Early Experiences in Mitotic Cells Recognition on HEp-2 Slides

P. Foggia∗, G. Percannella ∗, P. Soda∗∗, and M. Vento ∗

∗Dipartimento di Ingegneria dell’Informazione ed Ingegneria Elettrica, Universita di Salerno, Salerno, Italy

{pfoggia,pergen,mvento}@unisa.it∗∗

Integrated Research Centre, Universita Campus Bio-Medico di Roma, Roma, Italy

p.soda@ unicampus.it

Abstract

Indirect immunofluorescence (IIF) imaging is the rec-

ommended laboratory technique to detect autoantibodies in

patient serum, but it suffers from several issues limiting its

reliability and reproducibility. IIF slides are observed by

specialists at the fluorescence microscope, reporting fluo-

rescence intensity and staining pattern and looking for mi-

totic cells. Indeed, the presence of such cells is a key factor

to assess the correctness of slide preparation process and

the reported staining pattern. Therefore, the ability to de-

tect mitotic cells is needed to develop a complete computer-

aided-diagnosis system in IIF, which can support the spe-

cialists from image acquisition up to image classification.

Although recent research in IIF has been directed to im-

age acquisition, image segmentation, fluorescence intensity

classification and staining pattern recognition, no works

presented methods suited to classify such cells. Hence, this

paper presents an heterogeneous set of features used to de-

scribe the peculiarities of mitotic cells and then tests five

classifiers, belonging to different classification paradigms.

The approach has been evaluated on an annotated dataset

of mitotic cells. The measured performances are promising,

achieving a classification accuracy of 86.5 %.

1. Introduction

Indirect immunofluorescence (IIF) imaging is the rec-ommended laboratory technique to detect autoantibodies inpatient serum, which are at the basis of autoimmune disor-ders, a chronic inflammatory process involving connectivetissues and giving rise to connective tissue diseases (CTDs)[11, 18]. In patients with suspected CTDs, antinuclear au-toantibodies (ANAs) dispatched on the HEp-2 cell substrateare used as markers for disease detection [11]. HEp-2 slidesare observed by specialists at the fluorescence microscope,reporting fluorescence intensity and staining pattern, andlooking also for mitotic cells. Indeed, mitotic cells are

added to slides producers in order to guarantee the correct-ness of the tests and to help the doctors in decision on stain-ing pattern since peculiarities of mitosis cycle match withcertain staining patterns rather than others. Hence, suchcells permit to be confident with decisions and to assess thereports [16].

In IIF the availability of accurately performed and cor-rectly reported laboratory determinations is crucial for theclinicians, demanding for highly specialized personnel thatis not always available. Moreover, the readings in IIFare subjected to interobserver variability limiting the repro-ducibility of the method [2, 5]. In order to overcome suchlimitations, recent interests have been directed towards thedevelopment of computer-aided-diagnosis (CAD) systemssupporting IIF diagnostic procedure. Such works focus onimage acquisition [8, 20], image segmentation [10, 9, 16]and fluorescence intensity classification [14] as well asstaining pattern recognition [6, 7, 13, 16, 19].

It is worth noting that none of these papers discusses howto recognise the presence of mitotic cells in the image underconsideration. Since the development of CAD system in IIFcannot prescind from the recognition of mitotic cells, in thefollowing we present different features and classification ar-chitectures addressing the issue of mitotic cells recognition.Our proposal has been evaluated on an annotated dataset ofmitotic cells on which we carried out several tests.

2 Background and Motivations

2.1 Medical context

Current guidelines for ANA tests recommend the use ofHEp-2 substrate diluted at 1:80 titer and require to clas-sify both fluorescence intensity and staining pattern. Thisis a challenging task affecting the reliability of IIF diagno-sis [2, 5]. With regard to the classification of fluorescent in-tensity, the guidelines suggest scoring it semi-quantitativelyand independently by two physicians experts of IIF. Thescoring ranges from 0 up to 4+ relative to the intensity of

Figure 1. Examples of the homogenous, peripheral nuclear, speckled, nucleolar, cytoplasmic andcentromere staining patterns (left to right).

a negative and a positive control. The former allows the

physician to check the correctness of the preparation pro-

cess; the latter represents the auto-fluorescence level of the

slide under examination. To reduce the variability of mul-

tiple readings, it has been recently proposed to classify the

sample fluorescence intensity into three classes, named neg-ative, intermediate and positive, which maintain the clini-

cal significance of the IIF test and gets ground truth robust

enough to develop a classification system [18].

