Fractal analysis of elastographic images for automatic detection of diffuse diseases of salivary glands: preliminary results.  
Jump to Full Text  
MedLine Citation:

PMID: 23762183 Owner: NLM Status: InDataReview 
Abstract/OtherAbstract:

The geometry of some medical images of tissues, obtained by elastography and ultrasonography, is characterized in terms of complexity parameters such as the fractal dimension (FD). It is well known that in any image there are very subtle details that are not easily detectable by the human eye. However, in many cases like medical imaging diagnosis, these details are very important since they might contain some hidden information about the possible existence of certain pathological lesions like tissue degeneration, inflammation, or tumors. Therefore, an automatic method of analysis could be an expedient tool for physicians to give a faultless diagnosis. The fractal analysis is of great importance in relation to a quantitative evaluation of "realtime" elastography, a procedure considered to be operator dependent in the current clinical practice. Mathematical analysis reveals significant discrepancies among normal and pathological image patterns. The main objective of our work is to demonstrate the clinical utility of this procedure on an ultrasound image corresponding to a submandibular diffuse pathology. 
Authors:

Alexandru Florin Badea; Monica Lupsor Platon; Maria Crisan; Carlo Cattani; Iulia Badea; Gaetano Pierro; Gianpaolo Sannino; Grigore Baciut 
Related Documents
:

18003293  Livewirebased 3d segmentation method. 8402523  3d reconstruction of biological objects from sequential image planesapplied on cerebr... 17679063  Spect versus planar gated blood pool imaging for left ventricular evaluation. 
Publication Detail:

Type: Journal Article Date: 20130516 
Journal Detail:

Title: Computational and mathematical methods in medicine Volume: 2013 ISSN: 17486718 ISO Abbreviation: Comput Math Methods Med Publication Date: 2013 
Date Detail:

Created Date: 20130613 Completed Date:  Revised Date:  
Medline Journal Info:

Nlm Unique ID: 101277751 Medline TA: Comput Math Methods Med Country: United States 
Other Details:

Languages: eng Pagination: 347238 Citation Subset: IM 
Affiliation:

Department of CranioMaxilloFacial Surgery, University of Medicine and Pharmacy "Iuliu Haţieganu", Cardinal Hossu Street 37, 400 029 ClujNapoca, Romania. 
Export Citation:

APA/MLA Format Download EndNote Download BibTex 
MeSH Terms  
Descriptor/Qualifier:

Full Text  
Journal Information Journal ID (nlmta): Comput Math Methods Med Journal ID (isoabbrev): Comput Math Methods Med Journal ID (publisherid): CMMM ISSN: 1748670X ISSN: 17486718 Publisher: Hindawi Publishing Corporation 
Article Information Download PDF Copyright © 2013 Alexandru Florin Badea et al. openaccess: Received Day: 10 Month: 3 Year: 2013 Accepted Day: 12 Month: 4 Year: 2013 Print publication date: Year: 2013 Electronic publication date: Day: 16 Month: 5 Year: 2013 Volume: 2013Elocation ID: 347238 PubMed Id: 23762183 ID: 3671291 DOI: 10.1155/2013/347238 
Fractal Analysis of Elastographic Images for Automatic Detection of Diffuse Diseases of Salivary Glands: Preliminary Results  
Alexandru Florin Badea^{1}  
Monica Lupsor Platon^{2}  
Maria Crisan^{3}*  
Carlo Cattani^{4}  
Iulia Badea^{5}  
Gaetano Pierro^{6}  
Gianpaolo Sannino^{7}  
Grigore Baciut^{1}  
^{1}Department of CranioMaxilloFacial Surgery, University of Medicine and Pharmacy “Iuliu Haţieganu”, Cardinal Hossu Street 37, 400 029 ClujNapoca, Romania 

^{2}Department of Clinical Imaging, University of Medicine and Pharmacy “Iuliu Haţieganu”, Croitorilor Street 1921, 400 162 ClujNapoca, Romania 

^{3}Department of Histology, Pasteur 56 University of Medicine and Pharmacy “Iuliu Haţieganu”, 400 349 ClujNapoca, Romania 

^{4}Department of Mathematics, University of Salerno, Via Ponte Don Melillo, 84084 Fisciano, Italy 

^{5}Department of Dental Prevention, University of Medicine Pharmacy “Iuliu Haţieganu”, Victor Babes Street, 400 012 ClujNapoca, Romania 

^{6}Department of System Biology, Phd School, University of Salerno, Via Ponte Don Melillo, 84084 Fisciano, Italy 

