Unsupervised Segmentation of Leukocytes Images using Particle Swarm

Jocival Dantas Dias J

unior and Andr

e R. Backes

School of Computer Science, Federal University of Uberl

andia, Av. Jo

ao Naves de

Avila, 2121, Uberl

andia, MG, Brazil

Keywords:

Image Segmentation, Blood Cell, White Blood Cells, Leukocytes.

Abstract:

Blood smear image analysis is an essential task for many health related issues. Among the many blood

structures present in these images, leukocytes play an important role in the detection of many diseases (such

as leukemias), which can be detected by the amount, or abnormal aspect, of the leukocytes. To address this

problem, this paper presents an unsupervised segmentation method for the nuclear structures in leukocytes.

Our method uses color deconvolution to separate the dyes in different channels and a PSO algorithm to estimate

an optimal kernel ﬁlter to combine local features in different stain channels to emphasize the leukocytes

structures so that simple thresholding techniques are able to perform image segmentation. We also used a

postprocessing approach based on morphological operators to reﬁne the border of detected structures, thus

improving our performance. We performed a comparison with different approaches found in literature using

367 images containing leukocytes and other blood structures and results demonstrated the superiority of our

approach in terms of Jaccard index.

1 INTRODUCTION

One of the main parts of the immune system are the

White Blood Cells (WBCs), also called Leukocytes,

which are produced in the bone marrow and lymphoid

tissues. These cells are divided into ﬁve types Lym-

phocytes, Monocytes, Eosinophils Basophils, and

Neutrophils. They are responsible for protecting the

body against infections such as bacteria, viruses, and

fungi.

Normally, a healthy human has four to eleven

thousand leukocytes per cubic inch of blood (Banik

et al., 2020), and the excess or lack of these cells can

cause several diseases (Kutlu et al., 2020). The pro-

cess for counting these cells usually involves the seg-

mentation and classiﬁcation process. Hematologists,

with the aid of microscopes, have to manually seg-

ment the white cells to later classify them into their

types. This process, in addition to requiring several

hours from a trained professional, has its accuracy

quite dependent on the agent who is doing the mea-

surement (Banik et al., 2020).

Technological advances in the ﬁeld of digital

pathology have brought automatic procedures for the

detection and classiﬁcation of microscopic images of

WBCs. This procedure consists of connecting digital

cameras to microscopes to obtain high-resolution im-

ages to assist hematologists (Al-Dulaimi et al., 2020).

As a consequence, the use of image processing sys-

tems for this task have grown every day. Typically,

these systems have two main activities: segmentation

and classiﬁcation of blood cells.

Given the importance of the segmentation process

for further classiﬁcation, this work proposes an unsu-

pervised segmentation method for the nuclear struc-

tures in leukocytes. This approach separates the RGB

image into three channels and later uses the Parti-

cle Swarm Optimization(PSO) algorithm to estimate

an optimal kernel ﬁlter to combine local features in

different stain channels to emphasize the leukocyte’s

structures. After threshold segmentation, morpholog-

ical operators are used to reﬁne the edges of the de-

tected structures.

The remainder of this paper is organized as fol-

lows: In Section 2, we present a review of the state

of the art in leukocyte segmentation. In Section 3, we

present the concepts used in this work. We present our

approach in detail in Section 4. Section 5 presents the

experiments and the results obtained. Finally, Section

6 concludes this paper.

2 RELATED WORKS

Due to its great scientiﬁc relevance, several works

were proposed for the segmentation of WBCs

Dias Júnior, J. and Backes, A.

Unsupervised Segmentation of Leukocytes Images using Particle Swarm.

DOI: 10.5220/0010309404390446

In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP, pages

439-446

ISBN: 978-989-758-488-6

439

(Rezatoﬁghi et al., 2009) (Madhloom et al., 2010)

(Mohamed and Far, 2012a) (Mohamed and Far,

2012b) (Mohamed et al., 2012) (Tosta et al., 2015)

(Tareef et al., 2016) (Tareef et al., 2017). Below, we

highlight some works that were used as a comparison

with our approach.

