fig. 6.
Fig. 6. Segmentation using C-V model method. (a) and (c) Original leukocyte images. (b) and (d) Segmentation result images.
Fig. 7.
Fig. 7. Segmentation using four-direction Sobel method. (a) Original image. (b) Result image.
Fig. 8.
Fig. 8. Five Haar feature template images. (a) Horizontal line. (b) Vertical line. (c) Horizontal edge. (d) Vertical edge. (e) Diagonal.
Fig. 9.
Fig. 9. Uniform-LBP texture image. (a) B channel. (b) G channel. (c) R channel. (d) B channel texture image. (e) G channel texture image. (f) R channel texture image.
Fig. 10.
Fig. 10. Leukocyte G-channel Gabor texture image in four directions. (a) G channel. (b) 0° direction. (c) 90° direction. (d) 45° direction. (e) 135° direction.
Fig. 11.
Fig. 11. Leukocyte and impurity texture image downsampling. (a) Leukocyte texture image. (b) Downsampling leukocyte texture image. (c) Impurity texture image. (d) Downsampling impurity texture image.
Fig. 12.
Fig. 12. HOG feature visualization of leukocyte and impurity. (a) Leukocyte. (b) HOG feature visualization of leukocyte. (c) Impurity. (d) HOG feature visualization of impurity.
Fig. 13.
Fig. 13. Some impurities similar to leukocytes.

Tables (6)

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Table 1. Classification Results Using SVM

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Table 2. Classification Results Using AdaBoost

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Table 3. Classification Results Using Random Forest

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Table 4. Classification Results Using ANN

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Table 5. Classification Results of [8,9]

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Table 6. Segmentation Results of Three Methods

Equations (12)

Equations on this page are rendered with MathJax. Learn more.

Di,j={(ix)2+(jy)2Foreground0Background.
larger5,(number ofDi,j>Di+x,j+y),
equal3,(number ofDi,j=Di+x,j+y),
larger+equal=8,
x,y(1,0,1),|x|+|y|0.
|(icx)2+(jcy)2Dx,y|1,
0<Di,j3,(i,j)CP,
p=number(CP)number(BP).
Inewi,j={1(icm)2+(jcn)2RandIi,j=10else.
g(x,y;λ,θ,ψ,σ,γ)=exp(x2+γ2y22σ2)cos(2πxλ+ψ),
x=xcosθ+ysinθ,
y=xsinθ+ycosθ,