Papiloma septo nasal

The statistical analysis shows papiloma septo nasal the use of the fractal dimension of the mass texture discriminate best between malignant and benignant sinus tumor, therefore leads to an improvement of the diagnosis. Scopul acestui articol este de a dezvolta algoritmi având ca punct de plecaremetode din teoria sistemelor dinamice haotice analiza dimensiunii fractale și metode statistice nonparametrice bootstrap și determinarea intervalelor de încredere și de a-i aplica la studiul imaginilor CT reprezentând tumori maligne sau benigne ale zonei para-sinusale.

Studiul statistic a arătat că dimensiunea fractală a texturii discriminează mai bine între tumorile maligne și benigne, ducând astfel la îmbunătățirea posibilităților de diagnostic. Introduction Fractal analysis and nonlinear time series analysis are branches of chaos theory. They provide useful methods for the characterization of single and multi variable signals images and papiloma septo nasal series.

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Usually, fractal analysis refers to a collection of methods for the description and quantization of geometric features of irregular forms and patterns. It was largely applied for the study of biological systems and subsystems at microscopic and macroscopic scale, image enhancement and compression, fracture analysis, texture classification because of their fractal-like structure of themanipulated objects [3], [7], [8].

Its most known measure is the fractal dimension used to provideinformation on the irregularity of an object contour or self similarities in a texture.

Fractal analysis was largely applied for distinction between normal and modified tissue and between benignant and malignant tissue [3], [7][8]. The case study papiloma septo nasal in this paper targets the classification of masses into benignant and malignant.

When symptoms arise they often mimic the clinical picture of chronic rhinosinusitis, which is by far more frequent.

For these reasons tumor diagnosis is often delayed [9], [6]. As there is a myriad of different tumor entities, it may be beneficial for the clinician to distinguish between epithelial tumors papilloma, pleomorphic adenomamesenchymal tumors fibromatosis, osteomaneural-related tumors schwannoma and tumor like lesions giant cell granuloma, mucocele [1].

Imaging is indicated whenever there is clinical suspicion for a neoplastic process. Computed tomography [CT] and magnetic resonance imaging [MRI] are complimentary in the pretreatment evaluation of patients with sinonasal tumors. In this paper we present a study based on the fractal dimension of the analyzed masses formed in the paranasal sinuses region. We consider a series a number of 34 of CT images taken with contrast substance containing the tissue that has fractal — like structure.

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The 34 CT images consist in 17 malignant tumors and 17 benignant two samples of same size. Distinction between malignant and benignant masses is made by calculating: 1. A nonparametric bootstrap analysis with a jackknife correction for errors [5], [4] is run to assess the accuracy of our primary statistical results.

We conclude that a more accurate discrimination is obtained when using the Fd of texture. Methods In the last 2 decades fractal analysis become a powerful tool for analyzing the form, pattern and growth in biological systems.

The statistical inference is based on a nonparametric bootstrap technique. The general bootstrap method introduced by Efron is a resampling procedure for estimating the distributions of statistics based on independent observations.

The bootstrap method is shown to be successful in many situations, which is being accepted as an alternative to the asymptotic methods. In fact, it is better than some other asymptotic methods, such as the traditional normal approximation and the Edgeworth expansion [2].

The nonparametric bootstrap is suitable when the population distribution function is completely unknown and when the sample size is small less than It consists in : 1. We have used images that contain those parts of the masses that are not obstructed by nearby bones, such that we can obtain information papiloma septo nasal the free evolution of the mass. In the second case, the contour of the mass is extracted by using following procedure: — search for all the pixels that are black and are not part of the object, their neighbors are all white or, in some cases, have a limited number of black neighbors — one or two papiloma septo nasal make them white.

By employing this procedure we will obtain the interconnected pixels in the image that delimitate the object from the background.

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Statistical Methods A traditional approach to statistical inference is to make assumptions about the structure of the population e. This familiar approach has two potentially important deficiencies: if the assumptions about the population are wrong, then the corresponding sampling distribution of the statistic may be seriously inaccurate. On the other hand, if asymptotic results are relied upon, papiloma septo nasal may not hold to the required level of accuracy in a relatively small sample.

In contrast, the nonparametric bootstrap allows us to estimate the sampling distribution of a statistic empirically without making assumptions about the form of the population, and without deriving the sampling distribution explicitly.

The general nonparametric bootstrap procedure is based on five steps as follows refer to Efron and Tibshirani [5] for detailed discussions : There are several approaches to constructing bootstrap confidence intervals. One of the most widely used is the bootstrap percentile interval: the endpoints of a papiloma septo nasal to the uncorrected percentile interval. The above algorithm and the random generator papiloma septo nasal the bootstrap samples was implemented as a MatLab routine.

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In each of the four resulted data papiloma septo nasal, the statistics of interest were the mean and standard deviation. The confidence interval was calculated for the mean with the BCa method. Results Two sets of CT images representing sinus tumors are analyzed: 17 malignant Fig.

The fractal dimension values of the contours and textures of all this images were estimated using the procedures described in Chapter 2. Some results are displayed in Table 1. Conclusions Because of the asymptomatic or confounding symptoms of the paranasal sinuses tumors, any improvement in early diagnosis is beneficial for the rhinologist practician. The goals of patient evaluation for suspected sinonasal cancers include a precise diagnosis, determining the extend of disease and developing a treatment plan.

The two powerful algorithms involved in the study — the box-counting and the nonparametric bootstrap with BCa revealed that omputing the fractal dimension of the mass texture is an accurate and valuable method for a precise papiloma septo nasal. Parazit clan romania Fd for masses texture leads to a better statistical discrimination because it takes into consideration all the details of the analyzed image sections.

By improving the CT noninvasive diagnosis methods, the presence of a sinonasal tumor can be more accurate predicted, leading to a mor efficient medical evaluation and treatment. Anghel, A. Anghel, Davison, D. Hinkley, Cambridge: Cambridge University Press. Dobrescu, C. Vasilescu Efron, Journal of the American Statistical Association 82 : — Efron,R. Tibshirani, Chapman and Hall, London. Galzada, R. Weber, Landini Losa, Birkhauser Verlag : p. Olteanu, A. Udrea, Series A, Papiloma septo nasal.

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