Positive HEp-2 samples may reveal different patterns of

fluorescent staining that are relevant to diagnostic purposes

(Figure 2). Although more than thirty different nuclear and

cytoplasm patterns could be identified [21], in the literature

they are classified into one of the following groups [19]:

(i) homogeneous: diffuse staining of the interphase nuclei

and staining of the chromatin of mitotic cells; (ii) periph-eral nuclear: solid staining, primarily around the outer re-

gion of the nucleus, with weaker staining toward the cen-

ter of the nucleus; (iii) speckled: a fine or coarse granular

nuclear staining of the interphase cell nuclei; (iv) nucleo-lar: large coarse speckled staining within the nucleus, less

than six in number per cell; (v) cytoplasmic: fine fluores-

cent fibres running the length of the cell; it is frequently

associated with other autoantibodies to give a mixed pat-

tern; (vi) centromere: several discrete speckles (∼ 40− 60)

distributed throughout the interphase nuclei and character-

istically found in the condensed nuclear chromatin during

mitosis as a bar of closely associated speckles. Sometimes

two concomitant staining patterns can be observed in the

same well. In these cases, further dilution and/or better fo-

cusing may help to recognize different overlapping staining.

Since IIF is a subjective, semi-quantitative method,

physicians act differently when the same sample is pre-

sented to them: some are more conservative and others

more liberal, depending mostly on their skills and back-

ground, giving rise to classification variability. Another sig-

nificant reason of uncertainty is the low contrast of border-

line and negative samples. In order to guarantee the correct-

ness of the test, producers add to the slides some mitotic

cells1

which give to medical doctors the confidence with

1Mitosis is the process by which a eukaryotic cell separates the chro-

mosomes in its cell nucleus into two identical sets in two nuclei.

their decisions.

Mitotic cells are important in IIF for two reasons. First,

we can be sure that the well has been correctly prepared if

we are able to detect at least one fluorescent mitotic cell.

Second, they provide information on the staining pattern

since they match with certain kind of patterns, including all

stainings of antigens with different distribution throughout

the cell cycle, such as midbody, CENP-F, mitotic splindle,

centriole/centrosome and NuMA staining [19]. Notice that

producers typically guarantee that a certain percentage of

mitosis appear in each slide, but give no information about

their location.

Mitotic cells of HEp-2 substrate may exhibit two fluores-

cent patterns. In the first, the cell body is fluorescent, while

the collapsed chromosomes mass located in the middle part

of the cell does not exhibit a fluorescent pattern, or it has a

weak fluorescence. This kind of mitotic pattern is named as

negative mitosis (panels A and B of Figure 2). In the second

pattern, we can observe the opposite situation, i.e. the cell

body is weakly or not fluorescent, while the chromosomes

mass is fluroscent. An example of this type of mitotic cell,

referred to as positive mitosis, is shown in panels C and D

of Figure 2. In both cases it is worth observing that col-

lapsed chromosomes mass has a circular or elliptic shape,

approximately.

2.2 Motivations

In IIF, the availability of accurately performed and cor-

rectly reported laboratory determinations is crucial for the

clinicians. The relevance of the issue is emphasized by

the increase in the incidence of autoimmune diseases ob-

served over the last years, partly attributable to both im-

proved diagnostic capabilities and growing awareness of

this clinical problem in general medicine. However, the

major disadvantages of IIF are: (i) the lack of resources

and adequately trained personnel [6, 18]; (ii) the low level

of standardization [17]; (iii) the inter and intralaboratory

variance [2, 6]; (iv) the lack of automatised procedures;

(v) the photobleaching effect, which bleaches significantly

in a few seconds biological tissues stained with fluorescent

dyes [22].

To date, the highest level of automation in IIF tests isthe preparation of slides with robotic devices performingdilution, dispensation and washing operations [1, 3]. Be-ing able to automatically determine the presence of autoan-tibodies in IIF would enable easier, faster and more reli-able tests. Hence, an evident medical demand is the devel-opment of a CAD system, which may support physician’sdecision and overcome current method limitations, improv-ing the standardisation level and reducing the variability ob-served within the same laboratory or different laboratories[2]. Recent research has developed CAD tools both for im-age acquisition [8, 20] and classification [7, 6, 9, 10, 13,14, 16, 19]. Most of such works recognize fluorescence in-tensity and/or staining patterns using information belongingto single cells in the image [6, 7, 13, 16, 19], applying im-age segmentation algorithms based on Otsu’s or watershedalgorithms [12] in conjunction with morphological process-ing [6, 9, 10, 13, 16].