The work of (Tosta et al., 2015) proposed an un-

supervised approach to the segmentation of nuclear

structures in leukocytes. The authors’ method con-

sists of four steps. Firstly, the deconvolution process

is applied to the image to separate two components

of Giemsa stained images, methylene blue and eosin,

based on optical density, which is proportional to the

concentration of each component in speciﬁc cellular

structures. Later, in the second stage, a median ﬁl-

ter is applied to remove noise and standardize nuclear

regions. In the third stage, the Neighborhood Valley-

emphasis method automatically determines a thresh-

old value that separates regions of interest and back-

ground. In the last step, post-processing is performed

with the morphological opening and closing operators

to eliminate small holes. The work was evaluated us-

ing the Jaccard coefﬁcient and Precision. The authors

obtained a result of 89.89% in the Jaccard coefﬁcient

and 99.57% in Precision. In conclusion, the authors

point out that the main limitation of this work is the

low result in dealing with the edges of the structures.

In the work of (Tareef et al., 2016), a three-stage

approach to leukocyte segmentation is proposed. The

ﬁrst step is the segmentation of the nuclei, this seg-

mentation consists of the transformation of the RGB

color space to the CIE LAB, having both the RGB

image and the CIE LAB, a grayscale image is gen-

erated by adding the red channel with the luminance

and subtracting the color component A from the CIE

LAB. With the grayscale image, the Poisson distri-

bution based minimum error thresholding algorithm

is applied to the obtained gray-scale image to get the

nuclei candidates. In the second step, which consists

of cytoplasm segmentation, the authors use discrete

wavelet transform (DWT) and morphological ﬁltering

to eliminate small details and noise and to increase the

contrast between the cytoplasm and the other struc-

tures. At the end of this stage, cytoplasm candidates

are selected using the Otsu method. In the last step,

the authors perform a reﬁnement and ﬁltering to ob-

tain the ﬁnal segmentation. First, the regularized level

set is applied to reﬁne the cytoplasm candidate con-

tour. Subsequently, an opening is applied followed by

an expansion to remove the excess of edges. In the

end, a ﬁltering process is carried out to remove false

candidates for nuclei and cytoplasm, which consists

of removing nuclei that are not surrounded by cyto-

plasm. The authors’ work was evaluated using the

similarity metric, obtaining a result of 85.10% for the

BloodSeg dataset.

In the paper (Tareef et al., 2017), the authors pro-

posed a framework based on four stages for the seg-

mentation of leukocytes: clustering-based color en-

hancement and reduction, nuclei segmentation, cyto-

plasm segmentation, and post-processing. In the ﬁrst

stage, the authors created a technique that reduces

the range of colors while preserving the contours of

the cells. In this step, a median ﬁlter, followed by

a contrast adjustment, is applied to the original im-

age. Subsequently, they apply a clustering algorithm

to the image to divide it into coherent regions. For

each cluster found, they compute the median value for

each color channel. Then, the authors use the Gram-

Schmidt orthogonalization method to compute a vec-

tor of weights that is later used to highlight the re-

gion to be segmented. In the third stage, the method

applies the watershed transform to segments the cy-

toplasm. In the end, the authors apply several mor-

phological operations and ﬁlters to reﬁne the results.

The authors evaluated their results using the similarity

metric, which obtained an average result of 88.2.

3 MATERIAL AND METHODS

3.1 Color Deconvolution

The main goal of color deconvolution is to separate

immunohistochemical dye channels such as hema-

toxylin (H) and eosin (E). In this paper we used the

method based on the orthonormal transformation of

the RGB image in order to separate the dyes in differ-

ent channels (Ruifrok et al., 2001; Wang et al., 2017).

