Page 49 - 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. 49
olorization of Digital Pathology Images: A Comparative Study
Krishna Gopal Dhal1, Swarnajit Ray2, Arunita Das3, Iztok Fister Jr.4, Sanjoy Das5
1Dept. of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal, India.
Email: krishnagopal.dhal@midnaporecollege.ac.in
2Learningmate Solutions Pvt. Ltd., Kolkata, West Bengal, India. Email: swarnajit32@gmail.com
3Dept. of Information Technology, Kalyani Govt. Engineering College, Kalyani, Nadia, India. Email: arunita17@gmail.com.
4Faculty of Electrical Eng. and Computer Sc., University of Maribor, Slovenia, Email: iztok.fister1@um.si.
5Dept. of Eng. and Technological Studies, University of Kalyani, Kalyani, India, Email: dassanjoy0810@hotmail.com
ABSTRACT reduced to the order M×N with each pixel as a single scalar value,
then the computation for applying these techniques reduces
The major demerit of color to gray conversion is the loss of drastically. Another benefit is that this conversion facilitates the
visually important image features. Digital pathology images are application of single-channel algorithms on color images, like
treated as the gold Standard for detection of various diseases, Canny operator for edge detection [4]. In literature, this dimension
especially for the different types of cancer. Digital pathology reduction is considered as color to gray scale image conversion, or
images are color in nature, i.e. each pixel is a color vector decolorization.
represented by three values. Thus, the processing of these images Several color to gray scale conversion techniques have been
requires high computational time. If these color images are developed by following the human perception of brightness and
converted into one dimensional gray images, then processing time contrast, and they proved their efficiency in the traditional color
can be reduced, which will help the Computer-Aided Diagnosis image decolorization field [5-12]. However , the utilization of
(CAD) system significantly for accurate classification and decolorization techniques in the Digital Pathology domain is a
detection of different types of diseases. Therefore, this study little bit different. Information loss minimization for a specific
focuses on the fast conversion of color digital pathology images image is the main aspiration. Therefore, this study utilizes these
into gray images. In order to do that, seven well established color developed color to gray conversion techniques for the
to gray conversion, techniques have been employed for producing decolorization of pathology images to prove their efficacy in this
gray images with salient features. The outcomes have been medical image domain. All color to gray conversion techniques
validated visually and numerically. are categorized into three classes, namely Local, Global, and
Hybrid. In local processing based techniques [5, 6], the same
KEYWORDS color pixel within an image can be mapped into different gray
values, depending on the local distributions of colors, which is
Digital Pathology Images, Decolorization, Color to Gray generally not desired. Compared to local, global processing
Conversion, Gray Scale, RGB2GRAY. methods [4, 7-12] are able to produce natural looking images.
Several hybrid methods have also been developed by considering
1. Introduction global and local contrast or features for conversion [13, 14], but, it
is also true that local processing and utilization of local
Computer assisted pathology and microscopy image analysis, information based statistics take large computational time.
assist the decision making for automated disease diagnosing, as Therefore, this letter considers only global processing based
they provide digital images related to certain kinds of disease techniques [4, 7-12], which are discussed in the next Section.
using Computer-Aided Diagnosis (CAD) systems, which
facilitates quantitative and qualitative medical results with a high The paper is organized as follows: Section 2 discusses
throughput processing rate [1, 2, 3]. At present, automated all the global color to gray conversion techniques. Section 3
medical diagnosing has attracted the attention of several describes the experimental results and the paper is concluded in
pathologists in research and clinical practice , since CAD systems section 4.
reduce human error, false positive results and time complexity,
while pathology imaging provides more accurate results, faster 2. Decolorization Models
and reproducible image analysis. Digital pathology images are
stored as high-resolution color images, i.e. each pixel is The existing global decolorization, methods have been presented
represented as a three-dimensional vector, namely R, G, and B, in this Section.
and, due to that, they are of the order M×N×3, where M and N
indicate the number of row and column respectively. Therefore, 2.1. MATLAB Procedure (rgb2gray)
several image processing techniques, like enhancement,
segmentation, require high computational effort. In order to
overcome this issue, if these high dimensional images can be
StuCoSReC Proceedings of the 2019 6th Student Computer Science Research Conference DOI: https://doi.org/10.26493/978-961-7055-82-5.49-52 49
Koper, Slovenia, 10 October
Krishna Gopal Dhal1, Swarnajit Ray2, Arunita Das3, Iztok Fister Jr.4, Sanjoy Das5
1Dept. of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal, India.
