Detection and Identification of Neurons in Images of

Microscopic Brain Sections

Igor Gurevich, Artem Myagkov, Yuri Sidorov, Yulia Trusova and Vera Yashina

Dorodnicyn Computing Centre, Russian Academy of Sciences

40 Vavilov str., 119333 Moscow, Russian Federation

Abstract. This paper presents a new combined mathematical method, which

were proposed, implemented, and experimentally tested for extracting

information necessary for modeling and, in future, predicting Parkinson’s

disease. The method was developed for extraction “neurons” from microscopic

images of brain slices of experimental animals. Then it was adapted for

different types of initial data, because unfortunately the quality of initial images

depends on skills of the specialist who has done an experiment. Now the

method allows one to detect and identify as neurons a set of small informative

extended objects with well distinguished (by brightness) oval inclusions. The

result is a binary image of the contours of detected objects and their inclusions

and a list of characteristics calculated for each detected object. The method is

based on the joint application of image processing methods, methods of

mathematical morphology, methods of segmentation, and the methods of

classification of microscopic images. The method was applied to the following

areas of brain: the substantia nigra pars compacta and the arcuate nucleus of

hypothalamus.

1 Introduction

One of the most important problems of neurosciences is the development of

experimental models of socially important neurodegenerative diseases, in particular,

those associated with the death of dopaminergic (DAergic) neurons. The degeneration

of the latter in the human nigrostriatal system leads to the development of Parkinson’s

disease (PD) [2]. These models are designed for the development of new methods and

technologies for the diagnosis and treatment of such diseases. The models could be

developed much faster and would be economically more effective, with reduced time

and material expenses for morphological studies. The latter can be reached by the

automation and optimization of the methods for processing and analysis of

experimental data. In particular, the automation of the analysis of images of neurons

and their dendrons obtained from the microscope (MINs) makes it possible to reduce

material costs by an order and the time costs, by two orders [7].

In spite of the fact that interest to the problem of analysis of biomedical images,

including MINs, is being constantly growing, there have been very few successful

attempts to automatize the process (or its stages) (see, for example, [3, 9, 14]). The

detection and tracing of neurons in two dimensional microscopic images of brain

Gurevich I., Myagkov A., Sidorov Y., Trusova Y. and Yashina V..

Detection and Identiﬁcation of Neurons in Images of Microscopic Brain Sections.

DOI: 10.5220/0004396001270136

In Proceedings of the 4th International Workshop on Image Mining. Theory and Applications (IMTA-4-2013), pages 127-136

ISBN: 978-989-8565-50-1

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

slices is complicated by the following factors [9]: (a) MINs contain, in addition to

neurons, objects that are not neurons (particles of dirt, staining errors, tissue folds,

blood vessels, and other artifacts); (b) neurons in MINs may strongly differ from each

other both in size and shape; (c) neurons may be damaged during the preparation of

slices; this fact affects their shape and, hence, leads to a large number of cells of

different types on a slice; and (d) neurons may stick together or may be overlapped.

On the whole, one should admit that the concept of a neuron as a visual object is not

clearly defined, and there is no universal list of contextual, logical, geometrical,

qualitative, and quantitative conditions and characteristics that would allow one to

standardize the description/definition of this visual object.

As a rule, the detection and tracing of a neuron in histological microscopic images

involves the following main stages: preprocessing, segmentation, and classification.

The need for the preprocessing of a MIN is attributed to the presence of noise, low

resolution of a MIN, and the contrast non-uniformity due to error in staining the

slices. To improve the quality of MINs, one usually applies standard operations of

image processing such as smoothing, inverse convolution, morphological filtration,

and some other operations [12].

In the publications devoted to the automatic or semiautomatic detection of neurons

in MINs, one mainly uses, for segmentation, algorithms based on thresholding (for

example, [3, 10]), morphological operations (for example, [4]), Potts models (for

example, [13]), and watershed methods (for example, [15]) or active contour models

(for example, [5, 9]). The further classification is carried out, for example, with the

use of Bayesian procedures, principal component analysis, or machine learning

methods (for example, [1]).

An important stage in the analysis of the MINs of brain slices is the morphological

characterization of the detected neurons. In recent years, a wide set of specific

morphological parameters has been defined for the efficient mathematical

characterization of the morphology of neurons, including their nuclei and dendrons

(see, for example, [11]).