When a monochromatic radiation passes through an

absorbing dye, that dye absorbs a fraction of the light

according to the Bouguer-Lambert-Beer equation:

I = I

−δ.c

(1)

where I is the intensity of the monochromatic radia-

tion, I

is the intensity of the transmitted radiation, δ

is the spectral molar optical density for a uniﬁed layer

thickness and c is the dye concentration.

The optical density (OD) of a channel i is deﬁned

= −log

, (2)

and it has a linear relation with the concentration of

absorbing material so that it is useful to estimate the

contribution of each stain in a sample. The contri-

bution of each stain is given by a matrix where each

row represents a speciﬁc stain. After the orthonormal

transformation and normalization, the contribution of

VISAPP 2021 - 16th International Conference on Computer Vision Theory and Applications

440

H, E and DAB stains in a RGB image is given by the

following matrix, M:





0.65 0.70 0.29

0.07 0.99 0.11

0.27 0.57 0.78





(3)

Given a RGB image, y = CM describes the

amount of each stain in a particular pixel, where y and

C are 3 × 1 vector for, respectively, stains and RGB

colors. Then, color deconvolution is deﬁned as the

matrix D = M

−1

y. For H, E and DAB stains, D is

deﬁned as follows:





1.88 −0.70 −0.29

−0.07 1.13 −0.11

−0.55 −0.13 1.57





(4)

3.2 Particle Swarm Optimization

Nature has been inspiring of human development in

many subjects and it is not different in the computer

sciences area. There exists many methods and tech-

niques to solve computational problems that use mod-

els inspired in the biology, being one of them the Par-

ticle Swarm Optimization (PSO) algorithm. Proposed

by (Kennedy and Eberhart, 1995), PSO is a swarm

intelligent optimization algorithm. It is mostly in-

spired by the behavior of ﬂocks of birds and schools

of ﬁshes, where these animals are capable to move

synchronously while changing their direction, scat-

tering and regrouping. By mimicking this swarm be-

havior, a system of particles is capable to search for

global optimum by combining its own solution with

the one provided by other members of the swarm.

In PSO algorithm, given a system with N particles,

we associate a position, {X

(t), X

(t), . . . , X

(t)},

and velocity, {V

(t), V

(t), . . . , V

(t)}, to a particle

i, i = 1, 2, . . . , N, where D represents the number of

dimensions in the search space. For a current itera-

tion t, t = 1, 2, . . . , T

Max

, where T

Max

is the maximum

number of iterations, we update the velocity and po-

sition of each particle by using the following set of

equations:

(t + 1) = ω(t)V

(t) + c

(Pbest

(t) − X

(t))+

(Gbest(t) − X

(t)) (5)

(t + 1) = X

(t) +V

(t + 1) (6)

where c

and c

are the acceleration factors and r

and

are randomly generated values ranging from [0, 1].

ω is the inertia weight and it is deﬁned as a linear

decreasing variable as follows:

ω(t) = ω

max

−

(ω

max

− ω

min

)

Max

(7)

where ω

max

= 0.9 and ω

min

= 0.4. Personal and

global best solutions, Pbest

and Gbest respectively,

are deﬁned as follows:

Pbest

(t) = argmin{ f it(X

(1)), . . . , f it(X

(t))} (8)

Gbest(t) = arg min{ f it(Pbest

(t)),

. . . , f it(Pbest

(t))} (9)

3.3 Binarization

In many occasions it is necessary to reduce the num-

ber of gray-levels (or colors) of an image in order to

better evaluate its content. When the resulting im-

age is composed by only black and white pixels, this

process is called as image binarization (Sezgin and

Sankur, 2004).

Literature presents many and different approaches

to convert an image into a binary one, being the sim-

plest approach to apply a threshold value T to each

pixel. Thus, a pixel whose value is greater than the

threshold is classiﬁed as white or 1; otherwise, this

pixel is classiﬁed as black or 0.

