Page 50 - 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
P. 50
erally, global mapping techniques convert a color image 2.7. Parametric ratio-based method for
() into a grayscale image () by a linear weighting of the ,
, and channels, i.e. G(i, j) = ∑c=R,G,B cc (i, j), where efficient contrast preserving
∑c=R,G,B = 1. Here, the three linear weighting parameters w,
decolorization (PrDecolor)
should be estimated on the basis of some models.
In the MATLAB (Matrix Laboratory) software, developed by This PrDecolor was proposed by Xiong et. al. in 2018 [12]. The
MathWorks [11], an RGB image converts into gray-scale by the method is a contrast preserving multivariate parametrical
following weighting formula: constraint based decolorization model.
= 0.2989 × + 0.5870 × + 0.1140 × (1) 3. Experimental Results
2.2. Color2Gray The experiment was performed over 40 color hematopathology
and histopathology images with MatlabR2016a and a Windows-
This decolorization model was developed by Gooch et. al. in 10 OS, x64-based PC, RIZEN CPU, 3.6 GHz with 8 GB RAM.
2005 [7]. The proposed model uses CIELAB color space, and The proposed methods were tested on images taken from the ALL
maintains color contrast between pixel pairs by optimizing an IDB dataset [15] and UCSB Bio-Segmentation Benchmark
objective contrast function. dataset [16, 17].
2.3. Real-Time Contrast Preserving
Decolorization (RTCPD)
It has previously been said that G(i, j) = ∑c=R,G,B cc (i, j). In
2009, Lu et. al. [8] also developed a decolorization model called
Real-Time Contrast Preserving Decolorization (RTCPD) by
optimizing the linear weights c by minimizing the gradient error
energy function.
2.4. Gradient Correlation Similarity for (a) (b)
Decolorization (GcsDecolor)
Fig.1. (a) Original image of Acute Lymphoblastic Leukemia
The GcsDecolor [9] model was proposed by Liu et. al. in 2015, (b) Original image of Breast histopathology
which is the variant of RTCPD. Gradient correlation similarity
(Gcs) measure were utilized in GcsDecolor. Two variants of The decolorization efficacy of the proposed models has been
GcsDecolor have been developed by the authors. The first one is judged by computing three quality parameters, namely the Color-
iterative GcsDecolor and the other is discrete searching to-Gray Structural Similarity (C2G-SSIM) index (C2G-SSIM)
GcsDecolor. Discrete searching based GcsDecolor is utilized here, [10, 20], Edge based Contrast Measure (EBCM) [18], and Entropy
due to its simplicity and run time efficiency. [19]. C2G-SSIM [10, 20] is a color to gray evaluation metric
based on the popular image quality assessment metric SSIM. It
2.5. Semi-Parametric Decolorization demonstrates higher correlation with human subjective
(SPDecolor) model evaluations. It is expected that the efficient color to gray
conversion technique preserves the edge information. Therefore,
This Semi-Parametric Decolorization technique is another variant EBCM has been utilized to measure the edge information, as it is
of RTCPD proposed by Liu et. al. in 2016 [4]. SPDecolor has the less sensitive to digitization effects and noise. Entropy [19] value
strength of the parametric contrast preserving method and the reveals the information content in the image. If the distribution of
non-parametric rgb2gray method. the intensities is uniform, then it can be said that a histogram is
equalized and the entropy of the image is more.
2.6. Color to Gray Conversion by Correlation
(CorrC2G)
The CorrC2G [10] technique was proposed by Nafchi et. al. in
2017, where the linear weighting parameters (w) have been
estimated using the correlation information between each band of
RGB image and a contrast image. This method also does not
require any edge information or any optimization.
(a) (b)
StuCoSReC Proceedings of the 2019 6th Student Computer Science Research Conference 50
Koper, Slovenia, 10 October
() into a grayscale image () by a linear weighting of the ,
, and channels, i.e. G(i, j) = ∑c=R,G,B cc (i, j), where efficient contrast preserving
∑c=R,G,B = 1. Here, the three linear weighting parameters w,
decolorization (PrDecolor)
should be estimated on the basis of some models.
In the MATLAB (Matrix Laboratory) software, developed by This PrDecolor was proposed by Xiong et. al. in 2018 [12]. The
MathWorks [11], an RGB image converts into gray-scale by the method is a contrast preserving multivariate parametrical
following weighting formula: constraint based decolorization model.
= 0.2989 × + 0.5870 × + 0.1140 × (1) 3. Experimental Results
2.2. Color2Gray The experiment was performed over 40 color hematopathology
and histopathology images with MatlabR2016a and a Windows-
This decolorization model was developed by Gooch et. al. in 10 OS, x64-based PC, RIZEN CPU, 3.6 GHz with 8 GB RAM.
2005 [7]. The proposed model uses CIELAB color space, and The proposed methods were tested on images taken from the ALL
maintains color contrast between pixel pairs by optimizing an IDB dataset [15] and UCSB Bio-Segmentation Benchmark
objective contrast function. dataset [16, 17].
2.3. Real-Time Contrast Preserving
Decolorization (RTCPD)
It has previously been said that G(i, j) = ∑c=R,G,B cc (i, j). In
2009, Lu et. al. [8] also developed a decolorization model called
Real-Time Contrast Preserving Decolorization (RTCPD) by
optimizing the linear weights c by minimizing the gradient error
energy function.
2.4. Gradient Correlation Similarity for (a) (b)
Decolorization (GcsDecolor)
Fig.1. (a) Original image of Acute Lymphoblastic Leukemia
The GcsDecolor [9] model was proposed by Liu et. al. in 2015, (b) Original image of Breast histopathology
which is the variant of RTCPD. Gradient correlation similarity
(Gcs) measure were utilized in GcsDecolor. Two variants of The decolorization efficacy of the proposed models has been
GcsDecolor have been developed by the authors. The first one is judged by computing three quality parameters, namely the Color-
iterative GcsDecolor and the other is discrete searching to-Gray Structural Similarity (C2G-SSIM) index (C2G-SSIM)
GcsDecolor. Discrete searching based GcsDecolor is utilized here, [10, 20], Edge based Contrast Measure (EBCM) [18], and Entropy
due to its simplicity and run time efficiency. [19]. C2G-SSIM [10, 20] is a color to gray evaluation metric
based on the popular image quality assessment metric SSIM. It
2.5. Semi-Parametric Decolorization demonstrates higher correlation with human subjective
(SPDecolor) model evaluations. It is expected that the efficient color to gray
conversion technique preserves the edge information. Therefore,
This Semi-Parametric Decolorization technique is another variant EBCM has been utilized to measure the edge information, as it is
of RTCPD proposed by Liu et. al. in 2016 [4]. SPDecolor has the less sensitive to digitization effects and noise. Entropy [19] value
strength of the parametric contrast preserving method and the reveals the information content in the image. If the distribution of
non-parametric rgb2gray method. the intensities is uniform, then it can be said that a histogram is
equalized and the entropy of the image is more.
2.6. Color to Gray Conversion by Correlation
(CorrC2G)
The CorrC2G [10] technique was proposed by Nafchi et. al. in
2017, where the linear weighting parameters (w) have been
estimated using the correlation information between each band of
RGB image and a contrast image. This method also does not
require any edge information or any optimization.
(a) (b)
StuCoSReC Proceedings of the 2019 6th Student Computer Science Research Conference 50
Koper, Slovenia, 10 October