A survey of the methods and systems of analysis and recognition of MINs for

solving the problems of automation of diagnosis and the prediction of the clinical

course of neurodegenerative diseases has been published by the authors of the present

paper in [6].

The mathematical apparatus developed by the authors is based on the combined

application of the methods of mathematical theory of image analysis and

mathematical theory of pattern recognition, mathematical morphology, descriptive

image algebras, information theory, and the methods of mathematical statistics. The

methods developed allow one to efficiently detect and extract informative objects in

microscopic images of brain slices (under restrictions imposed on the shape, size,

topology, and the smoothness characteristics of the boundaries of objects [7, 8]).

2 Statement of the Problem

The problem consists in detecting the contours and calculating the morphofunctional

characteristics of neurons for constructing a PD model that represents differences

128

between the parameters of DAergic neurons in the test and control groups

(experimental animals from the test group were subjected to neurotoxin).

As a source of experimental data for constructing a PD model, we used digital

microscopic images of DAergic neurons and the fibers of brain slices of experimental

animals (with a resolution of 0.0117µm2/pixel2).

First of all, DAergic neurons are found on frontal serial slices of the substantia

nigra pars compacta (SNC) with a thickness of 20 µm (fig. 1, left figure). They

represent a key link in the regulation of motoric behavior. A progressive degeneration

of these neurons leads to the development of PD.

DAergic neurons represent dark oval cells with light nucleus. The shape of

DAergic neurons may be rather arbitrary; however, in many cases one can observe a

convex soma surrounding a nucleus. The mean diameter of DAergic neurons ranges

from 10 to 20 µm. The nuclei are of oval shape, with a minimum

diameter of 6–12

µm and a maximum diameter of 9–15 µm. The relative size of the body (soma) and

the nucleus vary strongly (one may observe either a very

thin soma around the

nucleus or a relatively small nucleus compared with the area of the soma). MINs

contain a large number of fibers around a cell, which

may mask the cell, forming an

intense background around a neuron.

In addition to the factors, listed in the Introduction, that impede the processing of

MINs, one should take into account that (a) MINs contain regions of brain with a

different number of neurons per unit area, and (b) microscopic images of different

brain slices differ strongly in contrast and brightness.

DAergic neurons are found on frontal serial slices of the arcuate nucleus of

hypothalamus (AN) with a thickness of 20 µm (fig. 1, right figure). The DAergic

neurons in this part of brain are the same as in SNC area, but the initial images of

slices have significant differences: 1) the background of AN images is brighter than

the background of SNC images; 2) neurons located in AN slices can have no visible

cores; 3) average number of AN neurons per slice is in several times smaller than

there are in SNC area; 4) neurons located in AN slices are distributed rarely than in

SNC slices and mutual contacts between neurons in AN area are practically excluded.

Fig. 1. Examples of images of neurons and their dendrons obtained from the microscope: a)

SNC area (left figure); b) AN area (right figure).

129

3 Automated Detection and Identification of Neurons in

Microscopic Images of Stained Brain Slices

The theoretical basis of the development of a new combined method for the analysis

of MINs is the descriptive approach to image analysis and understanding [8].

According to this approach, a mathematical method for the analysis of MINs is

represented as a special algorithmic scheme.

The method is designed for detecting a set of small informative extended objects

with well-distinguished (by brightness) oval inclusions in microscopic images of

frontal slices of the SNC and AN and calculating their morphometric characteristics.

The result is a binary image of the contours of selected objects and a list of

characteristics calculated for each detected object. The method is based on the

combined application of image processing methods, methods of mathematical

morphology, methods of segmentation, and methods of classification of microscopic

images.

The main feature of the method is that, after preprocessing of images, an iterative

analysis of microscopic images is carried out with a view to distinguishing various

classes of objects, that involves five procedures: (1) objects different from neurons are

eliminated at the stage of application of the classifier; (2) a special class of objects-

stuck-together neurons- is distinguished after the classification; the analysis of these

neurons reduces to the construction of boundaries between two neurons; (3)

segmentation ensures the separation of “good” neurons (neurons with regular shape);

(4) analysis of neurons with thin soma whose boundary has been partially erased and

the nucleus is in contact with the background; and (5) elimination of the dendrons of

neurons.