Email: krishnagopal.dhal@midnaporecollege.ac.in
2Learningmate Solutions Pvt. Ltd., Kolkata, West Bengal, India. Email: swarnajit32@gmail.com
3Dept. of Information Technology, Kalyani Govt. Engineering College, Kalyani, Nadia, India. Email: arunita17@gmail.com.
4Faculty of Electrical Eng. and Computer Sc., University of Maribor, Slovenia, Email: iztok.fister1@um.si.
5Dept. of Eng. and Technological Studies, University of Kalyani, Kalyani, India, Email: dassanjoy0810@hotmail.com
ABSTRACT reduced to the order M×N with each pixel as a single scalar value,
then the computation for applying these techniques reduces
The major demerit of color to gray conversion is the loss of drastically. Another benefit is that this conversion facilitates the
visually important image features. Digital pathology images are application of single-channel algorithms on color images, like
treated as the gold Standard for detection of various diseases, Canny operator for edge detection [4]. In literature, this dimension
especially for the different types of cancer. Digital pathology reduction is considered as color to gray scale image conversion, or
images are color in nature, i.e. each pixel is a color vector decolorization.
represented by three values. Thus, the processing of these images Several color to gray scale conversion techniques have been
requires high computational time. If these color images are developed by following the human perception of brightness and
converted into one dimensional gray images, then processing time contrast, and they proved their efficiency in the traditional color
can be reduced, which will help the Computer-Aided Diagnosis image decolorization field [5-12]. However , the utilization of
(CAD) system significantly for accurate classification and decolorization techniques in the Digital Pathology domain is a
detection of different types of diseases. Therefore, this study little bit different. Information loss minimization for a specific
focuses on the fast conversion of color digital pathology images image is the main aspiration. Therefore, this study utilizes these
into gray images. In order to do that, seven well established color developed color to gray conversion techniques for the
to gray conversion, techniques have been employed for producing decolorization of pathology images to prove their efficacy in this
gray images with salient features. The outcomes have been medical image domain. All color to gray conversion techniques
validated visually and numerically. are categorized into three classes, namely Local, Global, and
Hybrid. In local processing based techniques [5, 6], the same
KEYWORDS color pixel within an image can be mapped into different gray
values, depending on the local distributions of colors, which is
Digital Pathology Images, Decolorization, Color to Gray generally not desired. Compared to local, global processing
Conversion, Gray Scale, RGB2GRAY. methods [4, 7-12] are able to produce natural looking images.
Several hybrid methods have also been developed by considering
1. Introduction global and local contrast or features for conversion [13, 14], but, it
is also true that local processing and utilization of local
Computer assisted pathology and microscopy image analysis, information based statistics take large computational time.
assist the decision making for automated disease diagnosing, as Therefore, this letter considers only global processing based
they provide digital images related to certain kinds of disease techniques [4, 7-12], which are discussed in the next Section.
using Computer-Aided Diagnosis (CAD) systems, which
facilitates quantitative and qualitative medical results with a high The paper is organized as follows: Section 2 discusses
throughput processing rate [1, 2, 3]. At present, automated all the global color to gray conversion techniques. Section 3
medical diagnosing has attracted the attention of several describes the experimental results and the paper is concluded in
pathologists in research and clinical practice , since CAD systems section 4.
reduce human error, false positive results and time complexity,
while pathology imaging provides more accurate results, faster 2. Decolorization Models
and reproducible image analysis. Digital pathology images are
stored as high-resolution color images, i.e. each pixel is The existing global decolorization, methods have been presented
represented as a three-dimensional vector, namely R, G, and B, in this Section.
and, due to that, they are of the order M×N×3, where M and N
indicate the number of row and column respectively. Therefore, 2.1. MATLAB Procedure (rgb2gray)
several image processing techniques, like enhancement,
segmentation, require high computational effort. In order to
overcome this issue, if these high dimensional images can be
StuCoSReC Proceedings of the 2019 6th Student Computer Science Research Conference DOI: https://doi.org/10.26493/978-961-7055-82-5.49-52 49
Koper, Slovenia, 10 October
