NUCLEI IMAGES ANALYSIS

Technology, Diagnostic Features and Experimental Study

I. Gurevich, D. Murashov

Dorodnicyn Computing Centre of the Russian Academy of Sciences, 40, Vavilov str., Moscow, GSP-1, 119991, Russia

O. Salvetti

Institute of Information Science and Technologies, CNR, 1, Via G. Moruzzi, Pisa, 56124, Italy

H. Niemann

University of Erlangen-Nuremberg, Informatik 5, Martensstraße 3,91058 Erlangen, Germany

Keywords: Medical image mining, image analysis, information technology for automated diagnostics, morphologic

analysis of the cytological slides.

Abstract: The information technology for automated morphologic analysis of the cytological slides, taken from

patients with the lymphatic system tumours, was developed. The main contributions of the paper are the

technology, the set of features for representation of nuclei images in pattern recognition problems

(automated diagnostics), and experimental study of the technology and the features informativeness. The

main components of the technology are: acquisition of cytological slides, method for segmentation of nuclei

in the cytological slides, synthesis of the feature based nuclei description for subsequent classification,

nuclei image analysis based on pattern recognition and scale-space techniques. The experiments confirmed

efficiency of the developed technology. The discussion of the obtained results is given. The developed

technology is implemented in the software system.

1 INTRODUCTION

The technology for morphologic analysis of

cytological slides of patient with tumors of the

lymphatic system was developed. The technology is

intended for computer-aided diagnostic which is

based on the properties of tumor cells.

The main contributions of the paper are the

technology, the set of features for representation of

nuclei images in pattern recognition problems

(automated diagnostics), and experimental study of

the technology and the features informativeness.

The main components of the technology are

described and results of experimental testing are

discussed. The task of describing formally the

lymphoid tumour cells is challenging. Even high-

skilled experts could provide different conclusions

on the same specimen. Morphometric approach

allows one to enhance precision of the diagnosis by

utilizing quantitative characteristics of a specimen.

Further advancement could be achieved using

technologies based on the information extracted by

pattern recognition and other modern mathematical

techniques for image analysis.

In this paper the information technology for

automated morphologic analysis of the cytological

slides taken from patients with the lymphatic system

tumors, is presented. The technology includes the set

of methods and techniques developed by the authors

recently (author’s paper). All of the elements of

technology are naturally defined by the technology

of specimen slides acquiring and specimen image

analysis processes.

The main phases of the developed technology are

as follows: 1) design of the archive of tumour cell

images of patients with high- and low-grade

lymphoid tumours, 2) image preprocessing for

removing differences in illumination and colour; 3)

selection and calculation of features reflecting cell

nuclei peculiarities, 4) qualitative and statistical

204

Gurevich I., Murashov D., Salvetti O. and Niemann H. (2007).

NUCLEI IMAGES ANALYSIS - Technology, Diagnostic Features and Experimental Study.

In Proceedings of the Second International Conference on Computer Vision Theory and Applications, pages 204-210

DOI: 10.5220/0002071002040210

 SciTePress

analysis of features and evaluation of its information

weights, 5) composing feature description of a

patient based on the results of nuclei cluster

analysis; 6) classification (diagnostics) of slides.

For the design of the technology the following

problems are solved: a) selection of main features of

the nuclei in cytological slides; b) development of

techniques for nuclei segmentation in slides; c)

statistical analysis of feature description (descriptive

statistics, correlation, factor and cluster analysis); d)

selecting of diagnostic “mathematical” features; e)

experimental testing of efficiency of different

classifiers.

2 MORPHOLOGY OF THE

SPECIMENS

Primary diagnostics of lymphoid tumors is based on

morphological descriptions of cells and relations

between different cell populations. The presence of

large lymphoid cells points to diffuse large cell

lymphoma or high-grade transformation of indolent

lymphoma, while predominance of small

lymphocyte like cells indicates indolent lymphoma.

Quantitative analysis of cells was based on

calculating intensity, textural and structural features

of slides that characterized the size and the shape of

objects. A very important factor for diagnostic is the

visual structure of chromatin in cell nuclei.