The combined method involves the following main steps: (1) preprocessing of

MINs: (1.1) filtration of MINs by an unsharp mask, (1.2) normalization of MINs,

(1.3) morphological closing by reconstruction, and (1.4) binarization of MINs; (2)

analysis of MINs: (2.1) segmentation of MINs, (2.2) classification of detected objects,

(2.3) tracing neurons, (2.4) tracing the boundaries of neurons, (2.5) cutting off the

dendrons of the neurons, and (2.6) construction of the boundaries of detected neurons;

and (3) morphological characterization of neurons.

The algorithmic scheme for analysis of AN slices is almost the same: 1) the step

1.4 of MINs binarization is applied with other values of parameters; 2) the step 2.2 of

object classification is based on another method of learning classifier.

The algorithmic scheme that implements the detection and identification of objects

involves the following stages:

1. Preprocessing of MINs:

1.1) Filtration of MINs by an unsharp mask: the goal of this procedure is to

increase the sharpness of the MINs, which allows one to increase the accuracy of

tracing closely located neurons at further stages; the input data is a color MIN, and the

output data is a sharper color MIN.

A copy of the initial image is subjected to blurring (standard Gaussian blurring). If

the difference between the mask and the original exceeds a certain

threshold, the

images are subtracted. The threshold is needed to avoid undesirable details such as

noise in a digital image. The method ends with a pixel-by-pixel addition of the initial

and obtained images.

130

Unsharp masking increases the local contrast of an image in the regions that

initially contained sharp variations in color gradation; this allows one to keep the

boundaries between neurons.

1.2) Normalization: the aim of this procedure is a transition to a grayscale

representation of MINs to normalize the difference in the staining of preparations; the

input data are a color MIN with increased sharpness, and the output data is a

grayscale MIN.

The formula for the transition from a color to a grayscale MIN is standard, because

all three channels of the original image carry equal information necessary for the

further operation of the method. To pass from a color to a grayscale image, one

should apply the following formula for calculating the gray level at every point of this

image: gray= 0.299*R+ 0.587*G+0.114*B.

1.3) Filtration of MINs with the use of morphological closing by reconstruction;

the aim of this procedure is to suppress dark noise objects that are smaller than

neurons; the input data is a color MIN, and the output data is a grayscale MIN.

The applying of a morphological filter is motivated by the fact that the initial MINs

contain noisy objects that neither belong to the objects of the background nor

represent the goal objects. Noise in the images is produced due to the parts of neurons

(a part of the neuron shell) that do not completely fall into the slice, or due to the

terminals that fall into the slice. The noise objects in the image distort the results of

classification; moreover, they may affect the shape of the selected objects in case of

superimposition.

For the morphological processing of images, we chose a filter “closing by

reconstruction” because of the applying of this filter preserves the boundaries of the

objects subjected to filtration.

After the morphological filtration, large objects of the foreground that can easily be

detected against the background remain on the grayscale image. However, large noisy

objects may also exist among the remaining objects. This problem is solved at the

subsequent steps of the method.

1.4) Binarization; the aim of this procedure is a transition from a grayscale MIN to

a binary MIN followed by the segmentation of the objects; the input data is the

processed grayscale MIN, and the output data is the binary MIN with the goal objects,

which will be checked for membership in the “neuron” class.

The binarization of an image is performed by a thresholding algorithm with

adaptive threshold. The adaptive threshold is chosen by Otsu’s method [12]. After

that a threshold rule is applied to the grayscale image: all pixels of the original image

whose brightness function is above the threshold are assigned a brightness value of

255, and the remaining pixels are assigned a value of 0.

The binarization of AN images is performed by a thresholding algorithm with

adaptive threshold multiplied by some normalizing constant, because of the brightness

of the background. The constant (0.74) was found during the experiments.

The pixels with a brightness value of 0 on the binary image obtained are

considered to be neuron like objects, while the pixels with brightness 255 are assumed

to belong to the background.

2. Analysis of MINs:

2.1.2) Elimination of objects according to the shape and size of neurons; the aim of

this procedure is to eliminate redundant objects by their size (a) from above with a

very high threshold, in order to remove large areas that are certainly not neurons,

131

while retaining fragments corresponding to stuck together neurons, and (b) from

below with a small lower threshold, in order to remove undoubted noise; the input

data are the extracted connected components, and the output data is a shortened list of

connected components.

