Segmentation of White Blood Cell from Acute Lymphoblastic Leukemia Images Using Dual-Threshold Method

We propose a dual-threshold method based on a strategic combination of RGB and HSV color space for white blood cell (WBC) segmentation. The proposed method consists of three main parts: preprocessing, threshold segmentation, and postprocessing. In the preprocessing part, we get two images for further processing: one contrast-stretched gray image and one H component image from transformed HSV color space. In the threshold segmentation part, a dual-threshold method is proposed for improving the conventional single-threshold approaches and a golden section search method is used for determining the optimal thresholds. For the postprocessing part, mathematical morphology and median filtering are utilized to denoise and remove incomplete WBCs. The proposed method was tested in segmenting the lymphoblasts on a public Acute Lymphoblastic Leukemia (ALL) image dataset. The results show that the performance of the proposed method is better than single-threshold approach independently performed in RGB and HSV color space and the overall single WBC segmentation accuracy reaches 97.85%, showing a good prospect in subsequent lymphoblast classification and ALL diagnosis.

[1]  Kaoru Hirota,et al.  Interest-Based Ordering for Fuzzy Morphology on White Blood Cell Image Segmentation , 2012, J. Adv. Comput. Intell. Intell. Informatics.

[2]  Andrew G. Dempster,et al.  Segmentation of blood images using morphological operators , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[3]  Uzzal Kumar Prodhan,et al.  Segmentation of White Blood Cells Using Fuzzy C Means Segmentation Algorithm , 2014 .

[4]  Nancy M. Salem,et al.  Segmentation of white blood cells from microscopic images using K-means clustering , 2014, 2014 31st National Radio Science Conference (NRSC).

[5]  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,.

[6]  Dipti Patra,et al.  Automated leukemia detection using hausdorff dimension in blood microscopic images , 2010, INTERACT-2010.

[7]  K. V. Arya,et al.  Automated microscopic image analysis for leukocytes identification: a survey. , 2014, Micron.

[8]  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..

[9]  Preety Singh,et al.  Correlation based Feature Selection for Diagnosis of Acute Lymphoblastic Leukemia , 2015, WCI '15.

[10]  Rastislav Lukac,et al.  Color image processing , 2007, Comput. Vis. Image Underst..

[11]  Leyza Baldo Dorini,et al.  White blood cell segmentation using morphological operators and scale-space analysis , 2007, XX Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2007).

[12]  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.

[13]  Vincenzo Piuri,et al.  All-IDB: The acute lymphoblastic leukemia image database for image processing , 2011, 2011 18th IEEE International Conference on Image Processing.

[14]  Kamal A. ElDahshan,et al.  Segmentation Framework on Digital Microscope Images for Acute Lymphoblastic Leukemia Diagnosis based on HSV Color Space , 2014 .

[15]  Kamal A. ElDahshan,et al.  Comparison of Segmentation Framework on Digital Microscope Images for Acute Lymphoblastic Leukemia Diagnosis Using RGB and HSV Color Spaces , 2015 .

[16]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[17]  Le Yu,et al.  A WBC segmentation methord based on HSI color space , 2011, 2011 4th IEEE International Conference on Broadband Network and Multimedia Technology.

[18]  M. Y. Mashor,et al.  Local and Global Contrast Stretching For Color Contrast Enhancement on Ziehl-Neelsen Tissue Section Slide Images , 2008 .

[19]  Mohammad Hamghalam,et al.  Leukocyte Segmentation in Giemsa-stained Image of Peripheral Blood Smears Based on Active Contour , 2009, 2009 International Conference on Signal Processing Systems.

[20]  Yuan Zhou,et al.  A novel color image segmentation method and its application to white blood cell image analysis , 2006, 2006 8th international Conference on Signal Processing.

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

[22]  Jae-Yeal Nam,et al.  Automatic white blood cell segmentation using stepwise merging rules and gradient vector flow snake. , 2011, Micron.

[23]  Leyza Baldo Dorini,et al.  Semiautomatic White Blood Cell Segmentation Based on Multiscale Analysis , 2013, IEEE Journal of Biomedical and Health Informatics.

[24]  Yuan Xiong,et al.  A novel white blood cell segmentation scheme based on feature space clustering , 2006, Soft Comput..

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

[26]  Salim Arslan,et al.  A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images , 2014, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[27]  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..