Login Register Cart 0
Generic placeholder image

Current Cancer Therapy Reviews

Editor-in-Chief

ISSN (Print): 1573-3947
ISSN (Online): 1875-6301

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Development of MatLab Coding for Early Detection of Leukemia through Automated Analysis of RBCs

Author(s): Durjoy Majumder*

Volume 16, Issue 2, 2020

Page: [152 - 164] Pages: 13

DOI: 10.2174/1573394715666191204102545

Price: $65

Abstract

Background: Bone marrow biopsy has become an integral part of leukemia diagnosis and its treatment. Several advancements are being made towards the analysis of digital images of biopsy samples. Recently, the FDA approved the procedures of digital health. In tune with that, digital image analysis has become propelled. With the advent of high-throughput technologies, the scientific community focuses on the red blood cells (RBCs) for early detection of cancer, including leukemia. The reasons are due to their abundance in peripheral blood and hence, easily accessible compared to the bone marrow biopsy procedure. High magnification and high-resolution electron microscopy-based ultra-structural analysis of RBCs already proved the utility of the hypothesis about a decade ago. However, in clinical set-up, electron microscopy-based procedures are the major bottleneck in the implementation of early detection of leukemia. Algorithm-based computer vision may be suitable to overcome this limitation.

Methods: An intensive search with PubMed and Google for early diagnosis of leukemia through RBC light microscopic images was made. For this search, the image processing algorithm for RBC was also made in PubMed, IEEE Xplorer and Google; and the latest developments are noted. To fill the existing gap, a user-friendly MatLab coding is developed for automated analysis of RBC images.

Result: RBC images from both normal and leukemia were analyzed with the developed code. Each RBC cells were analyzed individually for each sample of normal and leukemia. Therefore, in the output cellular characteristics namely, radius, perimeter, area, convexity and solidity were represented in a quantitative manner. Comparison of mean values between normal and leukemia groups for corresponding variables showed statistical significance. In the test run, data of 82 RBC cells from single leukemia sample when compared with the data of pooled data of normal samples also showed significant difference (P<0.05).

Conclusion: Thus, the developed code successfully distinguishes between RBC cells of leukemia and normal. We hope that this RBC based developed code would be useful in identifying earlystage of leukemia in an individual patient; however, for this, ~100 RBC cells are needed to be analyzed. As the developed code is based on RBC based diagnosis therefore, may be applied to other cancers and if needed, further modification is possible due to availability of code. Thus, it is expected that the developed MatLab code may play a role in preventive oncology.

Keywords: Digital image, automated procedure, mathematical morphology, red blood cell, Leukemia, MatLab code, light microscopy image.