Although this threshold T can be manually de-

ﬁned, there is no guarantee that the chosen value

is the best threshold for images under different ac-

quisition conditions, such as illumination and con-

trast. This has motivated the development of algo-

rithm that compute the best threshold value for each

image based on the gray-levels distribution of the im-

age, such as the Otsu (Otsu, 1979), Valley Emphasis

(Ng, 2006), Modiﬁed Valley Emphasis (Fan and Lei,

2012) and Balanced Histogram (dos Anjos and Shah-

bazkia, 2008) methods.

It is also important to emphasize that due to fac-

tors such as non-uniform illumination, not all images

can be converted to a binary one by using a single

threshold value. For these images it is recommended

the use of adaptive image binarization, where differ-

ent threshold values are computed for different por-

tions of the image.

4 PROPOSED FRAMEWORK

In this section we describe the proposed framework

used to segment leukocytes from other blood struc-

tures in images. Given an input image, we compute

its deconvolution to emphasize the color information

of the leukocytes present in it. In the sequence, we use

a 3×3×3 kernel ﬁlter (computed using the PSO algo-

rithm) to convert the image resulting from deconvolu-

tion into a grayscale image that highlights the leuko-

cytes. Then we use a binarization algorithm to select

Unsupervised Segmentation of Leukocytes Images using Particle Swarm

441

the leukocytes regions of the images. Since more ob-

jects are present in the image (e.g., other blood struc-

tures) and may be detected as leukocytes, we apply

two a post processing steps to select actual leukocytes

and to ensure the quality of the leukocytes detected.

Figure 1 displays the framework of the method.

4.1 Image Segmentation using PSO

Cell staining, such as H&E, is a procedure used to

increase color contrast of different structures, thus al-

lowing for a clearer view and improving the perfor-

mance of segmentation algorithms. Therefore, in-

stead of using color segmentation algorithms or other

color space models, we propose to use a simple kernel

ﬁlter to emphasize the leukocytes in images by com-

bining local features in different stain channels.

We used a particle swarm algorithm to optimize

the 27 ﬂoating point values that composes a 3 × 3 ×3

kernel ﬁlter. After applying the color deconvolution

algorithm on an input image, we applied this kernel

ﬁlter to combine local characteristics present in each

stain channel, thus producing a single gray scale im-

age S. The main idea is that the kernel ﬁlter is able to

generate an image S that highlights the main charac-

teristics of leukocytes, so that it could be easily seg-

mented using a simple automatic threshold approach

(e.g., Otsu method), as shown in Figure 2. To mea-

sure how accurate the proposed segmentation is we

used Jaccard index (Ghose et al., 2012), as described

in Equation 10:

J(A, B) =

|A ∩ B|

A ∪ B

(10)

where A and B are two binary images, respectively,

our segmentation and the expert’s segmentation. D,

0 ≤ D ≤ 1, is the similarity level between the images,

and the more the value D is close to 1, the more simi-

lar the images are.

4.2 Leukocytes Selection

After the image segmentation, it is necessary to verify

whether each detected object is actually a leukocyte or

not. To accomplish that, we proposed the following

procedure. Initially we performed a morphological

opening using a disk of radius r = 5 in order to sepa-

rate near objects that are connected. We computed the

area of all objects. This area is normalized by the area

of the largest object. In the sequence, we removed all

objects with a normalized area smaller than or equal

to 0.2 from the image. We considered the remaining

objects as leukocytes. This step was performed in or-

der to remove objects that are too small in relation

to the others. These objects represent small segmen-

tation errors and noise present in the original image.

Finally, we computed the image complement of the

resulting segmentation containing the detected leuko-

cytes and removed all objects with an area smaller

than 100 pixels. The operation was carried out to

eliminate small background regions present inside the

leukocytes detected during the segmentation stage.