3 DIGITAL IMAGING

Footprints of lymphoid tissues were Romanovski-

Giemsa stained and photographed with digital

camera mounted on Leica DMRB microscope using

PlanApo 100/1.3 objective. The equivalent size of a

pixel was 0,0036 µ2; 24-bit color images were

stored in TIFF format, and analyzed as described

below.

4 SEGMENTATION SPECIMEN

IMAGES

Automated hematological diagnostics using

specimen images deals with evaluation of values of

a set of diagnostically important features which are

used for nuclei classification. The segmentation of

nuclei images is necessary for further nuclei image

analysis, in particular, for calculation of feature

values.

The two-level method for automated

segmentation of nuclei images based on the active

contour model was developed (Murashov, 2005).

The main features of the method are: a) the rough

segmentation is achieved using the blue component

of the image taking into account the properties of the

stain; the precise correction is made using the green

component; b) the modified Gaussian filter based on

the heat equation with heat source or sink is

implemented. The technique is successfully

implemented for segmenting cytological specimen

images.

5 NUCLEI IMAGES ANALYSIS

The problem of diagnostic-oriented feature

description of specimens is very actual for many

years (see, for example (Rodenacker, 1995,

Rodenacker and Bengtsson, 2003), but it didn’t get

final solution till now. In order to select and describe

diagnostically valuable attributes we have created an

archive of pictures of footprints of biopsies from

patients with 4 diagnoses: non-tumor (reactive)

enlargement of lymph nodes (RL), B-cell chronic

lymphatic leukemia (CLL), large cell transformation

of B-cell chronic lymphatic leukemia (TRCLL), de

novo large-cell lymphoma (LS) (Jaffe, 2001).

Two methods are used for composing nuclei

feature description and obtaining diagnostic criteria:

1) selecting a large set of features using image

analysis techniques and extracting minimal feature

set for classification of nuclei with suitable accuracy

(Churakova, Gurevich, et.al., 2003); 2) selecting a

set of features used by experts in visual analysis (the

features are invariant in some degree to slides

quality and parameters of image acquisition process)

(Gurevich, Murashov, 2004).

5.1 The Nuclei Descriptions in the

Form of a Vector of Numerical

Features

The diagnostic features of tumor cells are as follows:

a) nuclei size optical density; b) the shape of nuclei

(round, elliptic, folded), c) presence of

invaginations, d) textural characteristics of the

chromatin (condensed/dispersed, the chromatin

fibril’s diameter, presence of granules of condensed

chromatin and size of these granules), e) presence of

nucleoli. We provided formal equivalents for some

NUCLEI IMAGES ANALYSIS - Technology, Diagnostic Features and Experimental Study

205

of the above features. Thus, the following 47

features were chosen to describe nuclei morphology

(Churakova, Gurevich, et.al., 2003): an area of

nucleus in pixels, 4 statistical features calculated on

nucleus brightness histogram (average, dispersion,

3rd and 4th central moments); 16 granulometric

features of nucleus; 26 features calculated on the

Fourier-spectrum of nucleus. Further steps included

statistical and factor analysis of features and cluster

analysis of nuclei on appropriate sets of features.

Statistical and qualitative analysis included:

feature correlations, feature distribution, feature

histograms and their moments; feature “robustness”

to variations of nuclei geometry. The analysis

allowed excluding unstable and high-correlated

features. The feature values distribution was

estimated by Shapiro-Wilks W-test. It turned out

that the distributions for majority of features are not

normal once.

After calculation of descriptive nonparametric

statistics (medians and quartiles) it is appeared that

values of some textural features are grouped within

3 separated areas, i.e. the considered nuclei are

divided into 3 types. In case of CLL, cytological

slides contain mainly “mature” nuclei. In slides

corresponding to TRCLL one can find “mature”

nuclei as well as “transformed” nuclei. LS is

characterized by a larger percent of transformed”

nuclei. Since well-known for experts distribution of

“mature” and “transformed” nuclei over diagnosis

coincides with its distribution over obtained types, it

is possible to conclude that these nuclei types

correspond to “mature” and “transformed” nuclei.

