Unsupervised Segmentation of Leukocytes Images Using ThresholdingNeighborhood Valley-Emphasis

Thana A. A. Tosta, Andressa Finzi de Abreu,Bruno A. N. Travencolo and Marcelo Zanchetta do Nascimento

Department of Computer ScienceFederal University of Uberlandia, UFU

Uberlandia, Brazil{tosta.thaina, andressanzi, travencolo, marcelo.zanchetta}@gmail.com

Leandro Alves NevesDepartment of Computer Science and Statistics

Sao Paulo State University, UNESPSao Jose do Rio Preto, Brazil

neves.leandro@gmail.com

AbstractBlood smear image analysis is essential to corre-late the amount of leukocytes in these images with malignanciessuch as the leukemias. Techniques of digital image processingcan be used to aid pathologists in this analysis, leading toappropriate treatments for the patient. This paper presents anunsupervised segmentation method for the nuclear structuresin leukocytes. Deconvolution was used to split the Giemsa staincomponents and the regions of interest were selected using athresholding algorithm called Neighborhood Valley-emphasis.A postprocessing approach based on morphological operatorswas applied in these detected structures. The proposed al-gorithm was tested on 367 images containing leukocytes andother blood structures. A performance analysis was conductedthrough the Jaccard and accuracy metrics featuring results of89.89% and 99.57%, respectively. Such results were comparedto other published articles and this was considered the mostpromising method.

Keywords-Segmentation; Thresholding; White Blood Cells;Leukocytes; Nucleus; Blood Smear Images and Deconvolution.

I. INTRODUCTION

Leukocytes, also called white blood cells, are responsiblefor body defense [1]. Qualitative and quantitative charac-terization of these structures in blood images can indicatethe presence of infections, inammation and diseases, suchas the leukemias [2]. However, blood cells identication isa tedious, time-consuming and subjective task [2]. Digitalimage processing techniques are used in order to aid special-ists in this task. Among them, the segmentation is essentialto preserve most of the useful information and suppressirrelevant data, and its results also serve as basis in furthersteps [1].

Several studies in literature propose segmentation methodsfor leukocytes identication. Among them, Madhloom etal. presented a algorithm using a combination of contrastenhancement, arithmetical operations, minimum lter andOtsus thresholding [2]. Those techniques were also usedby Mohamed et al. [3] but with some modications, suchas the use of morphological operation of opening and theremoval of objects with small area.

II. MATERIALS AND METHODS

A. Dataset

A total of 367 images were used for evaluation of thissystem. RGB images with 640 480 pixels and 100magnication were obtained from peripheral blood samplesstained with Giemsa. The images are characterized by differ-ent illumination and contrast conditions, and differences inshape, spatial distribution and size of the cells. This datasetwas obtained from [3] and contain images with their nuclearregions marked manually by an expert.

B. Proposed Algorithm

1) Deconvolution: This technique separated two com-ponents of Giemsa stained images, methylene blue andeosin, based on optical density, which is proportional tothe concentration of each component in specic cellularstructures [4][5]. Deconvolution uses these concentrations toquantify the individual contribution of each stain componenton R, G and B channels through orthonormal transforma-tions. Figures 1(a), 1(b) and 1(c) exhibit an image fromthe dataset and its deconvolution results using the eosin andmethylene blue components, respectively.

2) Preprocessing: After deconvolution, the images wereprocessed using a median lter in order to remove noiseand standardize nuclear regions, due to differences in theintensity values of these structures. This lter was chosenfor its efciency and low processing time. Resulting imageswere submitted to linear contrast stretching to enhance thecontrast and make nuclear regions better represented byassigning darker colors to them, making them closer to aunimodal distribution. Figures 1(d) and 1(e) illustrate theapplications of median lter and linear contrast stretching,respectively.

3) Segmentation: The Neighborhood Valley-emphasismethod [6], used in this work, automatically determinea threshold value that separates regions of interest andbackground. This method uses the variance between classes,valley points and information from the neighborhood of

2015 IEEE 28th International Symposium on Computer-Based Medical Systems

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(a) (b) (c) (d) (e)

(f) (g) (h) (i) (j)

Figure 1. Obtained results using the image BloodImage 00052 (a)from the dataset by application of: (b) eosin component deconvolution,(c) methylene blue component deconvolution, (d) median lter, (e) linearcontrast stretching, (f) histogram of the Figure (e), (g) NeighborhoodValley-emphasis segmentation, (h) postprocessing step, (i) a mask fromthe automatic segmentation and (j) manual marking by a expert.

intensity levels from the histogram to determine the thresh-old value. This technique was chosen because most ofthe database images has unimodal histograms and someof them has more than one valley region, as illustrated byFigure 1(f), for which this method is indicated. The red linerepresents the threshold value chosen by this technique. TheFigure 1(g) shows the result of applying this segmentationon the image 1(e).