4.3 Border Reﬁnement

When analyzing the leukocyte image it is possible

to notice that there is a diffuse region separating the

leukocyte from the image background. As a conse-

quence, the segmentation process may not correctly

detect the leukocyte border or to detect background

regions adjacent to a leukocyte as part of it. Thus, we

proposed a process of reﬁning the leukocyte border

detected in the segmentation step. This process be-

gins with the dilation of the segmented image using

a disk of radius r = 5. This is performed so that un-

detected regions of the leukocyte border are included

in the reﬁnement process. Then, we performed an

erosion process guided by the color of the leukocyte.

This process is executed for each leukocyte detected

and it is deﬁned as follows:

1. Given an binary object A, compute its morpholog-

ical erosion using a disk of radius r = 2, B;

2. Compute the difference between objects A and B,

C = A − B, where C is the region removed in the

process of erosion;

3. Compute the average color of object B in the orig-

inal image,

RGB

;

4. For each pixel of C, compute the Euclidean dis-

tance between its color in the original image and

the average color

RGB

5. Remove from object A all point in C whose dis-

tance is greater than a threshold T = 75.

6. Repeat this process whenever more than 25% of

the pixels of C are removed from object A.

Figure 3 shows an example of border reﬁnement

for a given leukocytes image.

5 RESULTS AND DISCUSSION

To evaluate our approach we used a dataset containing

leukocytes images (Tosta et al., 2015). This dataset

contains 367 color images of leukocytes stained with

hematoxylin and eosin (H&E). Each image has

640 × 480 pixels size and we applied color deconvo-

lution over them before applying any other step of our

VISAPP 2021 - 16th International Conference on Computer Vision Theory and Applications

442

(a) (b)

(e) (f)

Figure 1: Proposed framework for leukocytes segmentation: (a) Input image; (b) Image deconvolution; (c) Filtered image; (d)

Image after Binarization; (e) Leukocytes selection; (f) Border reﬁnement.

proposed methodology. In order to segment the image

we trained a PSO algorithm to optimize the 27 ﬂoat-

ing point values that composes a 3×3×3 kernel ﬁlter.

To accomplish that we randomly select 10% of the

sample in the dataset for training. As for the ﬁtness

function, the PSO algorithm aimed to search for the

kernel ﬁlter that maximized the average Jaccard index

of the images. To execute the PSO algorithm we con-

sidered a population size of 100 individuals running

for 5400 generations. In the sequence, we applied all

steps of the proposed framework in all images of the

dataset in order to report the average results obtained.

Unsupervised Segmentation of Leukocytes Images using Particle Swarm

443

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

Figure 2: Comparison of the proposed ﬁltering scheme with image deconvolution: (a) Resulting ﬁltered image; (b) Hema-

toxylin; (c) Eosin; (d) DAB.

(a) (b)

Figure 3: (a) Selected Leukocytes; (b) Comparison of (a) with the markings provided by experts; (c) Image (a) after border

reﬁnement; (d) Comparison of (c) with the markings provided by experts. Blue color indicates a region undetected by our

method while red color indicates a region wrongly detected.

In our proposed framework, we use a binarization

approach to convert the ﬁltered image S into a binary

one. During the training of our PSO optimized kernel

ﬁlter we used Otsu method (Otsu, 1979) to compute

the global threshold for the image binarization. How-

ever, Otsu may not be the best choice for this given

problem so that we compared the results obtained by

Otsu with a more recent method, Modiﬁed Valley Em-

phasis (MVE)(Fan and Lei, 2012). Figure 4 presents

the average Jaccard index obtained for both Otsu and

Modiﬁed Valley Emphasis methods at each stage of

the proposed approach.