However, a deep analysis of such works reveal that noneof them proposed any method for mitotic cells recognition,although some papers have presented approaches tailoredto staining pattern classification of single cell. It is worthnoticing that the ability to automatically detect mitotic cellswould permit to develop systems for automatic slide anal-ysis in IIF, from image acquisition up to image classifica-tion. Indeed, the presence of these cells is a key factor usedby medical doctors to assess, on the one hand, the correct-ness of slide preparation process and, on the other hand,the reported staining pattern, as described in section 2.1.Therefore, the detection of such cells can help IIF acquisi-tion phase of a CAD system since, for instance, the systemshould scan the well under examination, look for mitoticcells and then acquire for classification purpose those partsof well containing mitotic cells. Moreover, if mitotic cellsare absent, such a system should warn the specialist thatsomething was wrong during the preparation of the slide.With reference to the classification step of a CAD system,the recognition of mitotic cells should reinforce the analysisof staining pattern.

On this basis, this paper proposes a novel approach torecognise mitotic cells thus addressing this lack found inthe literature and opening the chance to develop automaticsystem for IIF image analysis.

3. Features extraction and selection

This section describes how we have selected the hetero-geneous set of descriptors used to represent the peculiari-ties of mitotic cells. They are morphological descriptors in-spired by the peculiarities of cells at hand and texture mea-sures, e.g. first and second order histograms, rectangle fea-tures and local binary pattern (LBPs).

The first set of features is composed by morphological

DB

A C

Figure 2. Examples and stylised representa-tions of positive (panels A-B) and negative(panels C-D) mitosis. Light and dark greenrepresent high and low fluorescence.

descriptors, which are based on the observation that mitoticcells may be fluorescent inside or outside the chromosomesmass (section 2.1). We compute features that, on the oneside, look for elliptic shape and, on the other side, analysethe fluorescence intensity inside the cells. In order to catchinformation on the elliptic shape of chromosome mass, be-ing either a positive or negative mitosis, we fit inside thecell body the following model representing a multivariateGaussian distribution:

z = a + m · exp

�−

�x− µx

σx

�2

−�

y − µy

σy

�2�

(1)

where a is an offset, m is a scale factor, µx and µy arethe mean values of the distribution along the x and y axes,respectively, σx and σy are x and y standard deviations, re-spectively. We also measure the coefficient of determinationr2. Since the chromosomes mass has a particular orienta-tion inside the cells and the cells are randomly oriented inthe well, we perform several fittings, each one rotating thecell of 10 degrees. Such a rotation should permit to achievethe best fit of the model to the biological area of interest.For each fitting, the features set is composed of the fol-lowing parameters: a, m, µx, σx, µy, σy and r2. It is worthnoting that some orientations have a good fitting, whereasprevious and next orientations return very low fitting de-terminations. Furthermore, in these cases we notice largevariation between the estimated parameters of multivariateGaussian distribution. These situations may correspond tofalse mitosis: indeed, in case of true mitosis, we notice thatthe fitting determinations are high for several consecutiveorientations as well as the parameters values are not so dif-ferent. On this basis, we derive other sets of features basedon the mean values of model parameters measured on con-secutive orientations given by 20, 40, 60 and 80 degrees.

In order to catch information on fluorescent intensity in-side the cell body, we define other two morphological de-scriptors as follows. For each cell, we apply the Otsu’s al-gorithm [12] and then found the number of connected re-gions whose mean value of fluorescence intensity is largerthan Otsu’s threshold. Indeed, in mitotic cell we should

find only one region, either the chromosome mass in caseof positive mitosis or the other part of cell body in caseof negative mitosis. Cells with other patterns should havemore than one region. For instance, cells with nucleolarpattern, which have large coarse speckled staining withinthe nucleus, should have at least two regions with a fluores-cence intensity larger than Otsu’s threshold. Furthermore,we compute also the fraction of cell area with a fluorescenceintensity larger than Otsu’s threshold.

The second set of features consists of texture measuresthat have been successfully used in previous works on HEp-2 classification [13, 14]. They are related to statistical andspectral measures. The former have been extracted bothfrom intensity histogram and from grey level co-occurencematrix by means of computing their statistical moments,e.g. skewness, kurtosis, energy, entropy, to name a few.The latter have been computed from Fourier transform (FT),Wavelet transform and Zernike moments.