Cluster sets were obtained by application of

FOREL algorithm (Zagoruiko, 1999) to different

sets of features. The sets of clusters were evaluated

using different criteria: number and size of clusters

in each set (large clusters contained more than 400

nucleus), total percent of all nuclei belonging to

large clusters, the character of nuclei distribution in

large clusters. In the considered problem a taxonomy

with a few large clusters accumulating the main part

of nuclei is more preferable than a taxonomy with a

lot of small clusters where nuclei distribution over

clusters is uniform.

A new method to create feature description of a

patient was suggested. On the basis of cluster

analysis results a patient is described by a new type

of features - percentage of a patient’s nuclei

belonging to large clusters of the taxonomy.

Experiments showed that good classification results

can be obtained in such feature space.

Factor analysis was conducted for reducing of

the feature space. It was applied to several data sets:

a) all 8702 available nuclei; b) the sample

corresponding to 4 different diagnosis; c) samples

for each patient. Three techniques were used:

principal factor, centroid and maximum-likelihood.

The combination of the Kaizer criterion and the

scree-test was used to determine a number of factors,

while varimax-rotate strategy was used to calculate

factor loadings. Each method yielded the same

number of factors for each data set. The mean factor

loadings were calculated for three data sets. As a

result, the same factors were discovered. Similarity

of factors was confirmed by presence of high factor

loadings on the same features and, accordingly, by

presence of low factor loadings on the rest features.

Then, features with high factor loadings (its absolute

values exceed some threshold) were determined for

each obtained factor.

It is important that selection of the same factors

for data sets corresponding to different diagnoses

gives the corresponding sets of features with high

factor loadings differing substantially. It means that

for different diseases considered in our study there

are different significant features. It also appeared

that there are unique significant features for some

diseases that are not significant for the other

diseases.

We developed a new diagnostic procedure based

on factor patterns designed for considered deceases.

The pattern represents high-loadings-feature

distribution over factors extracted from the sample

corresponding to certain decease. Such distributions

are different for different deceases. If the factor

pattern of a new patient coincides with the pattern of

a particular decease, we could consider that this

patient has such decease.

The cluster analysis of nuclei was done on the

base of the results of factor analysis. For initial

minimization of the feature space Spearman R-

statistic was used. Further the features with high

loadings were selected from obtained set. It

appeared that the taxonomy structure (amount of

large clusters and their nuclei proportions) is

determined by only 3 features. The values of these

features are concentrated into separated areas. The

rest of features influences only on total amount and

size of clusters, however the nuclei proportions in

large clusters are the same (see Table 1, “+” means

the presence of explicit partition to malignant and

non-malignant groups).

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5.2 Scale-Space Features

Two basic approaches to analysis of a chromatin

constitution are known (Rodenacker and Bengtsson,

2003): a) structural - the chromatin distribution is

considered as a local arrangement of small objects of

varying intensity and the intensity features of dark

and bright particles are evaluated; b) textural - based

on statistical characteristics of chromatin

arrangement and on analysis of regularities of

chromatin structure. The second method uses gray

level dependency matrices (Weyn, et.al., 1999), co-

occurrence, run-length features, rice-field operators,

watersheds (topological methods) (Rodenacker,

1993, Rodenacker and Bengtsson, 2003, Weyn,

et.al., 1999, Young, et.al., 1986), heterogeneity,

clumpiness, margination, radius of particles (Young,

et.al., 1986) (the Mayall/Young features), and

invariant features (polynomial invariants).

Two methods for nuclei image analysis are

developed on the basis of the scale-space approach

(Lindeberg, 1994). The approach of Gaussian scale-

space is the most relevant for cell nuclei image

analysis because it provides the invariance with

respect to shift, rotation, scaling, and linear

transformations of intensity. It decreases the

sensitivity of the analysis to microscope focusing.

