Review on Image Segmentation Techniques Incorporated with Machine Learning in the Scrutinization of Leukemic Microscopic Stained Blood Smear Images

This paper is a contemplated work of N-different methods that have been employed in the area of revealing and classifying leukocytes and leukoblast cells. Blood cell images obtained through digital microscopes are taken as input to the algorithms reviewed. In bringing out the nucleus and cytoplasm of White Blood Cells (WBCs), the images have been undergone by a variety of image segmentation techniques along with filtering, enhancement, edge detection, feature extraction, classification, and image recognition steps. Apart from image processing, the analysis and categorization of the leukemic images are handled using some other machine learning techniques of computer science discipline. Assessment of accuracy and correctness of the proposals were done by applying texture, color, contour, morphological, geometrical, and statistical features.

[1]  Flávio H. D. Araújo,et al.  Unsupervised Leukemia Cells Segmentation Based on Multi-space Color Channels , 2016, 2016 IEEE International Symposium on Multimedia (ISM).

[2]  et al. Bhukya.  Detection of acute lymphoblastic leukemia using microscopic images of blood , 2017 .

[3]  V. Piuri,et al.  Morphological classification of blood leucocytes by microscope images , 2004, 2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA..

[4]  Wan Nurshazwani Wan Zakaria,et al.  Mobile based Automated Complete Blood Count (Auto-CBC) Analysis System from Blood Smeared Image , 2017 .

[5]  A. Heyden,et al.  Segmentation of dense leukocyte clusters , 2001, IEEE Workshop on Mathematical Methods in Biomedical Image Analysis.

[6]  Cecilia Di Ruberto,et al.  White Blood Cells Identification and Counting from Microscopic Blood Image , 2013 .

[7]  Huijuan Lu,et al.  Segmentation of Blood and Bone Marrow Cell Images via Learning by Sampling , 2009, ICIC.

[8]  Lalit Mohan Saini,et al.  Robust technique for the detection of Acute Lymphoblastic Leukemia , 2015, 2015 IEEE Power, Communication and Information Technology Conference (PCITC).

[9]  Sameem Abdul Kareem,et al.  Computer-aided acute leukemia blast cells segmentation in peripheral blood images , 2015 .

[10]  Dzulkifli Mohamad,et al.  Automatic Color Nuclei Segmentation of Leukocytes for Acute Leukemia , 2014 .

[11]  Ahasan Ulla Ratul,et al.  White blood cells nucleus segmentation from microscopic images of strained peripheral blood film during leukemia and normal condition , 2016, 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV).

[12]  Shubhangi Khobragade,et al.  Detection of leukemia in microscopic white blood cell images , 2015, 2015 International Conference on Information Processing (ICIP).

[13]  V. G. Nikitaev,et al.  A method for estimating the accuracy of measurements of optical characteristics of the nuclei of blood cells in the diagnosis of acute leukemia , 2017 .

[14]  Pagavathigounder Balasubramaniam,et al.  A new thresholding technique based on fuzzy set as an application to leukocyte nucleus segmentation , 2016, Comput. Methods Programs Biomed..

[15]  Sara Colantonio,et al.  Automatic fuzzy-neural based segmentation of microscopic cell images , 2008 .

[16]  Chaitali Raje,et al.  Detection of Leukemia in microscopic images using image processing , 2014, 2014 International Conference on Communication and Signal Processing.

[17]  I. Cseke,et al.  A fast segmentation scheme for white blood cell images , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis,.

[18]  H. Ramoser,et al.  Leukocyte segmentation and classification in blood-smear images , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[19]  Qingmin Liao,et al.  An accurate segmentation method for white blood cell images , 2002, Proceedings IEEE International Symposium on Biomedical Imaging.

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

[21]  Sos S. Agaian,et al.  Deterministic model for Acute Myelogenous Leukemia classification , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[22]  W. Badawy,et al.  High Throughput Algorithm for Leukemia Cell Population Statistics on a Hemocytometer , 2007, 2007 IEEE Biomedical Circuits and Systems Conference.

[23]  Ardeshir Talebi,et al.  Segmentation of White Blood Cells From Microscopic Images Using a Novel Combination of K-Means Clustering and Modified Watershed Algorithm , 2017, Journal of medical signals and sensors.

[24]  R.A. Zoroofi,et al.  Segmentation of nucleus and cytoplasm of white blood cells using Gram-Schmidt orthogonalization and deformable models , 2008, 2008 9th International Conference on Signal Processing.

[25]  Shuai Liu,et al.  A segmentation method based on HMRF for the aided diagnosis of acute myeloid leukemia , 2017, Comput. Methods Programs Biomed..

[26]  F. Scotti,et al.  Robust Segmentation and Measurements Techniques of White Cells in Blood Microscope Images , 2006, 2006 IEEE Instrumentation and Measurement Technology Conference Proceedings.