« Previous Next »
Graphical Abstract

[1]
Lehmann TM, Meinzer HP, Tolxdorff T. Advances in biomedical image analysis--past, present and future challenges. Methods Inf Med 2004; 43(4): 308-14.
[http://dx.doi.org/10.1055/s-0038-1633873] [PMID: 15472739]
[2]
Lovett L. FDA regulations could create digital health opportunities for pharma Available from: https://www.mobihealthnews.com/content/fda-regulations-could-create-digital-health-opportunities-pharma (Accessed on: March 28, 2019)
[3]
Muoio D. Stanford medicine licenses google glass-based autism tech to Cognoa, Mobihealth News Available from: https://www. mobihealthnews.com/content/stanford-medicine-licenses-google-glass-based-autism-tech-cognoa (Accessed on: April 10, 2019).
[4]
Alwan A. Global status report on noncommunicable diseases www.globalissues.org/article/588/global-health-overview
[5]
Whiteman H. 1 in 2 people will develop cancer in their lifetime MedicalNewsToday Available from: https://www.medicalnewstoday.com/articles/288916.php (Accessed on: Feb 4, 2015)
[6]
Ismail SM, Colclough AB, Dinnen JS, et al. Observer variation in histopathological diagnosis and grading of cervical intraepithelial neoplasia. BMJ 1989; 298(6675): 707-10.
[http://dx.doi.org/10.1136/bmj.298.6675.707] [PMID: 2496816]
[7]
Andrion A, Magnani C, Betta PG, et al. Malignant mesothelioma of the pleura: interobserver variability. J Clin Pathol 1995; 48(9): 856-60.
[http://dx.doi.org/10.1136/jcp.48.9.856] [PMID: 7490321]
[8]
Anderson C. Leveraging blood based diagnostics. J Prec Med 2015; 49: 1.
[9]
Mazumder R. Blood-based companion diagnostics. J Prec Med 2015; 51: 1.
[10]
Gurcan MN, Boucheron LE, Can A, Madabhushi A, Rajpoot NM, Yener B. Histopathological image analysis: a review. IEEE Rev Biomed Eng 2009; 2: 147-71.
[http://dx.doi.org/10.1109/RBME.2009.2034865] [PMID: 20671804]
[11]
Gonzalez RC, Woods RE, Eddins SL. Introduction. In:Digital Image Processing Using MATLAB. 3rd ed. Pearson: Knoxville, TN 2009; pp. 2-3.
[12]
Majumder D, Das M. Digital image analysis for early diagnosis of cancer: Identification of pre-cancerous state. In: Dey N, Ashour AS, Kalia H, Goswami RT, Das H, Eds. . Histopathological image analysis in medical decision making. IGI Global: USA 2019; pp. 69-102.
[http://dx.doi.org/10.4018/978-1-5225-6316-7.ch004]
[13]
Demirkaya O, Aysali MH, Sahoo PK. Image processing with MATLAB applications in medicine and biology. CRC Press: London, New York. 2009; p. p. 53 . ISBN 8493-9246.
[14]
Al-amri SS, Kalyankar NV, Khamitkar SD. Image segmentation by using thresholding technique. J Comput 2010; 2(5): 83-6.
[15]
Yu K-H, Zhang C, Berry GJ, et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features Nat Commu 2016; 7(12474): 1-10.
[http://dx.doi.org/10.1038/ncomms12474]
[16]
Jain A, Atey S, Vinayak S, Srivastava V. Cancerous cell detection using histopathological image analysis. Int J Innov Res Comp Commun Engg 2014; 2(12): 7419-26.
[http://dx.doi.org/10.15680/IJIRCCE.2014.0212026]
[17]
Demir C, Yener B. Automated cancer diagnosis based on histopathological images: A systematic survey [Technical Report] Rensselaer Polytechnic Institute, Department of Computer Science 2005; ; Tr-05-09: 1-16.
[18]
Ali U, Shaukat A, Hussain M, et al. Automatic cancerous tissue classification using discrete wavelet transformation and support vector machine. J Basic Appl Sci Res 2016; 6(7): 15-23.
[19]
Gurcan MN, Pan T, Shimada H, Saltz J. Image analysis for neuroblastoma classification: Segmentation of cell nuclei. Proc IEEE Int Conf Engg Med Biol Soc 2006. 2006; 4844-7.
[http://dx.doi.org/10.1109/IEMBS.2006.260837]
[20]
Waheed S, Moffitt RA, Chaudry Q, Young AN, Wang MD. Computer aided histopathological classification of cancer subtypes. Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering. 2007 Oct 14-17; Boston, MA, USA. New York: IEEE
[http://dx.doi.org/10.1109/BIBE.2007.4375608]
[21]
Sertel O, Catalyurek UV, Shimada H, Gurcan MN. Computer-aided prognosis of neuroblastoma: Detection of mitosis and karyorrhexis cells in digitized histological images. Proc IEEE Engg Med Biol Soc 2009. 2009; 1433-6.
[http://dx.doi.org/10.1109/IEMBS.2009.5332910]
[22]
Xu J, Janowczyk A, Chandran S, Madabhushi A. weighted mean shift, normalized cuts initialized color gradient based geodesic active contour model: Applications to histopathology image segmentation. Proc SPIE Digital Libr 2010; 7623(76230Y): 1-12.[http://spiedl.org/terms]
[23]
Basavanhallya A, Yu E, Xu J, et al. Incorporating domain knowledge for tubule detection in breast histopathology using O’Callaghan neighborhoods. SPIE Medical imaging 2011. 7963: 796310.
[24]
Nateghi R, Danyali H, Helfroush MS, Tashk A. Mitosis detection from breast cancer histology slide images using particle swarm optimization and support vector machine. Int J Sci Basic Appl Res 2014; 16(2): 164-77.
[25]
Kumar R, Srivastava R, Srivastava S. Detection and classification of cancer from microscopic biopsy images using clinically significant and biologically interpretable features. J Med Eng 2015; 2015: 457906
[http://dx.doi.org/10.1155/2015/457906] [PMID: 27006938]
[26]