2.1.3) Preparation of areas with extracted objects for classification; the aim of this

procedure is the construction of the minimal bounding square for each connected

component; the input data is a binary MIN and the list of connected components, and

the output data is the list of minimal bounding squares.

2.2) Classification of extracted objects; the aim of this procedure is the distribution

of detected objects into two classes (“neurons” and “other objects”); the input data is

the list of squares that contain connected components, and the output data are two lists

of squares (the class of “neurons” and the class of “other objects”).

The problem of recognition of neurons is solved by a classifier based on the

calculation of the distances from some special points of objects (the search of these

points is done during classifier teaching process) to the objects’ edge in fixed

direction and on the features based on such distances.

The classifier is applied with various resolutions to square fragments of a MIN that

contain connected region chosen at the previous step. This approach allows one to

determine the presence of a single or several neurons in a single connected region.

The accuracy of the trained classifier on a test sample was 5% (for AN - 7%) -

error of the first type (neurons assigned to the class of “other objects”) or 15% (for

AN - 20%) - error of the second type (“non-neurons” identified as “neurons”). The

accuracy of the classifier can be improved by increasing the learning sample and by

bootstrap estimates to choose background objects.

2.3) Tracing “neurons”:

2.3.1) Elimination of “other objects”; the aim of this procedure is to remove

connected components corresponding to “other objects” according to the

classification performed; the input data are the minimum size squares bounding “other

objects” and the processed connected components, and the output data is a shortened

list of connected components.

All the connected components are eliminated according to the list of minimum size

squares bounding “other objects”.

2.3.2) Checking the number of extracted “neurons” in a connected component; the

aim of this procedure is to calculate the number of neurons in one connected

component; the input data are the minimum size squares bounding neurons and the

shortened list of connected components, and the output data is the number of

extracted neurons in each connected component.

2.4) Tracing the boundaries of neurons:

2.4.1) Splitting stuck-together neurons; the aim of this procedure is the

construction of a boundary between stuck-together neurons; the input data is the

number of extracted neurons in each connected component and the binary MIN, and

the output data is a new list of connected components.

When a connected component contains several neurons, stuck-together neurons are

separated. The separation was proceed as follows. One “non-neuron” connected

component suitable in size is taken. The amount of “white color” regions completely

covering by connected component is calculated in the next step. If the amount of such

regions is more than one than the median perpendiculars for line connected its centers

132

conducted between these regions. These perpendiculars will be the part of new

neurons boundaries.

After the boundary of new candidate for “neuron” is highlighted the minimum size

square bounding it and the classifier is running.

2.4.2) The construction of the minimum convex hull around a connected

component; the aim of this procedure is to construct the minimum convex hull, which

allows one to detect both “good” neurons and neurons with thin soma and vanishes

after preprocessing of a MIN; the input data is the shortened list of connected

components, and the output data are the minimum convex hulls of the connected

components.

2.4.3) Obtaining a traced boundary of a connected region; the aim of this procedure

is to obtain the boundary of neurons; the input data is a new list of connected

components, and the output data are the preliminary boundaries of neurons.

2.4.4) Checking the coincidence of the exact boundary and the minimal convex

hull; the aim of this procedure is to obtain the exact boundaries of neurons: (2.4.4.1)

when a region of the convex hull differs substantially from the traced boundary of a

connected component, the region of the convex hull is taken as the boundary of a

neuron; (2.4.4.2) when the region of the convex hull and the traced boundary either

coincide or differ insignificantly, the boundary of the connected component is taken

as the boundary of a neuron; the input data are the preliminary boundaries of neurons

and the minimal convex hulls, and the output data are the exact boundaries of

neurons.

2.5) Cutting off the dendrons of neurons; the aim of this procedure is to eliminate

redundant parts of objects identified as “neurons”; the input data are the exact

boundaries of neurons, and the output data are the exact boundaries of neurons

without dendrons.

2.6) Construction of the boundaries of the nuclei of extracted neurons; the aim of

this procedure is to remove the pixels belonging to the connected region from the

domain enclosed inside the boundary; the remaining pixels belong to the nucleus by

definition; the input data are new exact boundaries of neurons and a new list of

connected components, and the output data are the exact boundaries of nuclei.