[27]  Aimi Salihah Abdul Nasir,et al.  Unsupervised Segmentation Technique for Acute Leukemia Cells Using Clustering Algorithms , 2015 .

[28]  Sos S. Agaian,et al.  Automated Screening System for Acute Myelogenous Leukemia Detection in Blood Microscopic Images , 2014, IEEE Systems Journal.

[29]  Chandan Chakraborty,et al.  Automated leukocyte recognition using fuzzy divergence. , 2010, Micron.

[30]  Cecilia Di Ruberto,et al.  A Multiple Classifier Learning by Sampling System for White Blood Cells Segmentation , 2015, CAIP.

[31]  F. Scotti,et al.  Automatic morphological analysis for acute leukemia identification in peripheral blood microscope images , 2005, CIMSA. 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, 2005..

[32]  Jaya Sharma,et al.  Identification and classification of acute leukemia using neural network , 2014, 2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom).

[33]  Behrouz Homayoun Far,et al.  An enhanced threshold based technique for white blood cells nuclei automatic segmentation , 2012, 2012 IEEE 14th International Conference on e-Health Networking, Applications and Services (Healthcom).

[34]  Babak Nadjar Araabi,et al.  Automatic Recognition of Acute Myelogenous Leukemia in Blood Microscopic Images Using K-means Clustering and Support Vector Machine , 2016, Journal of medical signals and sensors.

[35]  Kan Jiang,et al.  A novel white blood cell segmentation scheme using scale-space filtering and watershed clustering , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[36]  M. Y. Mashor,et al.  White blood cell segmentation for acute leukemia bone marrow images , 2012, 2012 International Conference on Biomedical Engineering (ICoBE).

[37]  Saeed Kermani,et al.  Computer aided detection and classification of acute lymphoblastic leukemia cell subtypes based on microscopic image analysis , 2016, Microscopy research and technique.

[38]  S. Hariharan,et al.  An efficient peripheral blood smear image analysis technique for Leukemia detection , 2017, 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC).

[39]  Camel Tanougast,et al.  Algorithmic processing to aid in leukemia detection , 2017 .

[40]  Nipon Theera-Umpon,et al.  Morphological Granulometric Features of Nucleus in Automatic Bone Marrow White Blood Cell Classification , 2007, IEEE Transactions on Information Technology in Biomedicine.

[41]  Mohamed Elhoseny,et al.  Computer-Aided Acute Lymphoblastic Leukemia Diagnosis System Based on Image Analysis , 2018 .

[42]  Lina,et al.  Leukocyte Detection Using Image Stitching and Color Overlapping Windows , 2016 .

[43]  Kosin Chamnongthai,et al.  Acute leukemia classification by using SVM and K-Means clustering , 2014, 2014 International Electrical Engineering Congress (iEECON).

[44]  Cecilia Di Ruberto,et al.  Accurate Blood Cells Segmentation through Intuitionistic Fuzzy Set Threshold , 2014, 2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems.

[45]  Wael M. Badawy,et al.  A High Throughput Screening Algorithm for Leukemia Cells , 2006, 2006 Canadian Conference on Electrical and Computer Engineering.

[46]  Rong-Ming Chen,et al.  Blood Cell Image Classification Based on Hierarchical SVM , 2011, 2011 IEEE International Symposium on Multimedia.

[47]  Xiaomei Li,et al.  Segmentation of White Blood Cells through Nucleus Mark Watershed Operations and Mean Shift Clustering , 2015, Sensors.

[48]  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.

[49]  Thorsten Zerfaß,et al.  Segmentation of leukocytes and erythrocytes in blood smear images , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[50]  Eduardo F. Morales,et al.  Image Segmentation Using Automatic Seeded Region Growing and Instance-Based Learning , 2007, CIARP.

[51]  Régis Beuscart,et al.  Image Segmentation And Classification Methods To Detect Leukemias , 1991, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Volume 13: 1991.

[52]  R. Kumar,et al.  Detection and Classification of Cancer from Microscopic Biopsy Images Using Clinically Significant and Biologically Interpretable Features , 2015, Journal of medical engineering.

[53]  José Rodellar,et al.  Feature Analysis and Automatic Identification of Leukemic Lineage Blast Cells and Reactive Lymphoid Cells from Peripheral Blood Cell Images , 2017, Journal of clinical laboratory analysis.

[54]  V. G. Nikitaev,et al.  The influence of physical factors on recognizing blood cells in the computer microscopy systems of acute leukemia diagnosis , 2017 .

[55]  Janu R. Panicker,et al.  Enhanced leucocyte classification for leukaemia detection , 2016, 2016 International Conference on Information Science (ICIS).

[56]  ByoungChul Ko,et al.  A novel framework for white blood cell segmentation based on stepwise rules and morphological features , 2011, Electronic Imaging.

