Red Blood Cell Count Automation Using Microscopic Hyperspectral Imaging Technology

Red blood cell counts have been proven to be one of the most frequently performed blood tests and are valuable for early diagnosis of some diseases. This paper describes an automated red blood cell counting method based on microscopic hyperspectral imaging technology. Unlike the light microscopy-based red blood count methods, a combined spatial and spectral algorithm is proposed to identify red blood cells by integrating active contour models and automated two-dimensional k-means with spectral angle mapper algorithm. Experimental results show that the proposed algorithm has better performance than spatial based algorithm because the new algorithm can jointly use the spatial and spectral information of blood cells.

[1]  H. Tanke,et al.  Quantification of fetomaternal hemorrhage: a comparative study of the manual and automated microscopic Kleihauer-Betke tests and flow cytometry in clinical samples. , 2004, American journal of obstetrics and gynecology.

[2]  Vidya Manian,et al.  Object segmentation in hyperspectral images using active contours and graph cuts , 2012 .

[3]  Paul Fieguth,et al.  Watershed deconvolution for cell segmentation , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Jürgen Popp,et al.  Identification and differentiation of single cells from peripheral blood by Raman spectroscopic imaging , 2010, Journal of biophotonics.

[5]  Hongying Liu,et al.  A combined spatial-spectral method for automated white blood cells segmentation , 2013 .

[6]  Ihor V Berezhnyy,et al.  Fast multi-spectral imaging technique for detection of circulating endothelial cells in human blood samples. , 2012, Journal of biomedical optics.

[7]  Yogesh Karunakar,et al.  An Unparagoned Application for Red Blood Cell Counting using Marker Controlled Watershed Algorithm for Android Mobile , 2011, 2011 Fifth International Conference on Next Generation Mobile Applications, Services and Technologies.

[8]  Yu Sun,et al.  A System for Counting Fetal and Maternal Red Blood Cells , 2014, IEEE Transactions on Biomedical Engineering.

[9]  Wei Wang,et al.  A flexible and robust approach for segmenting cell nuclei from 2D microscopy images using supervised learning and template matching , 2013, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[10]  Saeam Shin,et al.  Comparison study of the rates of manual peripheral blood smear review from 3 automated hematology analyzers, Unicel DxH 800, ADVIA 2120i, and XE 2100, using international consensus group guidelines. , 2012, Archives of pathology & laboratory medicine.

[11]  Hong-Wu Tang,et al.  Hadamard transform spectral microscopy for single cell imaging using organic and quantum dot fluorescent probes. , 2009, The Analyst.

[12]  Libo Zeng,et al.  A method based on multispectral imaging technique for White Blood Cell segmentation , 2007, Comput. Biol. Medicine.

[13]  Fred A. Kruse,et al.  The Spectral Image Processing System (SIPS): Software for integrated analysis of AVIRIS data , 1992 .

[14]  Hongying Liu,et al.  Methyl green and nitrotetrazolium blue chloride co-expression in colon tissue: A hyperspectral microscopic imaging analysis , 2014 .

[15]  Avinash C. Kak,et al.  PCA versus LDA , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Jungho Im,et al.  ISPRS Journal of Photogrammetry and Remote Sensing , 2022 .

[17]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[18]  Dongrong Xu,et al.  AOTF based molecular hyperspectral imaging system and its applications on nerve morphometry. , 2013, Applied optics.

[19]  Ansuman Banerjee,et al.  Unsupervised and supervised classification of hyperspectral imaging data using projection pursuit and Markov random field segmentation , 2012 .

[20]  Jerilyn A. Timlin,et al.  Advanced imaging of multiple mRNAs in brain tissue using a custom hyperspectral imager and multivariate curve resolution , 2006, Journal of Neuroscience Methods.

[21]  Nor Ashidi Mat Isa,et al.  Automated two-dimensional K-means clustering algorithm for unsupervised image segmentation , 2013, Comput. Electr. Eng..

[22]  Sofie Bekaert,et al.  Lower red blood cell counts in middle‐aged subjects with shorter peripheral blood leukocyte telomere length , 2008, Aging cell.

[23]  Fei Li,et al.  Red blood cell count as an indicator of microvascular complications in Chinese patients with type 2 diabetes mellitus , 2013, Vascular health and risk management.

[24]  Fabio K. Schneider,et al.  Image-based red cell counting for wild animals blood , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[25]  Alexander F. H. Goetz,et al.  Three decades of hyperspectral remote sensing of the Earth: a personal view. , 2009 .

[26]  A.M.T. Nasution,et al.  Comparison of Red Blood Cells Counting using two Algorithms: Connected Component Labeling and Backprojection of Artificial Neural Network , 2008, 2008 IEEE PhotonicsGlobal@Singapore.

[27]  Izzet Kale,et al.  A novel method to count the red blood cells in thin blood films , 2011, 2011 IEEE International Symposium of Circuits and Systems (ISCAS).

[28]  Fred A. Kruse,et al.  The Spectral Image Processing System (SIPS) - Interactive visualization and analysis of imaging spectrometer data , 1993 .

[29]  Dongrong Xu,et al.  Review of spectral imaging technology in biomedical engineering: achievements and challenges , 2013, Journal of biomedical optics.

[30]  J. Mirapeix,et al.  Data Processing Method Applying Principal Component Analysis and Spectral Angle Mapper for Imaging Spectroscopic Sensors , 2008, IEEE Sensors Journal.