^{7}Department of Oral Health, University of Rome Tor Vergata, Viale Oxford, 00100 Rome, Italy 

Correspondence: *Maria Crisan: mcrisan7@yahoo.com [other] Academic Editor: Shengyong Chen 
In some recent papers [^{1}–^{4}], the fractal nature of nucleotide distribution in DNA has been investigated in order to classify and compare DNA sequences and to single out some particularities in the nucleotide distribution, sometimes in order to be used as markers for the existence of certain pathologies [^{5}–^{9}]. Almost all these papers are motivated by the hypothesis that changes in the fractal dimension might be taken as markers for the existence of pathologies since it is universally accepted nowadays that bioactivity and the biological systems are based on some fractal nature organization [^{3}, ^{4}, ^{10}–^{13}]. From a mathematical point of view, this could be explained by the fact that the larger the number of interacting individuals, the more complex the corresponding system of interactions is. These hidden rules that lead to this complex fractal topology could be some simple recursive rules, typical of any fractallike structure, which usually requires a large number of recursions in order to fill the space.
In recent years, many papers [^{3}–^{6}, ^{9}, ^{14}, ^{15}] have investigated the multifractality of biological signals such as DNA and the possible influence of the fractal geometry on the functionality of DNA from a biologicalchemical point of view. Almost all these papers concerning the multifractality of biological signals are based on the hypothesis that the functionality and the evolution of tissues/cells/DNA are related to and measured by the evolving fractal geometry (complexity), so that malfunctions and pathologies can be linked with the degeneracy of the geometry during its evolution time [^{5}–^{7}, ^{16}–^{18}].
From a mathematical point of view, a fractal is a geometric object mainly characterized by the noninteger dimension and selfsimilarity so that a typical pattern repeats itself cyclically at different scales. A more complex definition of a fractal is based on the four properties: selfsimilarity, fine structure, irregularities, and noninteger dimension [^{19}]. The fractal dimension is a parameter which measures the relationship between the geometric unsmoothness of the object and its underlying metric space. Since it is a noninteger value, it is usually taken as a measure of the unsmoothness, thus being improperly related to the level of complexity or disorder. Fractality has been observed and measured in several fields of specialization in biology, similar to those in pathology and cancer models [^{20}, ^{21}]. However, only recently have been made some attempts to investigate the structural importance of the “fractal nature” of the DNA. It has been observed in some recent papers that the higher FD corresponds to the higher information complexity and thus to the evolution towards a pathological state [^{3}, ^{4}].
In the following, we will analyse the particularities of the fractal dimension focused on the pathological aspects of some tissues, more specific those belonging to a submandibular gland. For the first time, the FD is computed on images obtained by the new technology of elastographic imaging focused on this salivary gland.
A 55yearold woman presented herself in the emergency room of the MaxiloFacial Surgery Department for acute pain and enlargement of the left submandibular gland and was selected for ultrasound evaluation. The ultrasound examination was performed using the ACUSON S2000 (Siemens) ultrasound equipment, where the ARFI (acoustic radiation force impulse) and realtime elastography technique were implemented. The ACUSON S2000 is a powerful, noninvasive, ultrasound based device, which gives very accurate B mode and Doppler images of tissues. It has been profitably used for the analysis of abdominal, breast, cardiac, obstetrical, and gynaecological imaging and also for small parts such as thyroid and vascular imaging.
The patient was placed laying down and facing up, while the transducer was placed in contact with skin on the area of the right and then the left submandibular gland successively. The shear wave velocity within the right and the left submandibular gland parenchyma was determined for each submandibular gland (in meters/second); colour elastographic images were also acquired. A colour map was used where stiff tissues were coded in blue and soft tissues in red. These images were studied afterwards for fractal analysis.