Results show that Modiﬁed Valley Emphasis

method performs better than Otsu in all steps of our

approach. However, the difference of performance is

the highest at the ﬁrst step (i.e., binarization), where

the difference is of ≈ 2.00%. This indicates that Mod-

iﬁed Valley Emphasis method is capable to achieve

VISAPP 2021 - 16th International Conference on Computer Vision Theory and Applications

444

a threshold value that is more suitable for the ﬁl-

tered image S and its content, even though the PSO

was trained using Otsu. As we execute the other

steps of our framework (leukocytes selection and bor-

der reﬁnement), this difference of performance de-

crease until only 0.40%. This is expected as these are

post-processing steps and they were proposed to com-

pensate small segmentation problems that could arise

from the initial step, such as the presence of noise or

a very smooth or poorly deﬁned leukocyte border.

To improve our analysis we compared our ap-

proach with the results obtained by other segmenta-

tion methods found in literature. Table 1 show that

our approach is capable to surpass all compared ap-

proaches, independent of the binarization approach

used. When we consider our best result (i.e., Modiﬁed

Valley Emphasis), our framework achieves an aver-

age result 1.10% superior than the best compared ap-

proach, thus corroborating the effectiveness of our ap-

proach to segment leukocytes disregarding their size,

shape and spatial distribution of the cells in the image.

Table 1: Jaccard index achieved by different approaches.

Method Jaccard index

Paper (Rezatoﬁghi et al., 2009) 83.20%

Paper (Madhloom et al., 2010) 55.90%

Paper (Mohamed and Far, 2012a) 85.40%

Paper (Mohamed and Far, 2012b) 80.60%

Paper (Mohamed et al., 2012) 79.70%

Paper (Tosta et al., 2015) 89.89%

Paper (Tareef et al., 2016) 85.10%

Paper (Tareef et al., 2017) 90.10%

Proposed approach (Otsu) 90.80%

Proposed approach (MVE) 91.20%

Figure 4: Average Jaccard index obtained for two differ-

ent binarization methods at each stage of the proposed ap-

proach.

6 CONCLUSION

In this paper we presented a methodology to detect

and segment nuclear structures in leukocytes. To ac-

complish that our methodology uses a PSO algorithm

to estimate an optimal kernel ﬁlter, which is applied

after the color deconvolution of the image, so that it is

capable to explore and combine local features in dif-

ferent stain channels. Evaluation using a set of 367

images containing leukocytes and other blood struc-

tures showed that the estimated kernel ﬁlter highlights

the structures composing the leukocyte so that sim-

ple thresholding techniques are able to perform im-

age segmentation with high accuracy, surpassing the

results of various compared approaches found in lit-

erature.

ACKNOWLEDGEMENTS

Andr

e R. Backes gratefully acknowledges the ﬁ-

nancial support of CNPq (Grant #301715/2018-1).

This study was ﬁnanced in part by the Coordenac¸

de Aperfeic¸oamento de Pessoal de N

ıvel Superior -

Brazil (CAPES) - Finance Code 001.

REFERENCES

Al-Dulaimi, K., Banks, J., Nguyen, K., Al-Sabaawi, A.,

Tomeo-Reyes, I., and Chandran, V. (2020). Segmen-

tation of white blood cell, nucleus and cytoplasm in

digital haematology microscope images: A review-

challenges, current and future potential techniques.

IEEE Reviews in Biomedical Engineering.

Banik, P. P., Saha, R., and Kim, K.-D. (2020). An automatic

nucleus segmentation and cnn model based classiﬁca-

tion method of white blood cell. Expert Systems with

Applications, 149:113211.

dos Anjos, A. and Shahbazkia, H. (2008). Bi-level image

thresholding - A fast method. In BIOSIGNALS (2),

pages 70–76. INSTICC - Institute for Systems and

Technologies of Information, Control and Communi-

cation.

Fan, J.-L. and Lei, B. (2012). A modiﬁed valley-emphasis

method for automatic thresholding. Pattern Recogni-

tion Letters, 33(6):703–708.

