A Statistical Approach to Snakes for Bimodal and Trimodal Imagery

© 1999 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.DOI: 10.1109/ICCV.1999.790317In this paper, we describe a new region-based approach to active contours for segmenting images com- posed of two or three types of regions characterizable by a given statistic. The essential idea is to derive curve evolutions which separate two or more values of a pre- determined set of statistics computed over geometrically determined subsets of the image. Both global and local image information is used to evolve the active contour. Image derivatives, however, are avoided, thereby giving rise to a further degree of noise robust- ness compared to most edge-based snake algorithms

Multi-object segmentation using coupled nonparametric shape and relative pose priors

We present a new method for multi-object segmentation in a maximum a posteriori estimation framework. Our method is motivated by the observation that neighboring or coupling objects in images generate configurations and co-dependencies which could potentially aid in segmentation if properly exploited. Our approach employs coupled shape and inter-shape pose priors that are computed using training images in a nonparametric multi-variate kernel density estimation framework. The coupled shape prior is obtained by estimating the joint shape distribution of multiple objects and the inter-shape pose priors are modeled via standard moments. Based on such statistical models, we formulate an optimization problem for segmentation, which we solve by an algorithm based on active contours. Our technique provides significant improvements in the segmentation of weakly contrasted objects in a number of applications. In particular for medical image analysis, we use our method to extract brain Basal Ganglia structures, which are members of a complex multi-object system posing a challenging segmentation problem. We also apply our technique to the problem of handwritten character segmentation. Finally, we use our method to segment cars in urban scenes

Localizing Region-Based Active Contours

©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TIP.2008.2004611In this paper, we propose a natural framework that allows any region-based segmentation energy to be re-formulated in a local way. We consider local rather than global image statistics and evolve a contour based on local information. Localized contours are capable of segmenting objects with heterogeneous feature profiles that would be difficult to capture correctly using a standard global method. The presented technique is versatile enough to be used with any global region-based active contour energy and instill in it the benefits of localization. We describe this framework and demonstrate the localization of three well-known energies in order to illustrate how our framework can be applied to any energy. We then compare each localized energy to its global counterpart to show the improvements that can be achieved. Next, an in-depth study of the behaviors of these energies in response to the degree of localization is given. Finally, we show results on challenging images to illustrate the robust and accurate segmentations that are possible with this new class of active contour models

Automatic method of analysis of OCT images in assessing the severity degree of glaucoma and the visual field loss

Introduction: In many practical aspects of ophthalmology, it is necessary to assess the severity degree of glaucoma in cases where, for various reasons, it is impossible to perform a visual field test - static perimetry. These are cases in which the visual field test result is not reliable, e.g. advanced AMD (Age-related Macular Degeneration). In these cases, there is a need to determine the severity of glaucoma, mainly on the basis of optic nerve head (ONH) and retinal nerve fibre layer (RNFL) structure. OCT is one of the diagnostic methods capable of analysing changes in both, ONH and RNFL in glaucoma. Material and method: OCT images of the eye fundus of 55 patients (110 eyes) were obtained from the SOCT Copernicus (Optopol Tech. SA, Zawiercie, Poland). The authors proposed a new method for automatic determination of the RNFL (retinal nerve fibre layer) and other parameters using: mathematical morphology and profiled segmentation based on morphometric information of the eye fundus. A quantitative ratio of the quality of the optic disk and RNFL – BGA (biomorphological glaucoma advancement) was also proposed. The obtained results were compared with the results obtained from a static perimeter. Results: Correlations between the known parameters of the optic disk as well as those suggested by the authors and the results obtained from static perimetry were calculated. The result of correlation with the static perimetry was 0.78 for the existing methods of image analysis and 0.86 for the proposed method. Practical usefulness of the proposed ratio BGA and the impact of the three most important features on the result were assessed. The following results of correlation for the three proposed classes were obtained: cup/disk diameter 0.84, disk diameter 0.97 and the RNFL 1.0. Thus, analysis of the supposed visual field result in the case of glaucoma is possible based only on OCT images of the eye fundus. Conclusions: The calculations and analyses performed with the proposed algorithm and BGA ratio confirm that it is possible to calculate supposed mean defect (MD) of the visual field test based on OCT images of the eye fundus

Algoritmos de segmentação de imagem e sua aplicação em imagens do sistema cardiovascular

Em grande parte dos países desenvolvidos, doenças cardiovasculares, como ataques cardíacos ou acidentes vasculares cerebrais, representam a maior causa de morte. O comportamento do sistema cardiovascular e as causas dos seus problemas têm vindo a ser alvo de diversos estudos. Para tal, é essencial o desenvolvimento de técnicas robustas e eficientes de processamento e análise de imagem que facilitem a compreensão, diagnóstico e tratamento do sistema cardiovascular.Graças às novas técnicas de imagiologia, tais como Tomografia Computorizada (CT), Angiografia, Ressonância Magnética (MR) e Ultrassons (Doppler), tem vindo a ser possível a modelação geométrica 3D das estruturas do sistema cardiovascular, como vasos e coração. Contudo, devido à complexidade das imagens envolvidas, tal modelação ainda requer frequentemente a utilização de procedimentos e ajustes manuais de maneira a conseguir-se modelos realistas.Técnicas de processamento de imagem permitem melhorar e realçar a informação contida nas imagens originais. Por seu lado, técnicas de análise de imagem, como de segmentação de imagem, têm um papel crucial na extracção de informação de alto-nível a partir das imagens pré-processadas. No que diz respeito à segmentação de imagem, uma das principais tarefas para a compreensão, análise e interpretação de imagens, o seu principal objectivo é a divisão da imagem original em regiões (ou classes) homogéneas relativamente a uma ou mais características.O principal objectivo deste artigo é a apresentação e discussão de métodos de segmentação adequados para a análise de imagens do sistema cardiovascular, nomeadamente de imagens de Doppler