Color Transfer Method for Efficient Enhancement of Color Images and Its Application to Peripheral Blood Smear Analysis

In this paper, we propose an efficient color transfer approach as a pre-processing step, and demonstrate use of the method for enhancement of peripheral blood smear images. Peripheral blood smear analysis is subjective, laborious and is error prone. Automation is highly desirable to obtain objective and accurate results. It also would help reduce the burden of pathologists. However, automation of peripheral blood smear analysis is challenging due to the fact that the microscopic images suffer from illumination variations. Also, variations in the process of manual staining leads to variations in colors expressed by the various components of the peripheral blood smear namely the red blood cells, white blood cells and platelets. Many research groups have reported various approaches to color transfer to enhance the quality of peripheral blood smear images to facilitate automation. Color transfer approach uses a template image with the desirable qualities and transfers the color characteristics of this image to the images under study. We propose an efficient color transfer approach and demonstrate its use for enhancement of peripheral blood smear images.

[1]  Nassir Navab,et al.  Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images , 2016, IEEE Transactions on Medical Imaging.

[2]  Der-Chen Huang,et al.  A computer assisted method for leukocyte nucleus segmentation and recognition in blood smear images , 2012, J. Syst. Softw..

[3]  Peter Meer,et al.  Unsupervised segmentation based on robust estimation and color active contour models , 2005, IEEE Transactions on Information Technology in Biomedicine.

[4]  Hamid Soltanian-Zadeh,et al.  Automatic Recognition of Five Types of White Blood Cells in Peripheral Blood , 2010, ICIAR.

[5]  Carlos A. Moreira Neto,et al.  OCT: Artifacts and Errors , 2018 .

[6]  Mazin Z. Othman,et al.  Segmentation and Feature Extraction of Lymphocytes WBC using Microscopic Images , 2014 .

[7]  A. Ruifrok,et al.  Quantification of histochemical staining by color deconvolution. , 2001, Analytical and quantitative cytology and histology.

[8]  Manohar Kuse,et al.  Scalable system for classification of white blood cells from Leishman stained blood stain images , 2013, Journal of pathology informatics.

[9]  Klaus Kayser,et al.  How to measure image quality in tissue-based diagnosis (diagnostic surgical pathology) , 2008, Diagnostic pathology.

[10]  Nasrul Humaimi Mahmood,et al.  Segmentation of White Blood Cell Nucleus Using Active Contour , 2015 .

[11]  Xiaomei Li,et al.  White Blood Cell Segmentation by Color-Space-Based K-Means Clustering , 2014, Sensors.

[12]  Saeed Kermani,et al.  Recognition of Acute Lymphoblastic Leukemia Cells in Microscopic Images Using K-Means Clustering and Support Vector Machine Classifier , 2015, Journal of medical signals and sensors.

[13]  Nasir M. Rajpoot,et al.  A Nonlinear Mapping Approach to Stain Normalization in Digital Histopathology Images Using Image-Specific Color Deconvolution , 2014, IEEE Transactions on Biomedical Engineering.

[14]  Erik Reinhard,et al.  Color Transfer between Images , 2001, IEEE Computer Graphics and Applications.

[15]  J. S. Marron,et al.  A method for normalizing histology slides for quantitative analysis , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[16]  Min Wang,et al.  Subimage Cosegmentation in a Single White Blood Cell Image , 2015, 2015 7th International Conference on Computational Intelligence, Communication Systems and Networks.

[17]  Kosei Tamiya,et al.  Color Standardization Method and System for Whole Slide Imaging Based on Spectral Sensing , 2011, Analytical cellular pathology.

[18]  A. Ruifrok,et al.  Comparison of Quantification of Histochemical Staining By Hue-Saturation-Intensity (HSI) Transformation and Color-Deconvolution , 2003, Applied immunohistochemistry & molecular morphology : AIMM.

[19]  Zahoor Jan,et al.  Color Based Segmentation of White Blood Cells in Blood Photomicrographs Using Image Quantization , 2014 .