The first method deals with the analysis of the

nuclei image intensity features (Gurevich and

Murashov, 2004). A diagnostically important

criterion is obtained - a total amount of spatial

extrema in scale-space generated by an image of a

cell nucleus. The main idea of the analysis is based

on the assumption that significant structures in scale-

space are likely corresponding to significant

structures in the image. The chromatin structure in

the nucleus image is represented by a grey-level

relief containing connected bright and dark regions

(peaks and valleys) corresponding to chromatin

filaments, furrows and centers. Each of these regions

contains at least one intensity extremum. The

developed method is based on the topological

properties of iso-intensity manifolds in the

neighborhoods of spatial extrema and allows to

evaluate approximate amount of chromatin particles

represented in the image as grey-level blobs.

The second method is a textural one – an integral

feature characterizing the chromatin pattern is

involved. The quantitative feature is defined using

the second moment matrix which is defined as a

covariance matrix of the stochastic value

characterizing the nuclei image. The steps of the

method are: a) calculation of the module gradient

image for emphasizing the chromatin pattern; b)

selection of the scale parameter σ corresponding to

the most informative image in the scale-space (the

most informative image corresponds to the maximal

feature value, see (Lindeberg, 1994) ); c) calculation

features values via 2d moment matrix

(trace

, determinant

det

, quotient

det /

qtr

). The nuclei images of

different patient groups prepared for textural

analysis are shown in Figure 1.

(a)

(b)

(c)

(d)

Figure 1: Nuclei images prepared for textural analysis:

a) CLL; b) RL, c) TRCLL, and d) LS.

Table 1: Cluster analysis of different sets of features.

Set No Features Total amount

of clusters

Amount of

large clusters

(>400 nuclei)

Percentage

of all

nuclei

Partition to

malign. and

non-malign.

% of malign.

nuclei in non-

malig. clusters

% of non-

malig. nuclei in

malign. clusters

3 19,22,27 23 5 83,5 + 8,5 33,6

4 1,19,22,27 27 5 84,1 + 8,5 33,2

5 1,2,19,22,27 49 5 77,9 + 3,6 35,8

6 1,2,19,22,27,3,6,10 91 5 79,5 + 3,8 34,1

8 18 features 136 5 82 + 5,7 32,7

NUCLEI IMAGES ANALYSIS - Technology, Diagnostic Features and Experimental Study

207

The value of scale parameter σ is obtained

experimentally and corresponds to

maximum.

For the considered class of images σ is equal to

3,25.

The experimental chart displaying the

distribution of cell nuclei is presented in Figure 2,

where n is the total amount of spatial extrema, s is

an area of a nucleus.

The diagnostic features were tested for: 1)

selection of a textural feature providing the best

classification result; 2) testing efficiency of the

features for diagnosing; 3) testing the diagnostic

importance of the nuclei marked by experts and used

for training classifiers. In the experiments we used

slides from patients with diagnoses CLL, TrCLL,

LS, and RL.

The computing experiment includes two steps.

At the first step classifiers were trained, and the

three tasks pointed out above were carried out.

Training control sample included 829 nuclei taken

from 25 patients, control sample included 832

nuclei. At this step the diagnostically important

nuclei marked by experts in the specimen images

were used for training. The following classifiers

were applied: k-nearest neighbors, support vector

machine (SVM), Fisher liner discriminant,

multilayer perceptron, and committee algorithm

(Zhuravlev, et. al., 2003). For each nuclei image the

features s, n,

det

and

were calculated.

The suitable accuracy was obtained by k-nearest

neighbors and SVM classifiers. Committee classifier

increased the reliability of classification. The

classification accuracy in case of 2 nuclei classes

(non-malignant and malignant) exceeds 90%. In case

of 4 different diagnoses the accuracy is comparable

with the accuracy of expert estimates. The results of

the first step show that the selected feature set can be

used for description of nuclei chromatin structure

corresponding to the considered diseases.