[57]  Reham Mohammed,et al.  Segmentation of Acute Lymphoblastic Leukemia Using C-Y Color Space , 2014 .

[58]  Jesus A. Gonzalez,et al.  Segmentation and Classification of Bone Marrow Cells Images Using Contextual Information for Medical Diagnosis of Acute Leukemias , 2015, PloS one.

[59]  Wendy N Erber,et al.  Applications of imaging flow cytometry in the diagnostic assessment of acute leukaemia. , 2017, Methods.

[60]  Abdul Rahman Ramli,et al.  A Framework for White Blood Cell Segmentation in Microscopic Blood Images Using Digital Image Processing , 2009, Biological Procedures Online.

[61]  Dorin Comaniciu,et al.  Computer-assisted discrimination among malignant lymphomas and leukemia using immunophenotyping, intelligent image repositories, and telemicroscopy , 2000, IEEE Transactions on Information Technology in Biomedicine.

[62]  M.Y. Mashor,et al.  Nucleus segmentation technique for acute Leukemia , 2011, 2011 IEEE 7th International Colloquium on Signal Processing and its Applications.

[63]  Soumen Biswas,et al.  Blood Cell Detection Using Thresholding Estimation Based Watershed Transformation with Sobel Filter in Frequency Domain , 2016 .

[64]  Mohd Yusoff Mashor,et al.  White blood cell segmentation for acute leukemia bone marrow images , 2012 .

[65]  A. Shanmugam,et al.  WBC image segmentation and classification using RVM , 2014 .

[66]  Nor Hazlyna Harun,et al.  Comparison of acute leukemia Image segmentation using HSI and RGB color space , 2010, 10th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010).

[67]  Dean C. Barratt,et al.  Designing image segmentation studies: Statistical power, sample size and reference standard quality , 2017, Medical Image Anal..

[68]  Mznah Al-Rodhaan,et al.  Report: Nuclei segmentation of leukocytes in blood smear digital images. , 2015, Pakistan journal of pharmaceutical sciences.

[69]  N Chaitra,et al.  Automatic detection of acute lymphoblasitc leukemia using image processing , 2016, 2016 IEEE International Conference on Advances in Computer Applications (ICACA).

[70]  Hossein Rabbani,et al.  Extraction of Nucleolus Candidate Zone in White Blood Cells of Peripheral Blood Smear Images Using Curvelet Transform , 2012, Comput. Math. Methods Medicine.

[71]  Sung Wook Baik,et al.  Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities , 2017, IEEE Access.

[72]  P. Viswanathan,et al.  Fuzzy C Means Detection of Leukemia Based on Morphological Contour Segmentation , 2015 .

[73]  Preety Singh,et al.  Local Binary Pattern for automatic detection of Acute Lymphoblastic Leukemia , 2014, 2014 Twentieth National Conference on Communications (NCC).

[74]  ByoungChul Ko,et al.  Cell image classification based on ensemble features and random forest , 2011 .

[75]  Mohd Azhar Abdul Razak,et al.  Automated Red Blood Cells Counting in Peripheral Blood Smear Image Using Circular Hough Transform , 2013, 2013 1st International Conference on Artificial Intelligence, Modelling and Simulation.

[76]  H. S. Bhadauria,et al.  Computer Aided Diagnostic System for Detection of Leukemia Using Microscopic Images , 2015 .

[77]  B. k. Lakshmikanth,et al.  Acute Myelogenous Leukemia Detection in Blood Microscopic Images using Different Wavelet Family Techniques , 2017 .

[78]  Adnan Khashman,et al.  Image segmentation of blood cells in leukemia patients , 2010 .

[79]  Tanuja Sarode,et al.  Cancerous Cell Detection in Bone Marrow Smear , 2016 .

[80]  A. G. Ramakrishnan,et al.  Automation of differential blood count , 2003, TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region.

[81]  Amit Konar,et al.  Automatic leukocyte nucleus segmentation by intuitionistic fuzzy divergence based thresholding. , 2014, Micron.

[82]  Hamid Soltanian-Zadeh,et al.  A New Approach to White Blood Cell Nucleus Segmentation Based on Gram-Schmidt Orthogonalization , 2009, 2009 International Conference on Digital Image Processing.

[83]  Leyza Baldo Dorini,et al.  White blood cell segmentation using morphological operators and scale-space analysis , 2007 .

[84]  Dipti Patra,et al.  Automated cell nucleus segmentation and acute leukemia detection in blood microscopic images , 2010, 2010 International Conference on Systems in Medicine and Biology.

[85]  A. Vahedi,et al.  Leukemia Cell Recognition with Zernike Moments of Holographic Images , 2006, Proceedings of the 7th Nordic Signal Processing Symposium - NORSIG 2006.

[86]  Yan Li,et al.  Segmentation of White Blood Cell from Acute Lymphoblastic Leukemia Images Using Dual-Threshold Method , 2016, Comput. Math. Methods Medicine.