3. Morphological characterization of neurons.

3.1) Determination of the necessary feature space; the aim of this procedure is to

determine a parametric model for the characterization of neurons. Biological experts

consider the mean brightness, perimeter, area, form factor, optical density, and

amount as the main parameters of the model.

3.2) The characteristics are calculated for individual neurons and their averaged

values by slices and series of slices; the input data is a binary MIN with the

boundaries of objects and the original colored MIN, and the output data are the values

of features and parametric models of neurons.

4 Experimental Testing of the Method Developed

The method of analysis of MINs developed is convergent, stable with respect to small

variations in the initial data, and has quadratic computational complexity. The method

is software implemented and is used for the automation, filling, and analysis of

133

preclinical models of PD by experimental data at the Kol’tsov Institute of

Developmental Biology, Russian Academy of Sciences (Moscow, Russian

Federation).

Experimental testing was carried out on 58 brain slices of the same animal. The

accuracy of the results was estimated by verifying whether the distributions of neuron

characteristics detected automatically coincide with those detected manually by the

Kolmogorov–Smirnov criterion (the zero hypothesis).

The accuracy of the results was also estimated by comparing the mean

multifunctional characteristics obtained under the automatic and manual tracing of

neurons on randomly chosen brain slices (we took five brain slices). The averaged

values of the characteristics of objects traced in the automatic and manual modes are

shown in Table 1 (for SNC case) and in Table 2 (for AN case). These tables present

also the correlation between the characteristics calculated in the manual and automatic

modes. The results confirm that the automatic tracing of neurons is close to the

manual tracing: the difference in the brightness and the area of the selected region is

within admissible limits, while the difference in the perimeters and the shape factors

of objects is attributed to the fact that it is very difficult to draw the exact boundary

manually.

During the experimental investigations, we confirmed the following characteristics

of the algorithms developed. 1. The algorithms guarantee the analysis and recognition

of the images of neurons on two dimensional brain slices of experimental animals. 2.

According to the statistical estimate of the Kolmogorov–Smirnov criterion, the

accuracy of the automatic analysis of neuron images is comparable with the accuracy

of visual analysis of neuron images, which is carried out when studying PD without

computer-aided analysis of images.

Table 1. Comparison of the characteristics of objects detected automatically and manually in

SNC.

Manual detection

Image Mean brightness

(0...255)

Area (mm

) Perimeter

(mm)

Shape factor

1 149,7 2,355 5,09 -0,06

2 145,9 3,046 6,08 -0,02

3 149,8 2,815 5,77 -0,03

4 146,4 2,756 5,31 -0,02

5 150,0 2,493 5,23 -0,02

Automatic detection

1 149,1 2,403 5,42 -0,05

2 145,2 3,166 6,69 -0,03

3 148,1 2,848 5,66 -0,03

4 146,6 2,813 5,11 -0,04

5 148,4 2,508 5,37 -0,03

Correlation between the manual and automatic modes

0,95 0,99 0,87 0,78

134

Table 2. Comparison of the characteristics of objects detected automatically and manually in

AN.

Manual detection

Image Mean brightness

(0...255)

Area (mm

) Perimeter

(mm)

Shape factor

1 122,1 2,239 4,79 -0,06

2 112,7 3,691 4,32 -0,03

3 117,0 2,512 6,15 -0,03

4 123,4 3,254 4,67 -0,02

5 122,7 2,417 4,30 -0,04

Automatic detection

1 119,7 2,432 4,89 -0,05

2 111,0 4,843 5,13 -0,03

3 118,7 2,712 6,15 -0,02

4 123,1 3,373 4,47 -0,04

5 121,1 2,313 4,51 -0,04

Correlation between the manual and automatic modes

0,93 0,95 0,86 0,75

5 Conclusions

We have developed a new mathematical method and a software designed for

extracting and characterizing DAergic neurons in microscopic images of brain slices

in the SNC and in AN. The problem of detecting neurons in the images of SNC and

AN slices has been posed by scientists from the Laboratory of Hormonal Regulations

at the Kol’tsov Institute of Developmental Biology, Russian Academy of Sciences.