At the second step the percentage of the nuclei of

different chromatin structure in the specimen images

was evaluated. At this step the slides taken from new

patients (different from those at step 1) were used.

Unlike the previous step all of the nuclei appeared in

the slides were analyzed. The feature values (s,

n) were calculated and the nuclei were classified

using the algorithms trained at step 1.

The results of the classification presented in

Tables 2 and 3 satisfy the existing morphologic

criteria mentioned in section 2. The disease is

considered as malignant by the experts if the amount

of malignant cells exceeds 20% of the total amount

of cells in specimens taken from patient. The

obtained results (see Tables 2, 3) allow one to make

the following conclusions.

Figure 2: Nuclei distribution in axes s, qμ, and n for 4

groups of diseases.

Table 2: The percentage of nuclei recognized as

“malignant” and “non-malignant” in the specimen images

corresponding to different diseases.

Amount of nuclei (%)

Diagnosis

Non-malignant Malignant

CLL 90 10

RL 76,5 23,5

TRCLL 62,3 37,7

LS 54,3 45,7

First, a few types of nuclei characterizing the

diseases under consideration can be outlined (we

deal with “CLL”, “RL”, “TRCLL”, and “LS” types;

“CLL” and “RL” form the “non-malignant” group,

“TRCLL” and “LS” form “malignant” group).

Second, in the slides corresponding to CLL non-

malignant nuclei are prevailing. Third, in the slides

corresponding to TRCLL the majority of malignant

nuclei are classified as TRCLL type. Also, in the

slides corresponding to LS the majority of malignant

nuclei are classified as LS type (see Table 3).

Fourth, in the RL slides nuclei partly (about 20%)

are classified as malignant due to enlarged cells, but

none of the types TRCLL or LS is dominating (see

Table 3). Fifth, the difference in feature values

reflecting the peculiarities of different types of

chromatin structure corresponding to different

diseases will provide the criteria for differential

diagnostics.

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208

Table 3: The percentage of different types of nuclei in the

specimen images corresponding to different diseases.

Amount of nuclei (%)

Diagnose Classifier

Non-

malign.*

TRCLL LS

k-nearest

neighbors

82 10 4

SVM 92 4 4

CLL

Committee 92 4 4

k-nearest

neighbors

74,5 11,8

13,

SVM 90,2 2,0 7,8

Committee 90,2 2,0 7,8

k-nearest

neighbors

47,2 39,6

13,

SVM 62,3 35,8 1,9

TRCLL

Committee 62,3 35,8 1,9

k-nearest

neighbors

50,8 4,3

45,

SVM 56,5 6,5

37,

Committee 56,5 6,5

37,

* - combined CLL and RL group.

6 CONCLUSIONS

The results of the computational experiments and

estimations of hematologists approve the efficiency

of the developed technology for automated

morphologic analysis of the lymphoid cells. It is

confirmed that the objective differentiation of nuclei

may be used for patients classifying according to the

main classification of lymphatic tumors. The

important problems were solved: a) the automated

technique for segmenting the lymphoid cell nuclei is

developed and implemented; b) a feature set for

nuclei description is formed; c) the procedures for

nuclei classification is developed; d) the method for

estimating the efficiency of the developed

procedures is proposed. The technology is

implemented in the specialized software system.

The next step of the research will be devoted to

adaptation of the developed technology for analysis

of histological slides: 1) the general analysis of

tissue structure (quasi-textural analysis of the slides

obtained under the low magnification of the

microscope); 2) the analysis of single cell images

obtained at maximal resolution.

ACKNOWLEDGEMENTS

This work was partially supported by the Russian

Foundation for Basic Research Grants 05-07-08000,

06-01-81009, 06-07-89203, by the project of the

Program of the Presidium of the Russian Academy

of Sciences "Fundamental Problems of Computer

Science and Information Technologies", by INTAS

Grant № 04-77-7067, and by Cooperative grant

“Image Analysis and Synthesis: Theoretical

Foundations and Prototypical Applications in

Medical Imaging” within agreement between Italian

National Research Council and Russian Academy of

Sciences.

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