Page 52 - Fister jr., Iztok, Andrej Brodnik, Matjaž Krnc and Iztok Fister (eds.). StuCoSReC. Proceedings of the 2019 6th Student Computer Science Research Conference. Koper: University of Primorska Press, 2019
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ecolor [4] and CorrC2G [10] provide nearly the same results Transactions on Graphics (TOG) (Vol. 24, No. 3, pp. 634-639).
as PrDecolor [12], as their corresponding numerical values of the ACM.
quality parameters are almost the same. It can also be seen that 8) Lu, C., Xu, L., & Jia, J. (2012, November). Real-time contrast
these three methods, namely, SPDecolor [4], CorrC2G [10], and preserving decolorization. In SIGGRAPH Asia 2012 Technical
PrDecolor [12] outperform the other four methods significantly in Briefs (p. 34). ACM.
terms of quality parameters. When we consider computational 9) Liu, Q., Liu, P. X., Xie, W., Wang, Y., & Liang, D. (2015).
time, it can be seen that the MATLAB based rgb2gray [11] GcsDecolor: gradient correlation similarity for efficient contrast
method is the best method. However, among the SPDecolor [4], preserving decolorization. IEEE Transactions on Image
CorrC2G [10], and PrDecolor [12] methods, CorrC2G is Processing, 24(9), 2889-2904.
associated with the lowest computational time. 10) Nafchi, H. Z., Shahkolaei, A., Hedjam, R., & Cheriet, M.
(2017). CorrC2G: Color to gray conversion by
4. Conclusion correlation. IEEE Signal Processing Letters, 24(11), 1651-1655.
11) MATLAB and Image Processing Toolbox Release 2012b, The
This paper presents a comparative study among seven existing MathWorks, Inc., Natick, Massachusetts, United States.
decolorization methods in the case of digital pathology images. 12) Xiong, J., Lu, H., Liu, Q., & Xu, X. (2018). Parametric ratio-
The visual and decolorization quality parameters prove clearly based method for efficient contrast-preserving
that PrDecolor [12], proposed by Xiong et. al., provided the best decolorization. Multimedia Tools and Applications, 77(12),
outcomes compared to the other six methods. Computational time 15721-15745.
shows that the MATLAB based rgb2gray method outperformed 13) Du, H., He, S., Sheng, B., Ma, L., & Lau, R. W. (2014).
the others, although CorrC2G [10] produced nearly the same Saliency-guided color-to-gray conversion using region-based
outputs as the PrDecolor [12] method, but within the second less optimization. IEEE Transactions on Image Processing, 24(1),
computational time. One challenging future direction of this study 434-443.
can be the application of nature-inspired optimization algorithms 14) Jin, Z., Li, F., & Ng, M. K. (2014). A variational approach for
to set the parameters of the parametric decolorization methods by image decolorization by variance maximization. SIAM Journal
considering different objective functions. on Imaging Sciences, 7(2), 944-968.
15) Labati, R. D., Piuri, V., & Scotti, F. (2011, September). All-
REFERENCES IDB: The acute lymphoblastic leukemia image database for
image processing. In 2011 18th IEEE International Conference
1) Gurcan, M. N., Boucheron, L. E., Can, A., Madabhushi, A., on Image Processing (pp. 2045-2048). IEEE.
Rajpoot, N. M., & Yener, B. (2009). Histopathological image 16) Dhal, K. G., Ray, S., Das, S., Biswas, A., & Ghosh, S. Hue-
analysis: A review. IEEE reviews in biomedical engineering, 2, Preserving and Gamut Problem-Free Histopathology Image
147-171. Enhancement. Iranian Journal of Science and Technology,
Transactions of Electrical Engineering, 1-28.
2) Irshad, H., Veillard, A., Roux, L., & Racoceanu, D. (2014). 17) Dhal, K. G., Fister Jr, I., Das, A., Ray, S., & Das, S (2018)
Methods for nuclei detection, segmentation, and classification Breast Histopathology Image Clustering using Cuckoo Search
in digital histopathology: a review—current status and future Algorithm. StuCoSReC. 5th Student Computer Science
potential. IEEE reviews in biomedical engineering, 7, 97-114. Research Conference, 47-54.
