Using Bayesian neural networks to classify segmented images

We present results that compare the performance of neural networks trained with two Bayesian methods, (i) the Evidence Framework of MacKay (1992) and (ii) a Markov Chain Monte Carlo method due to Neal (1996) on a task of classifying segmented outdoor images. We also investigate the use of the Automatic Relevance Determination method for input feature selection.

Automatic quantitation of multiple sclerosis lesions on MR images

A two-pronged approach for the automatic quantitation of multiple sclerosis (MS) lesions on magnetic resonance (MR) images has been developed. This method includes the design and use of a pulse sequence for improved lesion-to-tissue contrast (LTC) and seeks to identify and minimize the sources of false lesion classifications in segmented images. The new pulse sequence, referred to as AFFIRMATIVE (Attenuation of Fluid by Fast Inversion Recovery with MAgnetization Transfer Imaging with Variable Echoes), improves the LTC, relative to spin-echo images, by combining Fluid-Attenuated Inversion Recovery (FLAIR) and Magnetization Transfer Contrast (MTC). In addition to acquiring fast FLAIR/MTC images, the AFFIRMATIVE sequence simultaneously acquires fast spin-echo (FSE) images for spatial registration of images, which is necessary for accurate lesion quantitation. Flow has been found to be a primary source of false lesion classifications. Therefore, an imaging protocol and reconstruction methods are developed to generate "flow images" which depict both coherent (vascular) and incoherent (CSF) flow. An automatic technique is designed for the removal of extra-meningeal tissues, since these are known to be sources of false lesion classifications. A retrospective, three-dimensional (3D) registration algorithm is implemented to correct for patient movement which may have occurred between AFFIRMATIVE and flow imaging scans. Following application of these pre-processing steps, images are segmented into white matter, gray matter, cerebrospinal fluid, and MS lesions based on AFFIRMATIVE and flow images using an automatic algorithm. All algorithms are seamlessly integrated into a single MR image analysis software package. Lesion quantitation has been performed on images from 15 patient volunteers. The total processing time is less than two hours per patient on a SPARCstation 20. The automated nature of this approach should provide an objective means of monitoring the progression, stabilization, and/or regression of MS lesions in large-scale, multi-center clinical trials. ^

Correction of spatial distortion in magnetic resonance imaging

Dissertation to Obtain the Degree of Master in Biomedical Engineering

L’asymétrie mammaire chez les adolescentes présentant une scoliose idiopathique

La scoliose idiopathique de l’adolescence (SIA) est une déformation tridimensionnelle complexe du rachis affectant majoritairement les filles. L’atteinte progressive est surtout esthétique avec, notamment, une déformation de la cage thoracique résultante. L’asymétrie mammaire est une préoccupation fréquente chez ces jeunes filles. Se définissant comme une différence de forme, de position ou de volume des seins ou des complexes aréolo-mamelonnaires, l’asymétrie mammaire est courante chez les femmes, mais habituellement mineure et non visible. Il demeure incertain dans la littérature si l’asymétrie mammaire est plus fréquente chez les scoliotiques. De plus, très peu d’études ont évalué la relation entre la scoliose et l’asymétrie mammaire. De façon instinctive, on serait porté à croire que ce ne sont pas les seins qui sont asymétriques, mais plutôt la déformation du thorax en rotation qui donne cette impression. Les seins représentent un des organes les plus difficiles à mesurer étant donné leur grande variabilité. Plusieurs méthodes de mesure ont été décrites. L’imagerie par résonance magnétique (IRM) est considérée l’outil le plus précis pour définir la glande mammaire et plus particulièrement, sa délimitation sur la cage thoracique. Ce projet consiste à quantifier l’asymétrie mammaire, représentée par une différence de volume entre les deux seins, chez une cohorte de jeunes filles présentant une SIA significative, en utilisant l’IRM comme outil de mesure. Ensuite, une méthode de mesure automatisée, à partir de la topographie surfacique 3D, est proposée. Les résultats obtenus avec cette méthode sont confrontés à ceux de l’IRM. L’influence de la posture sur le volume mammaire est également étudiée à partir de ces deux modalités différentes. Pour réaliser ces objectifs, une cohorte de 30 patientes scoliotiques a été recrutée sur la base de leur courbure thoracique et de leur maturité osseuse et mammaire. Deux imageries de tronc ont été effectuées : la topographie surfacique 3D et la résonance magnétique. Dans un premier temps, la sommation des images segmentées acquises par IRM nous a permis de mesurer de façon très précise le volume mammaire. Notre cohorte présente une asymétrie mammaire moyenne statistiquement significative de 8.32%. 66.6% des patientes présentent une asymétrie ≥ 5%. Par ailleurs, le sein gauche est plus volumineux chez 65.5% des patientes. Une faible corrélation non-significative existe entre les volumes mammaires et l’angle de Cobb ainsi que la gibbosité thoracique. Par la suite, une méthode de mesure automatisée, développée à partir de l’environnement mathématique Matlab, est appliquée directement sur les reconstructions 3D. En bref, elle consiste à identifier les contours des seins pour les exciser afin d’exposer la cage thoracique puis, à soustraire le thorax complet du thorax sans seins pour déterminer les volumes mammaires. Les volumes mammaires acquis par la méthode automatisée sont, de manière attendue, de plus petites tailles que ceux obtenus à l’IRM. Une forte corrélation est établie entre les volumes mammaires obtenus par les deux différentes techniques de mesure. Bien que statistiquement significatives, les asymétries mammaires (r= 0.614, p< .001) ne sont pas aussi fortement corrélées entre elles que les volumes. Le sein droit (r=0.805) présente une corrélation plus élevée que le sein gauche (r=0.747). Finalement, l’influence de la posture est étudiée à partir des maillages 3D de l’IRM (décubitus ventral) et de la topographie surfacique 3D (position debout). D’excellentes corrélations sont confirmées entre les volumes mammaires ; r= 0.896 et r= 0.939, respectivement pour les volumes mammaires gauches et droits. Ce projet a permis de démontrer, pour la première fois, qu’il est possible de calculer le volume mammaire de façon objective et précise avec l’IRM, chez une cohorte scoliotique. Grâce à la précision des repères anatomiques, l’IRM nous a permis de revisiter une croyance populaire dans la communauté de la scoliose. Celle soutenant que l’asymétrie mammaire ressentie par les patientes n’est qu’une perception. Ces nouvelles données nous permettrons de conseiller les jeunes filles avec la SIA, concernant leurs préoccupations sur l’asymétrie de leurs seins. Nous avons confirmé que la méthode de mesure automatisée est réalisable cliniquement et pourrait être utilisée pour prédire les volumes obtenus à l’IRM. Par ailleurs, c’est le premier outil de mesure de volumétrie mammaire complètement automatisé à notre connaissance. Les volumes mammaires obtenus debout et en décubitus ventral sont comparables.