Spanhol FA, Oliveira LS, Petitjean C, Heutte L. Breast cancer histopathological image classification using convolutional neural networks. Proceedings of the International Joint Conference on Neural Networks (IJCNN). 2016 July 24-29; Vancouver, BC, Canada. New York: IEEE
[http://dx.doi.org//10.1109/IJCNN.2016.7727519]
[27]
Wang P, Hu X, Li Y, Liu Q, Zhu X. Automatic cell nuclei segmentation and classification of breast cancer histopathology images. Signal Process 2016; 122: 1-13.
[http://dx.doi.org/10.1016/j.sigpro.2015.11.011]
[28]
Araújo T, Aresta G, Castro E, et al. Classification of breast cancer histology images using Convolutional Neural Networks. PLoS One 2017; 12(6): e0177544
[http://dx.doi.org/10.1371/journal.pone.0177544] [PMID: 28570557]
[29]
Zhou L, Weng G. Red blood cell extraction based on mathematical morphology. Adv Mat Res 2013; 659: 149-52.
[http://dx.doi.org/10.4028/www.scientific.net/AMR.659.149]
[30]
Taherisadr M, Nasirzonouzi M, Baradaram B, Mehdizade A. New Approach to red blood cell classification using morphological image processing. Shiraz E Med J 2013; 14(1): 44-53.
[31]
Oussama L, Jamil MMA, Mahani W, Mahmud HW. Image segmentation techniques for red blood cell: On overview. J Teknol 2015; 77(6): 71-5.
[http://dx.doi.org/10.11113/jt.v77.6230]
[32]
Loddo A, Di Ruberto C, Kocher M. Recent advances of malaria parasites detection systems based on mathematical morphology. Sensors (Basel) 2018; 18(2): 513.
[http://dx.doi.org/10.3390/s18020513] [PMID: 29419781]
[33]
Anoraganingrum D. Cell segmentation with median filter and mathematical morphology operation. Proceedings of the 10th International Conference on Image Analysis and Processing. 1999 September 27-29; Venice, Italy. New York: IEEE
[http://dx.doi.org/10.1109/ICIAP.1999.797734]
[34]
Di Ruberto C, Dempster A, Khan S, Jarra B. Analysis of infected blood cell images using morphological operators. Image Vis Comput 2002; 20: 133-46.
[http://dx.doi.org/10.1016/S0262-8856(01)00092-0]
[35]
How KB, Bin ASK, Kok Bin AS, Siong NT, Soo KK. Red blood cell segmentation utilizing various image segmentation techniques. Proceedings of the International Conference on Biomedical Engineering (ICoBE). 2012 February 27-28; Penang, Malaysia. New York; IEEE:
[36]
Adollah R, Mashor MY, Mohd Nasir NF, Rosline H, Mahsin H, Adilah H. Blood cell image segmentation: A review. 4th Kuala Lumpur International Conference on Biomedical Engineering. IFMBE Proceedings. Springer; Berlin, Heidelberg 2008.
[37]
Kim K, Jeon J, Choi W, Kim P, Ho Y-S . Automatic cell classification in human’s peripheral blood images based on morphological image processing Advances in Artificial Intelligence. Lecture Notes in Computer Science; Springer, Berlin, Heidelberg 2001.
[http://dx.doi.org/10.1007/3-540-45656-2_20]
[38]
Sharif JM, Miswan M, Ngadi M, Salam MSH, Mahadi M. Red Blood cell segmentation using masking and watershed algorithm: A preliminary study. Proceedings of the International Conference on Biomedical Engineering (ICoBE). Penang, Malaysia. February 27-28, 2012; New York; IEEE.
[http://dx.doi.org/10.1109/ICoBE.2012.6179016]
[39]
Tomari R, Zakaria WNW, Jamil MMA, Nor FM, Fuad NFN. Computer aided system for red blood cell classification in blood smear image. Procedia Comput Sci 2014; 42: 206-13.
[http://dx.doi.org/10.1016/j.procs.2014.11.053]
[40]
Majumder D, Banerjee D, Chandra S, Banerjee S, Chakrabarti A. Red cell morphology in leukemia, hypoplastic anemia and myelodysplastic syndrome. Pathophysiology 2006; 13(4): 217-25.
[http://dx.doi.org/10.1016/j.pathophys.2006.06.002] [PMID: 16876391]
[41]
Das M, Das B, Das A, Chatterjee I, Majumder D. Computational analysis of ultrastructural images of red blood cells. J Oncol Transl Res 2015; 1: 104.
[http://dx.doi.org/10.4172/jotr.1000104]
[42]
Das M, Majumder D. An analytical approach for leukemia diagnosis from light microscopic images of RBCs. 2nd International Conference on Next Generation Computing Technologies (NGCT). 2016 October 14-16; Dehradun, India. New York: IEEE
[43]
Ledda A, Quintelier J, Samyn P, Baets PD, Philips W. Quantitative image analysis with mathematical morphology Proc Pro RISC2003. 2000; 1: 399-406.
[44]
Renato K. A morphological view on traditional signal processing. In: Mathematical morphology & its applications to image & signal processing, Computational imaging & vision series. Springer: USA 2000; pp. 3-12.
[45]
Mishra SJ, Deshmukh AP. Detection of leukemia using MatLab. Int J Adv Res Electron Commu Engg 2015; 4: 394-8.
[46]
McCann MT. Tools for automated histology image analysis [thesis] Carnegie Mellon University: Pittsburgh 2015.
[47]
Nelikanti A. Segmentation and analysis of cancer cells in blood samples. Int J Comput Sci Eng 2015; 6: 147-51.
[48]
Sirinukunwattana K, Ahmed Raza SE, Yee-Wah T , Snead DRJ, Cree IA, Rajpoot NM. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imaging 2016; 35(5): 1196-206.
[http://dx.doi.org/10.1109/TMI.2016.2525803] [PMID: 26863654]
[49]
Venmathi V, Shobana KN, Kumar A, Gajeshwaran D. Leukemia Detection using Image Processing. Digital Image Processing 2017; 9: 119-23.

Rights & Permissions Print Cite
Article Metrics
25
1
© 2024 Bentham Science Publishers | Privacy Policy