We also computed rectangle features, which are simpledescriptors very effective in the field of face detection [15].Indeed, similarly to face detection, we are interested in de-tecting regions inside an object. A rectangle feature is com-puted as a weighted sum of pixels inside adjacent rectanglesin the image. Defining as RecSumi(r) the weighted sumof pixels into a rectangular region r according to the maskωi [15], rectangle features RecFeat are given by

RecFeat =�

i

RecSumi(r) (2)

Rectangle features owe their success, besides their effec-tiveness, to the possibility of being efficiently implementedthrough an intermediate representation of the image calledintegral image.

LBPs assign to each pixel of the image a label obtainedcomparing it with its neighbourhood matrix. Different typesof LBP operators can be computed varying both the pixel ar-rangement and the number of neighbours, respectively. Inthis work we use the standard LBP operator obtained apply-ing a 3x3 neighbourhood square matrix [23]. More specifi-cally, defining as gc the value of the pixel with coordinates(xc, yc), and with gp the value of its p-th neighbour, the LBPoperator is defined as follows:

LBP (xc, yc) =7�

p=0

2ps(gp − gc) (3)

where

s(x) =

�1 if x ≥ 00 otherwise

The basic LBP operator is neither robust to changes inspatial resolution nor to texture rotations. For this rea-son we extract also the circular LBP, which is computed

using bilinear interpolated circular neighbourhoods, sincethey demonstrated robustness to grayscale variations and torotated textures [24].

The search of the best discriminant subset has been per-formed running greedy stepwise and best first searches,both forward and backward, and using cross-validation.Then, it has been refined by an exhaustive search, takinginto account the dimensionality of the dataset and of thefeatures set. At the end of this process we individuated15 descriptors belonging to morphological descriptors, sta-tistical moments of intensity histogram and grey level co-occurence matrix, and circular LBP (table 1). Morpholog-ical descriptors are: mean values of multivariate Gaussiandistribution averaging out consecutive orientations given by60 degrees, the standard deviations and the fraction of cellarea with a fluorescent intensity larger than Otsu’s thresh-old. From the intensity histogram we selected the meanand the skewness: the former is a measure of average inten-sity, whereas the letter estimates the asymmetry of the his-togram. From the grey level co-occurence matrix describ-ing image texture we selected the covariance and the inertia,which specify the energy spread about the matrix diagonal.Circular LBP features describe image texture with referenceto circular information. In this case we selected statisticalmeasures from the derived second order histograms suchas autocorrelation, covariance, energy around the absolutemaximum.

4. Experimental evaluation

Since, to our knowledge, there are not referencedatabases of IIF images publicly available, we populateda database of annotated mitotic cells, using slides of HEp-2 substrate at the fixed dilution of 1:80, as recommendedby the guidelines [2]. Specialists take HEp-2 imageswith an acquisition unit consisting of the fluorescence mi-croscope (40-fold magnification) coupled with a 50 Wmercury vapour lamp and with a digital camera. Thecamera has a CCD with squared pixel of equal side to6.45µm. The images have a resolution of 1388x1038pixels, a colour depth of 24 bits and they are stored inbitmap format. Specialists manually segment and anno-tate each cell at a workstation monitor since at the flu-orescence microscope is not possible to observe one cellat a time, and report data on fluorescence intensity, pat-tern and mitosis phase. The used database consists of 126cells, 63 mitotic cells and 63 no mitotic cells, which there-fore exhibit one of staining patterns reported in section2.1. Dataset is available at http://mivia.unisa.it/databases/db_database/biomedical/.

We investigate the performance that could be achievedin mitotic cell recognition when popular classifiers belong-ing to different paradigms are used. In this respect, we

Table 1. Selected features.

Category of features Features

Morphological descriptors Mean of multivariate Gaussian distribution averaging out consecutive orientations at 60 degrees,

standard deviations and the fraction of cell area with a fluorescent intensity larger than Otsu’s threshold

Intensity histogram Mean, skewness

Grey-level co-occurrence matrix Covariance, inertia

Circular LBP Autocorrelation, covariance, energy around the absolute maximum of the output image second-order histogram

test a Multi-Layer Perceptron (MLP) as a neural network, a

Naıve Bayes classifier as a bayesian classifier, a a k-Nearest

Neighbour as a statistical classifier (kNN), a Support Vector

Machine (SVM) as a kernel machine, and AdaBoost as an

ensemble of classifiers. In the following we briefly report

the experimental set-up of each classification architecture:

MLP We use a MLP with one hidden layer. The number

of neurons in the input layer is given by the number

of the features, whereas the number of neurons in the

output layer is equal to two. Several preliminary tests

have been carried out to determine the best configura-

tion of the MLP in terms of number of neurons in the

hidden layer: specifically, configurations from 1 up to

100 neurons were tested.