Fast automatic microstructural segmentation of ferrous alloy samples using optimum-path forest

In this work we propose a novel automatic cast iron segmentation approach based on the Optimum-Path Forest classifier (OPF). Microscopic images from nodular, gray and malleable cast irons are segmented using OPF, and Support Vector Machines (SVM) with Radial Basis Function and SVM without kernel mapping. Results show accurate and fast segmented images, in which OPF outperformed SVMs. Our work is the first into applying OPF for automatic cast iron segmentation. © 2010 Springer-Verlag.

ANN statistical image recognition method for computer vision in agricultural mobile robot navigation

The main application area in this project, is to deploy image processing and segmentation techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. Thereby, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for image recognition. Hence, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave computational platforms, along with the application of customized Back-propagation Multilayer Perceptron (MLP) algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of segmented images in which reasonably accurate results were obtained. © 2010 IEEE.

Método de detecção e classificação de lesões de pele em imagens digitais a partir do modelo Chan-Vese e máquina de vetor de suporte

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Generación de modelos físicos personalizados y segmentación de la pared arterial y el trombo intraluminal en imágenes de aneurismas aórticos abdominales

Esta tesis doctoral está encuadrada dentro del marco general de la ingeniería biomédica aplicada al tratamiento de las enfermedades cardiovasculares, enfermedades que provocan alrededor de 1.9 millones (40%) de muertes al año en la Unión Europea. En este contexto surge el proyecto europeo SCATh-Smart Catheterization, cuyo objetivo principal es mejorar los procedimientos de cateterismo aórtico introduciendo nuevas tecnologías de planificación y navegación quirúrgica y minimizando el uso de fluoroscopía. En particular, esta tesis aborda el modelado y diagnóstico de aneurismas aórticos abdominales (AAA) y del trombo intraluminal (TIL), allí donde esté presente, así como la segmentación de estas estructuras en imágenes preoperatorias de RM. Los modelos físicos específicos del paciente, construidos a partir de imágenes médicas preoperatorias, tienen múltiples usos, que van desde la evaluación preoperatoria de estructuras anatómicas a la planificación quirúrgica para el guiado de catéteres. En el diagnóstico y tratamiento de AAA, los modelos físicos son útiles a la hora de evaluar diversas variables biomecánicas y fisiológicas de las estructuras vasculares. Existen múltiples técnicas que requieren de la generación de modelos físicos que representen la anatomía vascular. Una de las principales aplicaciones de los modelos físicos es el análisis de elementos finitos (FE). Las simulaciones de FE para AAA pueden ser específicas para el paciente y permiten modelar estados de estrés complejos, incluyendo los efectos provocados por el TIL. La aplicación de métodos numéricos de análisis tiene como requisito previo la generación de una malla computacional que representa la geometría de interés mediante un conjunto de elementos poliédricos, siendo los hexaédricos los que presentan mejores resultados. En las estructuras vasculares, generar mallas hexaédricas es un proceso especialmente exigente debido a la compleja anatomía 3D ramificada. La mayoría de los AAA se encuentran situados en la bifurcación de la arteria aorta en las arterias iliacas y es necesario modelar de manera fiel dicha bifurcación. En el caso de que la sangre se estanque en el aneurisma provocando un TIL, éste forma una estructura adyacente a la pared aórtica. De este modo, el contorno externo del TIL es el mismo que el contorno interno de la pared, por lo que las mallas resultantes deben reflejar esta particularidad, lo que se denomina como "mallas conformadas". El fin último de este trabajo es modelar las estructuras vasculares de modo que proporcionen nuevas herramientas para un mejor diagnóstico clínico, facilitando medidas de riesgo de rotura de la arteria, presión sistólica o diastólica, etc. Por tanto, el primer objetivo de esta tesis es diseñar un método novedoso y robusto para generar mallas hexaédricas tanto de la pared aórtica como del trombo. Para la identificación de