Figure 1 represents a 2D ultrasound evaluation in a “grey scale” mode, and Figure 2 represents a combination between 2D ultrasonography and “colour flow map” (CFM, or “duplex sonography”). From the first viewing, we can easily detect, by its enlargement, the gland swelling (Figure 1) and the hyper vascular pattern (Figure 2), both of these pieces of information being highly suggestive for the inflammation diagnosis. The combined clinical and ultrasound evaluation is conclusive for an acute inflammation of the submandibular gland. Figures 3 and 5 (obtained on the right salivary swollen gland) and Figures 4 and 6 (obtained on the left side, normal gland) represent elastography in quantitative mode (Figures 3 and 4), color mode (Figures 5 and 6) (ARFI tissue imaging mapping color).
Concerning the fractal analysis in this section, we will summarize some definitions already given in [^{3}].
As a measure of the complexity and fractal geometry, we will consider only the fractal dimension and regression analysis (Shannon information entropy, lacunarity, and succolarity will be considered in a forthcoming paper).
Let p_{x}(n) be the probability to find the value x at the position n, the fractal dimension is given by [^{3}, ^{4}, ^{22}]
(1)
D=1N∑n=2Nlog px(n)log n. 
(2)
μr(k)=∑s=kk+r−1vsh∗ 
(3)
pr(k)=1r∑s=kk+r−1vsh∗. 
(4)
{pr(k)}k=1,…,N, 
Concerning the fractal dimension of the elastographic images, as given by (1), we can see (Table 1) that the highest FD is shown by Figure 7 and lowest by the Figure 8.
The images were analyzed in 8bit using the Image J software (tools box counting).
The figures are referred to a patient with an acute inflammation of the submandibular gland.
Figure 1 shows a 2D ultrasound evaluation in grey scale. Figure 2 shows a 2D colour flow map evaluation (duplex sonography). Figures 3 and 4 were obtained by using the method elastography ARFISiemens, and they display quantitative information. The values of fractal dimension (FD) of Figures 3 and 4 are similar, and it is not possible to distinguish between pathological (Figure 3) and normal (Figure 4) states. The Figures 5 and 6 are obtained through elastography ARFI with qualitative information. From the fractal analysis by the box counting method, we have noticed that the value of Fd is lower (1.650) in Figure 5 (pathological condition) than Figure 6 (normal state). Figures 7 (pathological state) and 8 (normal state) were obtained through real time elastography.
From the computations, we can note that the higher value of Fd belongs to the pathological state (1.907), thus suggesting that the Fd increases during the evolution of the pathology (increasing degeneracy). Therefore, from Fd, analysis is possible to distinguish between pathological state and normal state of tissues by real time elastography because it is the better method to discriminate Fd values in a clear, sharp way.
Elastography is an ultrasonographic technique which appreciates tissue stiffness either by evaluating a colour map [^{23}, ^{24}] or by quantifying the shear wave velocity generated by the transmission of an acoustic pressure into the parenchyma (ARFI technique) [^{25}–^{27}]. In the first situation, the visualization of the tissue stiffness implies a “realtime” representation of the colour mode elastographic images overlapped on the conventional grayscale images, each value (from 1 to 255) being attached to a color. The system uses a color map (redgreenblue) in which stiff tissues are coded in dark blue, intermediate ones in shades of green, softer tissues in yellow and the softest in red, but the color scale may be reversed in relation to how the equipment is calibrated. Depending on the color and with the help of a special software, several elasticity scores that correlate with the degree of tissue stiffness can be calculated [^{23}]. Numerous clinical applications using these procedures were introduced into routine practice, many of them being focused on the detection of tumoral tissue in breast, thyroid, and prostate.
In the last years, a new elastographic method, based on the ARFI technique (acoustic radiation force impulse imaging), is available on modern ultrasound equipment. The ARFI technique consists in a mechanical stimulation of the tissue on which it is applied by the transmission of a short time acoustic wave (<1 ms) in a region of interest, determined by the examiner, perpendicular on the direction of the pressure waves, and leading to a micronic scale “dislocation” of the tissues. Therefore, in contrast with the usual ultrasonographic examination, where the sound waves have an axial orientation, the shear waves do not interact directly with the transducer. Furthermore, the shear waves are attenuated 10.000 faster than the conventional ultrasound waves and therefore need a higher sensitivity in order to be measured [^{25}–^{29}]. Detection waves, which are simultaneously generated, have a much lower intensity than the pressure acoustic wave (1 : 1000). The moment when the detection waves interact with the shear waves represents the time passed from the moment the shear waves were generated until they crossed the region of interest. The shear waves are registered in different locations at various moments and thus the shear wave velocity is automatically calculated, the stiffer the organ the higher the velocity of the shear waves. Therefore, the shear wave velocity is actually considered to be an intrinsic feature of the tissue [^{25}–^{29}]. In current clinical practice, the same transducer is used both to generate the pressure acoustic wave and to register the tissue dislocation. Since the technique is implemented in the ultrasound equipment through software changes, B mode ultrasound examination, color Doppler interrogation and ARFI images are all possible on the same machine [^{30}].