Ghose, S., Oliver, A., Mart

ı, R., Llad

o, X., Vilanova, J. C.,

Freixenet, J., Mitra, J., Sidib

e, D., and Meriaudeau,

F. (2012). A survey of prostate segmentation method-

ologies in ultrasound, magnetic resonance and com-

puted tomography images. Comput. Methods Pro-

grams Biomed, 108(1):262–287.

Kennedy, J. and Eberhart, R. (1995). Particle swarm opti-

mization. In Proceedings of ICNN’95 - International

Conference on Neural Networks, volume 4, pages

1942–1948 vol.4.

Unsupervised Segmentation of Leukocytes Images using Particle Swarm

445

Kutlu, H., Avci, E., and

Ozyurt, F. (2020). White blood

cells detection and classiﬁcation based on regional

convolutional neural networks. Medical hypotheses,

135:109472.

Madhloom, H. T., Kareem, S. A., Arifﬁn, H., Zaidan,

A. A., Alanazi, H. O., and Zaidan, B. B. (2010). An

automated white blood cell nucleus localization and

segmentation using image arithmetic and automatic

threshold. Journal of Applied Sciences, (10):959–966.

Mohamed, M., Far, B., and Guaily, A. (2012). An efﬁcient

technique for white blood cells nuclei automatic seg-

mentation. In SMC, pages 220–225. IEEE.

Mohamed, M. M. A. and Far, B. (2012a). A fast technique

for white blood cells nuclei automatic segmentation

based on gram-schmidt orthogonalization. In ICTAI,

pages 947–952. IEEE Computer Society.

Mohamed, M. M. A. and Far, B. H. (2012b). An enhanced

threshold based technique for white blood cells nuclei

automatic segmentation. In Healthcom, pages 202–

207. IEEE.

Ng, H. F. (2006). Automatic thresholding for defect detec-

tion. Pattern Recognition Letters, 27(14):1644–1649.

Otsu, N. (1979). A threshold selection method from gray-

level histograms. IEEE Transactions on Systems,

Man, and Cybernetics, 9(1):62–66.

Rezatoﬁghi, S. H., Soltanian-Zadeh, H., Shariﬁan, R., and

Zorooﬁ, R. A. (2009). A new approach to white blood

cell nucleus segmentation based on gram-schmidt or-

thogonalization. In ICDIP, pages 107–111. IEEE

Computer Society.

Ruifrok, A. C., Johnston, D. A., et al. (2001). Quantiﬁca-

tion of histochemical staining by color deconvolution.

Anal Quant Cytol Histol, 23(4):291–299.

Sezgin, M. and Sankur, B. (2004). Survey over image

thresholding techniques and quantitative performance

evaluation. Journal of Electronic Imaging, 13:13 – 13

– 20.

Tareef, A., 0001, Y. S., Cai, T. W., 0021, Y. W., Feng, D. D.,

and Chen, M. (2016). Automatic nuclei and cytoplasm

segmentation of leukocytes with color and texture-

based image enhancement. In ISBI, pages 935–938.

IEEE.

Tareef, A., 0001, Y. S., Feng, D., Chen, M., and Cai, W.

(2017). Automated multi-stage segmentation of white

blood cells via optimizing color processing. In ISBI,

pages 565–568. IEEE.

Tosta, T. A. A., de Abreu, A. F., Traven

A§olo, B. A. N.,

do Nascimento, M. Z., and Neves, L. A. (2015). Un-

supervised segmentation of leukocytes images using

thresholding neighborhood valley-emphasis. In 28th

IEEE International Symposium on Computer-Based

Medical Systems, CBMS 2015, Sao Carlos, Brazil,

June 22-25, 2015, pages 93–94. IEEE Computer So-

ciety.

Wang, C., 0004, J. S., 0003, Q. Z., and Ying, S. (2017).

Histopathological image classiﬁcation with bilinear

convolutional neural networks. In EMBC, pages

4050–4053. IEEE.

VISAPP 2021 - 16th International Conference on Computer Vision Theory and Applications

446