18) Beghdadi, A., & Le Negrate, A. (1989). Contrast enhancement
3) Hinojosa, S., Dhal, K. G., Elaziz, M. A., Oliva, D., & Cuevas, technique based on local detection of edges. Computer Vision,
E. (2018). Entropy-based imagery segmentation for breast Graphics, and Image Processing, 46(2), 162-174.
histology using the Stochastic Fractal 19) Dhal, K. G., Ray, S., Das, A., & Das, S. (2018). A Survey on
Search. Neurocomputing, 321, 201-215. Nature-Inspired Optimization Algorithms and Their
Application in Image Enhancement Domain. Archives of
4) Liu, Q., Liu, P. X., Wang, Y., & Leung, H. (2016). Computational Methods in Engineering, 1-32.
Semiparametric decolorization with Laplacian-based perceptual 20) Ma, K., Zhao, T., Zeng, K., & Wang, Z. (2015). Objective
quality metric. IEEE Transactions on Circuits and Systems for quality assessment for color-to-gray image conversion. IEEE
Video Technology, 27(9), 1856-1868. Transactions on Image Processing, 24(12), 4673-4685.
5) Neumann, L., Čadík, M., & Nemcsics, A. (2007, June). An
efficient perception-based adaptive color to gray
transformation. In Proceedings of the Third Eurographics
conference on Computational Aesthetics in Graphics,
Visualization and Imaging (pp. 73-80). Eurographics
Association.
6) Smith, K., Landes, P. E., Thollot, J., & Myszkowski, K. (2008,
April). Apparent greyscale: A simple and fast conversion to
perceptually accurate images and video. In Computer Graphics
Forum (Vol. 27, No. 2, pp. 193-200). Oxford, UK: Blackwell
Publishing Ltd.
7) Gooch, A. A., Olsen, S. C., Tumblin, J., & Gooch, B. (2005,
July). Color2gray: salience-preserving color removal. In ACM
StuCoSReC Proceedings of the 2019 6th Student Computer Science Research Conference 52
Koper, Slovenia, 10 October
as PrDecolor [12], as their corresponding numerical values of the ACM.
quality parameters are almost the same. It can also be seen that 8) Lu, C., Xu, L., & Jia, J. (2012, November). Real-time contrast
these three methods, namely, SPDecolor [4], CorrC2G [10], and preserving decolorization. In SIGGRAPH Asia 2012 Technical
PrDecolor [12] outperform the other four methods significantly in Briefs (p. 34). ACM.
terms of quality parameters. When we consider computational 9) Liu, Q., Liu, P. X., Xie, W., Wang, Y., & Liang, D. (2015).
time, it can be seen that the MATLAB based rgb2gray [11] GcsDecolor: gradient correlation similarity for efficient contrast
method is the best method. However, among the SPDecolor [4], preserving decolorization. IEEE Transactions on Image
CorrC2G [10], and PrDecolor [12] methods, CorrC2G is Processing, 24(9), 2889-2904.
associated with the lowest computational time. 10) Nafchi, H. Z., Shahkolaei, A., Hedjam, R., & Cheriet, M.
(2017). CorrC2G: Color to gray conversion by
4. Conclusion correlation. IEEE Signal Processing Letters, 24(11), 1651-1655.
11) MATLAB and Image Processing Toolbox Release 2012b, The
This paper presents a comparative study among seven existing MathWorks, Inc., Natick, Massachusetts, United States.
decolorization methods in the case of digital pathology images. 12) Xiong, J., Lu, H., Liu, Q., & Xu, X. (2018). Parametric ratio-
The visual and decolorization quality parameters prove clearly based method for efficient contrast-preserving
that PrDecolor [12], proposed by Xiong et. al., provided the best decolorization. Multimedia Tools and Applications, 77(12),
outcomes compared to the other six methods. Computational time 15721-15745.