Naıve Bayes The Naıve Bayes does not demand for spe-

cific set-up.

kNN We explore different values of k in the interval [1; 9]to determine the best configuration.

SVM In the SVM experiments, we use a Radial Basis

Function kernel. We perform a grid search to deter-

mine the best setup of the SVM in terms of Gaus-

sian width, σ, and of regularisation parameter, C. The

searches of σ and C have been carried out in the inter-

vals [0.01; 30] and [0.1; 60], respectively.

AdaBoost For AdaBoost we use decision stumps for base

hypotheses, exploring different numbers of iterations

(in the interval [10; 100]) to determine the best config-

uration.

Tests were performed using leave-one-out (LOO) ap-

proach, using the training set for the parameters selection

phase reported above. Afterwards we report only the results

achieved using the best classifier configuration, averaging

over the different runs.

To estimate the recognition performance we measure the

following parameters: global accuracy (Acc), true posi-

tive rate or sensitivity (Sens), true negative rate or speci-

ficity (Spec), precision, and F-measure. We also draw ROC

curves and use the area-under-curve (AUC) for cell clas-

sifiers comparison [4]. Table 2 show the mean values of

performance of the five classification paradigms estimated

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Figure 3. Classifiers ROC curves.

using the LOO approach, where the values of the param-

eters for the best configuration of each classifier are given

in parentheses. In case of MLP the number represents the

neurons in the hidden layers, in case of k-NN the number is

the value of k, in case of SVM the values are σ and C, and

in case of AdaBoost the value corresponds the number of

iterations. Figure 3 shows the corresponding ROC curves,

built using the best configuration of each classifier.

Table 2. Performance of mitotic cells classifi-

cation.

Classifier Acc (%) Sens (%) Spec (%) Precision (%) F-measure (%) AUCMLP (60) 82.37 80.95 69.84 72.86 58.98 0.856

Naıve Bayes 80.16 87.30 73.02 76.39 66.69 0.868

k-NN (1) 86.51 85.71 87.30 87.10 74.65 0.865

SVM (20,1) 81.09 82.54 80.95 81.25 67.06 0.802

AdaBoost (70) 85.00 87.30 82.54 83.33 72.75 0.901

The results show that both k-NN and AdaBoost provides

the largest performance. While the former achieves the

largest accuracy (86.5%), the latter has the best value AUC

and the largest sensitivity, whose value is important for the

needs of real applications. These results are promising and

should open the way to the development of a comprehen-

sive CAD in IIF, since the ability to recognise mitotic cells

is important both for image acquisition and staining pattern

recognition.

5. Conclusions

In this paper we have presented an approach for mitoticcells classification in HEp-2 slides. Despite recent researchhas focused on CAD system in IIF, classification of mitoticcells is an issues never discussed in the literature up to now.Such cells play a key role in IIF image classification sincespecialists look at them to be confident with their decisions.Being able to recognise such cells would permit to assessthe correctness of slide preparation process and the reportedstaining pattern. We have investigate an heterogeneous setof features to represent the peculiarities of mitotic cells andtested five different classifiers achieving promising results.

Future works are directed towards the test of this ap-proach on a larger dataset and then the integration of thesystem for mitotic cells recognition with systems for IIFimage acquisition and staining pattern classification. Thegoal is a comprehensive CAD supporting all phases of IIFdiagnosis.

Acknowledgments

This work has been supported by “Regione Campania”within the project “Classification of ImmunofluorescenceImages for the Diagnosis of Autoimmune Diseases” and by“Regione Lazio” under the Programme “Intese con i Parchiscientifici e tecnologici del Lazio per favorire l’innovazionee il trasferimento tecnologico alle PMI laziali” (art. 182comma 4, L.R. 28 Aprile 2006 n. 4 lettera c)). The authorsthank DAS s.r.l. for acquisition equipment and A. Rigonand L. Onofri for IIF images acquisition and annotation.

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