Page 51 - 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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(d) (e) (f)
Fig.3. Results of decolorizarion
for Fig.1(b): (a) rgb2gray [11]
(b) Color2Gray [7] (c) RTCPD
[8] (d) GcsDecolor [9] (e)
SPDecolor [4] (f) CorrC2G [10]
(g) PrDecolor [12].
(e) (f) (g)
Fig.2. Results of decolorizarion
for Fig.1(a): (a) rgb2gray [11] (b) Table 1. Average Quality parameters over 40 images
Color2Gray [7] (c) RTCPD [8]
(d) GcsDecolor [9] (e) SPDecolor Method C2G-SSIM EBCM Entropy
[4] (f) CorrC2G [10] (g)
PrDecolor [12]. rgb2gray [11] 0.8912 183.24 7.19
Color2Gray [7] 0.8314 172.13 6.98
RTCPD [8] 0.8914 183.57 7.19
GcsDecolor [9] 0.8598 174.90 7.11
SPDecolor [4] 0.9030 187.38 7.23
(g) CorrC2G [10] 0.9032 187.98 7.25
PrDecolor [12] 0.9035 188.74 7.25
/* Best results obtained are given in bold*/
Table 2. Computational time of decolorization methods
Method Fig.1(a) Fig.1(b)
257x257 896x768
rgb2gray [11] 0.0086 0.0221
Color2Gray [7] 157.01 263.23
RTCPD [8] 0.0721 0.0636
(a) (b) GcsDecolor [9] 0.0397 0.0723
SPDecolor [4] 0.0942 1.0967
CorrC2G [10] 0.0187 0.0385
PrDecolor [12] 2.9678 27.8316
/* Best results obtained are given in bold*/
3.1. Analysis of Experimental Results
Performance analysis of the considered seven decolorization
models was performed by using three image quality parameters
(c) (d) and computational time. Figs. 2 and 3 demonstrate the outcomes
of the seven decolorization models over pathology images,
represented as Fig. 1. Values of the quality parameters and
computational times are given in Tables 1 and 2 respectively.
Visual analysis of Figs. 2 and 3 shows clearly that SPDecolor [4],
CorrC2G [10], and PrDecolor [12] produce better outcomes
compared to other decolorization methods. However, when we
compare these methods based on quality parameters, it can be
seen that PrDecolor [12] outperforms the other methods.
StuCoSReC Proceedings of the 2019 6th Student Computer Science Research Conference 51
Koper, Slovenia, 10 October
Fig.3. Results of decolorizarion
for Fig.1(b): (a) rgb2gray [11]
(b) Color2Gray [7] (c) RTCPD
[8] (d) GcsDecolor [9] (e)
SPDecolor [4] (f) CorrC2G [10]
(g) PrDecolor [12].
(e) (f) (g)
Fig.2. Results of decolorizarion
for Fig.1(a): (a) rgb2gray [11] (b) Table 1. Average Quality parameters over 40 images
Color2Gray [7] (c) RTCPD [8]
(d) GcsDecolor [9] (e) SPDecolor Method C2G-SSIM EBCM Entropy
[4] (f) CorrC2G [10] (g)
PrDecolor [12]. rgb2gray [11] 0.8912 183.24 7.19
Color2Gray [7] 0.8314 172.13 6.98
RTCPD [8] 0.8914 183.57 7.19
GcsDecolor [9] 0.8598 174.90 7.11
SPDecolor [4] 0.9030 187.38 7.23
(g) CorrC2G [10] 0.9032 187.98 7.25
PrDecolor [12] 0.9035 188.74 7.25
/* Best results obtained are given in bold*/
Table 2. Computational time of decolorization methods
Method Fig.1(a) Fig.1(b)
257x257 896x768
rgb2gray [11] 0.0086 0.0221
Color2Gray [7] 157.01 263.23
RTCPD [8] 0.0721 0.0636
(a) (b) GcsDecolor [9] 0.0397 0.0723
SPDecolor [4] 0.0942 1.0967
CorrC2G [10] 0.0187 0.0385
PrDecolor [12] 2.9678 27.8316
/* Best results obtained are given in bold*/
3.1. Analysis of Experimental Results
Performance analysis of the considered seven decolorization
models was performed by using three image quality parameters
(c) (d) and computational time. Figs. 2 and 3 demonstrate the outcomes
of the seven decolorization models over pathology images,
represented as Fig. 1. Values of the quality parameters and
computational times are given in Tables 1 and 2 respectively.
Visual analysis of Figs. 2 and 3 shows clearly that SPDecolor [4],
CorrC2G [10], and PrDecolor [12] produce better outcomes
compared to other decolorization methods. However, when we
compare these methods based on quality parameters, it can be
seen that PrDecolor [12] outperforms the other methods.
StuCoSReC Proceedings of the 2019 6th Student Computer Science Research Conference 51
Koper, Slovenia, 10 October