shows that the MATLAB based rgb2gray method outperformed 13) Du, H., He, S., Sheng, B., Ma, L., & Lau, R. W. (2014).
the others, although CorrC2G [10] produced nearly the same Saliency-guided color-to-gray conversion using region-based
outputs as the PrDecolor [12] method, but within the second less optimization. IEEE Transactions on Image Processing, 24(1),
computational time. One challenging future direction of this study 434-443.
can be the application of nature-inspired optimization algorithms 14) Jin, Z., Li, F., & Ng, M. K. (2014). A variational approach for
to set the parameters of the parametric decolorization methods by image decolorization by variance maximization. SIAM Journal
considering different objective functions. on Imaging Sciences, 7(2), 944-968.
15) Labati, R. D., Piuri, V., & Scotti, F. (2011, September). All-
REFERENCES IDB: The acute lymphoblastic leukemia image database for
image processing. In 2011 18th IEEE International Conference
1) Gurcan, M. N., Boucheron, L. E., Can, A., Madabhushi, A., on Image Processing (pp. 2045-2048). IEEE.
Rajpoot, N. M., & Yener, B. (2009). Histopathological image 16) Dhal, K. G., Ray, S., Das, S., Biswas, A., & Ghosh, S. Hue-
analysis: A review. IEEE reviews in biomedical engineering, 2, Preserving and Gamut Problem-Free Histopathology Image
147-171. Enhancement. Iranian Journal of Science and Technology,
Transactions of Electrical Engineering, 1-28.
2) Irshad, H., Veillard, A., Roux, L., & Racoceanu, D. (2014). 17) Dhal, K. G., Fister Jr, I., Das, A., Ray, S., & Das, S (2018)
Methods for nuclei detection, segmentation, and classification Breast Histopathology Image Clustering using Cuckoo Search
in digital histopathology: a review—current status and future Algorithm. StuCoSReC. 5th Student Computer Science
potential. IEEE reviews in biomedical engineering, 7, 97-114. Research Conference, 47-54.
18) Beghdadi, A., & Le Negrate, A. (1989). Contrast enhancement
3) Hinojosa, S., Dhal, K. G., Elaziz, M. A., Oliva, D., & Cuevas, technique based on local detection of edges. Computer Vision,
E. (2018). Entropy-based imagery segmentation for breast Graphics, and Image Processing, 46(2), 162-174.
histology using the Stochastic Fractal 19) Dhal, K. G., Ray, S., Das, A., & Das, S. (2018). A Survey on
Search. Neurocomputing, 321, 201-215. Nature-Inspired Optimization Algorithms and Their
Application in Image Enhancement Domain. Archives of
4) Liu, Q., Liu, P. X., Wang, Y., & Leung, H. (2016). Computational Methods in Engineering, 1-32.
Semiparametric decolorization with Laplacian-based perceptual 20) Ma, K., Zhao, T., Zeng, K., & Wang, Z. (2015). Objective
quality metric. IEEE Transactions on Circuits and Systems for quality assessment for color-to-gray image conversion. IEEE
Video Technology, 27(9), 1856-1868. Transactions on Image Processing, 24(12), 4673-4685.
5) Neumann, L., Čadík, M., & Nemcsics, A. (2007, June). An
efficient perception-based adaptive color to gray
transformation. In Proceedings of the Third Eurographics
conference on Computational Aesthetics in Graphics,
Visualization and Imaging (pp. 73-80). Eurographics
Association.
6) Smith, K., Landes, P. E., Thollot, J., & Myszkowski, K. (2008,
April). Apparent greyscale: A simple and fast conversion to
perceptually accurate images and video. In Computer Graphics
Forum (Vol. 27, No. 2, pp. 193-200). Oxford, UK: Blackwell
Publishing Ltd.
7) Gooch, A. A., Olsen, S. C., Tumblin, J., & Gooch, B. (2005,
July). Color2gray: salience-preserving color removal. In ACM
StuCoSReC Proceedings of the 2019 6th Student Computer Science Research Conference 52
Koper, Slovenia, 10 October