Currently, elastography is widely studied in relation to different clinical applications: breast, thyroid, liver, colon and prostate [^{29}, ^{31}–^{36}]. The application in salivary gland pathology has been singularly considered at least in our literature database. Some reports present the utility of elastography in a better delineation of tumors of these glands. Applications on diffuse disease are few although the importance of this kind of pathology is important! Inflammations of salivary glands occur in many conditions and the incidence is significant. There is a need for accurate diagnosis, staging, and prognosis. The occurrence of complications is also very important! Elastography represents a “virtual” way of palpation reproductive and with possibility of quantification.
Although there are several improvements, the main limitation of elastography is the dependency of the procedure to the operator's experience. This characteristic makes elastography vulnerable with a quite high amount of variations of elastographic results and interpretation. A more accurate analysis of the elastographic picture based on very precise evaluation as fractal analysis is an obvious step forward. In our preliminary study, the difference between normal and pathologic submandibular tissue using the fractal analysis was demonstrated. Because of the very new technologies accessible in practice as elastography is, and because of the mathematical instruments available as fractal analysis of the pictures, we are encouraged to believe that the ultrasound procedure might become operator independent and more confident for subtle diagnosis. However, a higher number of pictures coming from different patients with diffuse diseases in different stages of evolution are needed.
In this work, the multifractality of 2D and elastographic images of diffuse pathological states in submandibular glands has been investigated. The corresponding FD has been computed and has shown that images with the highest FD correspond to the existence of pathology. The extension of this study with incrementing the number of ultrasound images and patients is needed to demonstrate the practical utility of this procedure.
The authors declare that there is no conflict of interests concerning the validity of this research with respect to some possible financial gain.
References
1.  Anh V,ZhiMin G,ShunChao L. Fractals in DNA sequence analysisChinese PhysicsYear: 2002111213131318 
2.  Buldyrev SV,Dokholyan NV,Goldberger AL,et al. Analysis of DNA sequences using methods of statistical physicsPhysica AYear: 19982491–44304382s2.00031996849 
3.  Cattani C. Fractals and hidden symmetries in DNAMathematical Problems in EngineeringYear: 2010201031 pages2s2.077954525762507056 
4.  Pierro G. Sequence complexity of Chromosome 3 in Caenorhabditis elegansAdvances in BioinformaticsYear: 2012201212 pages287486 
5.  Bedin V,Adam RL,de Sá BCS,Landman G,Metze K. Fractal dimension of chromatin is an independent prognostic factor for survival in melanomaBMC CancerYear: 201010, article 2602s2.077953073626 
6.  Ferro DP,Falconi MA,Adam RL,et al. Fractal characteristics of MayGrünwaldGiemsa stained chromatin are independent prognostic factors for survival in multiple myelomaPLoS ONEYear: 2011662s2.079959279611e20706 
7.  Metze K,Adam RL,Ferreira RC. Robust variables in texture analysisPathologyYear: 20104266096102s2.07795692100520854091 
8.  Metze K. Fractal characteristics of May Grunwald Giemsa stained chromatin are independent prognostic factors for survival in multiple myelomaPLoS OneYear: 20116618 
9.  Dey P,Banik T. Fractal dimension of chromatin texture of squamous intraepithelial lesions of cervixDiagnostic CytopathologyYear: 201240215215422246932 
10.  Voss RF. Evolution of longrange fractal correlations and 1/f noise in DNA base sequencesPhysical Review LettersYear: 19926825380538082s2.0000020487810045801 
11.  Voss RF. Longrange fractal correlations in DNA introns and exonsFractalsYear: 19922116 
12.  ChatzidimitriouDreismann CA,Larhammar D. Longrange correlations in DNANatureYear: 199336164092122132s2.000274596848423849 
13.  Fukushima A,Kinouchi M,Kanaya S,Kudo Y,Ikemura T. Statistical analysis of genomic information: longrange correlation in DNA sequencesGenome InformaticsYear: 2000113153316 