They also supplied images for processing and analysis. The experimental testing of

the method has been carried out jointly. At present, we study the possibility of

improving the mathematical and functional characteristics of the method by using

combinatorial recognition algorithms that admit the input of spatial information and

essentially employ contextual and logical conditions and constraints.

Acknowledgements

This work was supported in part by the Russian Foundation for Basic Research

(projects nos. 11-01-00990, 12-07-31123) and by the Presidium of the Russian

Academy of Sciences within the program “Fundamental Science to Medicine” as well

as within the program “Information, Control, and Intelligent Technologies and

Systems” (project no. 204).

135

References

1. Alavi, A., Cavanagh, B., Tuxworth, G., Meedeniya, A., Mackay-sim, A.: Automated

classification of dopaminergic neurons in the rodent brain. In: Proceedings of International

Joint Conference on Neural Networks, Atlanta, Georgia, USA, June 14-19 (2009) 81-88

2. Albin, R. L., Young, А. В., Penney, J. В.: The functional anatomy of basal ganglia

disorders. In: Trends Neurosci., 12 (1989) 366-375

3. Benali, A., Leefken, I., Eysel, U. T., Weiler, E.: A computerized image analysis system for

quantitative analysis of cells in histological brain sections. In: Journal of Neuroscience

Methods, 125(1-2) (2003) 33-43

4. Dias, A. V., Picanço-Diniz, C. W., Lotufo, R. A., Garçon, S.: Morphological segmentation

of neurons and axon terminals in rat visual cortex. In: Perception, 27 (ECVP Abstract

Supplement) (1998)

5. Fok, Y.-L., Chan, J., Chin, R. T.: Automated Analysis of Nerve-Cell Images Using Active

Contour Models. In: IEEE Transactions on Medical Imaging, 15(3) (1996) 353-368

6. Gurevich, I., Beloozerov, V., Myagkov, A., Sidorov, Yu., Trusova, Yu.: Systems of Neuron

Image Recognition for Solving Problems of Automated Diagnoses of Neurodegenerative

Diseases. In: Pattern Recognition and Image Analysis: Advances in Mathematical Theory

and Applications, 21(3) (2011) 392-397

7. Gurevich, I. B., Kozina, E. A., Myagkov, A. A., Ugryumov, M. V., Yashina, V. V.:

Automating Extraction and Analysis of Dopaminergic Axon Terminals in Images of Frontal

Slices of the Striatum. In: Pattern Recognition and Image Analysis: Advances in

Mathematical Theory and Applications, 20(3) (2010) 349-359

8. Gurevich, I. B., Yashina, V. V.: Descriptive Approach to Image Analysis: Image Models.

In: Pattern Recognition and Image Analysis: Advances in Mathematical Theory and

Applications, 18(4) (2008) 518-541

9. Inglis, A., Cruz, L., Roe, D. L., Stanley, H. E., Rosene, D. L., Urbanc, B.: Automated

identification of neurons and their locations. In: Journal of Microscopy, 230(3) (2008) 339-

352

10. Masseroli, M., Bollea, A., Forloni, G.: Quantitative morphology and shape classification of

neurons by computerized image analysis. In: Computer Methods and Programs in

Biomedicine, 41(2) (1993) 89-99

11. Meijering, E.: Neuron Tracing in Perspective. In: Cytometry. Part A, 77(7) (2010) 693-704

12. Otsu, N.: A Threshold Selection Method from Gray-Level Histograms. In: IEEE

Transactions on systems, man, and cybernetics, 9(1) (1975): 62-66

13. Peng, S., Urbanc, B., Cruz, L., Hyman, B.T., Stanley, H.E.: Neuron Recognition by Parallel

Potts Segmentation. In: Proceedings of the National Academy of Sciences of the United

States of America, 100(7) (2003) 3847-3852.

14. Sciarabba, M., Serrao, G., Bauer, D., Arnaboldi, F., Borghese, N. A.: Automatic Detection

of Neurons in Large Cortical Slices. In: Journal of Neuroscience Methods, 182 (2009) 123-

140

15. Wang, Y.-Y., Sun, Y.-N., Chou-Ching, K., Ju, M.-S.: Nerve Cell Segmentation via Multi-

Scale Gradient Watershed Hierarchies. In: Conference proceedings: Annual International

Conference of the IEEE Engineering in Medicine and Biology Society (2006) 6698-6701

136