4) Postprocessing: In order to obtain images closer tothose marked by the expert, regions with less than 2000pixels, dened experimentally, were removed. The morpho-logical operations of opening and closing were applied toopen spaces between near objects and eliminate small gaps.A disk shape was chosen for opening and a square forclosing, both with size 5 and set empirically. Figure 1(h)displays the processing output.

C. Evaluation methods

Two metrics were used to evaluate the segmentation.Jaccards Similarity Coefcient measures the similarity be-tween two segmentations by dividing the size of its objectsintersection by the size of their union. The second is theAccuracy and divides the amount of pixels which receivethe same classication on both segmentations, by the totalamount of pixels in the image [7].

III. RESULTS AND DISCUSSION

Figure 1(i) presents the result of applying the maskobtained by automatic segmentation on original image andFigure 1(j) the nuclear region marked by a specialist. Using367 images with one or more nuclear regions, the proposedmethod achieved results of 89.89% and 99.57% with Jaccardand Accuray metrics, respectively, whereas the obtainedby [3] were 73.07% and 98.15%, and by [2], 54.41%and 95.49%. The use of deconvolution allows considerableinformation gain because it preserves nuclear regions, andeliminates other irrelevant structures. Furthermore, deconvo-lution reduces color variations that could be represented dueto different tissue preparation [8]. Use of the thresholding

technique was also effective, being based on color informa-tion while disregarding spatial distribution, size and shapeof the cells. However, a limitation of this algorithm is itsunsatisfactory results with weak boundaries.

IV. CONCLUSIONNuclei detection of leukocytes are sufcient for diagno-

sis of diseases and contribute for leukocytes classication,making quantitative analysis even more accurate. This workpresented a method for the automatic segmentation of thesestructures associating median lter, linear contrast stretch-ing, Modied Valley-emphasis, removal of small areas andmorphological operations. Experimental results proved thatthe proposed algorithm achieved better results using Jaccardand Accuracy metrics compared to [3] and [2] methods.

V. ACKNOWLEDGMENTST.A.A.T and A.F.A thank to CAPES and B.A.N.T. thanks

to FAPEMIG (Rede RED-00011-14 and APQ-01345-13) fornancial support.

REFERENCES

[1] R. Adollah, M. Y. Mashor, N. F. Mohd Nasir, H. Rosline,H. Mahsin and H. Adilah, 2008. Blood Cell Image Segmen-tation: A Review, 4th Kuala Lumpur International Conferenceon Biomedical Engineering 2008. Springer, 141 144.

[2] H. T. Madhloom, S. A. Kareem, H. Arifn, A. A. Zaidan,H. O. Alanazi and B. B. Zaidan, 2010. An Automated WhiteBlood Cell Nucleus Localization and Segmentation UsingImage Arithmetic and Automatic Threshold, J. Applied Sci;10: 959 966.

[3] M. Mohamed, B. Far and A. Guaily, 2012. An EfcientTechnique for White Blood Cells Nuclei Automatic Segmen-tation, IEEE International Conference on Systems, Man, andCybernetics (SMC). IEEE, 220 225.

[4] A. C. Ruifrok and D. A. Johnston, 2001. Quantication of His-tochemical Staining by Color Deconvolution, Analytical andquantitative cytology and histology/the International Academyof Cytology [and] American Society of Cytology. 23: 291 299.

[5] J. C. Junqueira and J. Carneiro, 2004. Histologia Basica,chapter Celulas do Sangue, Guanabara Koogan, 10 edition.pages 223 225.

[6] J. Fan and B. Lei, 2012. A Modied Valley-Emphasis Methodfor Automatic Thresholding, Pattern Recognition Letters. Else-vier, 33: 703 708.

[7] S. Ghosea, A. Olivera, R. Marta, X. Lladoa, J. C. Vilanovac,J. Freixeneta, J. Mitraa, D. Sidibeb and F. Meriaudeaub,2012. A Survey of Prostate Segmentation Methodologies inUltrasound, Magnetic Resonance and Computed TomographyImages, Computer Methods and Programs in Biomedicine.Elsevier, 108: 262 287.

[8] F. Dong, H. Irshad, E-Y. Oh, M. F. Lerwill, E. F. Brachtel,et al., 2014 Computational Pathology to Discriminate Benignfrom Malignant Intraductal Proliferations of the Breast, PLoSONE 9(12): e114885.

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