14.  Li M. Fractal time seriesa tutorial reviewMathematical Problems in EngineeringYear: 2010201026 pages2s2.077951489276157264 
15.  Li M,Zhao W. Quantitatively investigating locally weak stationarity of modified multifractional Gaussian noisePhysica AYear: 20123912462686278 
16.  D’Anselmi F,Valerio M,Cucina A,et al. Metabolism and cell shape in cancer: a fractal analysisInternational Journal of Biochemistry and Cell BiologyYear: 2011437105210582s2.07995810935220460170 
17.  Pantic I,HarhajiTrajkovic L,Pantovic A,Milosevic NT,Trajkovic V. Changes in fractal dimension and lacunarity as early markers of UVinduced apoptosisJournal of Theoretical BiologyYear: 201230321879222763132 
18.  Vasilescu C,Giza DE,Petrisor P,Dobrescu R,Popescu I,Herlea V. Morphometrical differences between resectable and nonresectable pancreatic cancer: a fractal analysisHepatogastroentologyYear: 201259113284288 
19.  Mandelbrot B. The Fractal Geometry of NatureYear: 1982New York, NY, USAW. H. Freeman 
20.  Baish JW,Jain RK. Fractals and cancerCancer ResearchYear: 20006014368336882s2.0003466089010919633 
21.  Cross SS. Fractals in pathologyJournal of PathologyYear: 199718211189227334 
22.  Backes AR,Bruno OM. Segmentação de texturas por análise de complexidadeJournal of Computer ScienceYear: 2006518795 
23.  FriedrichRust M,Ong MF,Herrmann E,et al. Realtime elastography for noninvasive assessment of liver fibrosis in chronic viral hepatitisAmerican Journal of RoentgenologyYear: 200718837587642s2.03384720222417312065 
24.  Sǎftoui A,Gheonea DI,Ciurea T. Hue histogram analysis of realtime elastography images for noninvasive assessment of liver fibrosisAmerican Journal of RoentgenologyYear: 20071894W232W2332s2.03534885317517885039 
25.  Dumont D,Behler RH,Nichols TC,Merricks EP,Gallippi CM. ARFI imaging for noninvasive material characterization of atherosclerosisUltrasound in Medicine and BiologyYear: 20063211170317112s2.03375091351917112956 
26.  Zhai L,Palmeri ML,Bouchard RR,Nightingale RW,Nightingale KR. An integrated indenterARFI imaging system for tissue stiffness quantificationUltrasonic ImagingYear: 2008302951112s2.05274908457818939611 
27.  Behler RH,Nichols TC,Zhu H,Merricks EP,Gallippi CM. ARFI imaging for noninvasive material characterization of atherosclerosis part II: toward in vivo characterizationUltrasound in Medicine and BiologyYear: 20093522782952s2.05834909787319026483 
28.  Nightingale K,Soo MS,Nightingale R,Trahey G. Acoustic radiation force impulse imaging: in vivo demonstration of clinical feasibilityUltrasound in Medicine and BiologyYear: 20022822272352s2.0003621869511937286 
29.  Lupsor M,Badea R,Stefanescu H,et al. Performance of a new elastographic method (ARFI technology) compared to unidimensional transient elastography in the noninvasive assessment of chronic hepatitis C. Preliminary resultsJournal of Gastrointestinal and Liver DiseasesYear: 20091833033102s2.07394912350319795024 
30.  Fahey BJ,Nightingale KR,Nelson RC,Palmeri ML,Trahey GE. Acoustic radiation force impulse imaging of the abdomen: demonstration of feasibility and utilityUltrasound in Medicine and BiologyYear: 2005319118511982s2.02494448076616176786 
31.  Goertz RS,Amann K,Heide R,Bernatik T,Neurath MF,Strobel D. An abdominal and thyroid status with acoustic radiation force impulse elastometry—a feasibility study: acoustic radiation force impulse elastometry of human organsEuropean Journal of RadiologyYear: 2011803e226e23020971591 
32.  Rafaelsen SR,VagnHansen C,Sørensen T,Lindebjerg J,Pløen J,Jakobsen A. Ultrasound elastography in patients with rectal cancer treated with chemoradiationEuropean Journal of RadiologyYear: 2013 
33.  Taverna G,Magnoni P,Giusti G,et al. Impact of realtime elastography versus systematic prostate biopsy method on cancer detection rate in men with a serum prostatespecific antigen between 2.5 and 10 ng/mLISRN OncologyYear: 201320135 pages584672 
34.  Rizzo L,Nunnari G,Berretta M,Cacopardo B. Acoustic radial force impulse as an effective tool for a prompt and reliable diagnosis of hepatocellular carcinoma—preliminary dataEuropean Review for Medical and Pharmacological SciencesYear: 201216111596159823111977 
35.  Zhang YF,Xu HX,He Y,et al. Virtual touch tissue quantification of acoustic radiation force impulse: a new ultrasound elastic imaging in the diagnosis of thyroid nodulesPLoS OneYear: 2012711e49094 
36.  Dighe M,Luo S,Cuevas C,Kim Y. Efficacy of thyroid ultrasound elastography in differential diagnosis of small thyroid nodulesEuropean Journal of RadiologyYear: 2013 
Article Categories:

Previous Document: Evaluation of the diagnostic power of thermography in breast cancer using bayesian network classifie...
Next Document: Classification of Prolapsed Mitral Valve versus Healthy Heart from Phonocardiograms by Multifractal ...