estas estructuras se utilizan imágenes de resonancia magnética (RM). Deben mantenerse sus propiedades de adyacencia utilizando elementos de alta calidad, prestando especial atención al modelado de la bifurcación y a que sean adecuadas para el análisis de FE. El método tiene en cuenta la evolución de la línea central del vaso en el espacio tridimensional y genera la malla directamente a partir de las imágenes segmentadas, sin necesidad de reconstruir superficies triangulares. Con el fin de reducir la intervención del usuario en el proceso de generación de las mallas, es también objetivo de esta tesis desarrollar un método de segmentación semiautomática de las distintas estructuras de interés. Las principales contribuciones de esta tesis doctoral son: 1. El diseño, implementación y evaluación de un algoritmo de generación de mallas hexaédricas conformadas de la pared y el TIL a partir de los contornos segmentados en imágenes de RM. Se ha llevado a cabo una evaluación de calidad que determine su aplicabilidad a métodos de FE. Los resultados demuestran que el algoritmo desarrollado genera mallas conformadas de alta calidad incluso en la región de la bifurcación, que son adecuadas para su uso en métodos de análisis de FE. 2. El diseño, implementación y evaluación de un método de segmentación automático de las estructuras de interés. La luz arterial se segmenta de manera semiautomática utilizando un software disponible a partir de imágenes de RM con contraste. Los resultados de este proceso sirven de inicialización para la segmentación automática de las caras interna y externa de la pared aórtica utilizando métodos basado en modelos de textura y forma a partir de imágenes de RM sin contraste. Los resultados demuestran que el algoritmo desarrollado proporciona segmentaciones fieles de las distintas estructuras de interés. En conclusión, el trabajo realizado en esta tesis doctoral corrobora las hipótesis de investigación postuladas, y pretende servir como aportación para futuros avances en la generación de modelos físicos de geometrías biológicas. ABSTRACT The frame of this PhD Thesis is the biomedical engineering applied to the treatment of cardiovascular diseases, which cause around 1.9 million deaths per year in the European Union and suppose about 40% of deaths per year. In this context appears the European project SCATh-Smart Catheterization. The main objective of this project is creating a platform which improves the navigation of catheters in aortic catheterization minimizing the use of fluoroscopy. In the framework of this project, the specific field of this PhD Thesis is the diagnosis and modeling of abdominal aortic aneurysm (AAAs) and the intraluminal thrombus (ILT) whenever it is present. Patient-specific physical models built from preoperative imaging are becoming increasingly important in the area of minimally invasive surgery. These models can be employed for different purposes, such as the preoperatory evaluation of anatomic structures or the surgical planning for catheter guidance. In the specific case of AAA diagnosis and treatment, physical models are especially useful for evaluating pressures over vascular structures. There are multiple techniques that require the generation of physical models which represent the target anatomy. Finite element (FE) analysis is one the principal applications for physical models. FE simulations for AAA may be patient-specific and allow modeling biomechanical and physiological variables including those produced by ILT, and also the segmentation of those anatomical structures in preoperative MR images. Applying numeric methods requires the generation of a proper computational mesh. These meshes represent the patient anatomy using a set of polyhedral elements, with hexahedral elements providing better results. In the specific case of vascular structures, generating hexahedral meshes is a challenging task due to the complex 3D branching anatomy. Each patient’s aneurysm is unique, characterized by its location and shape, and must be accurately represented for subsequent analyses to be meaningful. Most AAAs are located in the region where the aorta bifurcates into the iliac arteries and it is necessary to model this bifurcation precisely and reliably. If blood stagnates in the aneurysm and forms an ILT, it exists as a conforming structure with the aortic wall, i.e. the ILT’s outer contour is the same as the wall’s inner contour. Therefore, resulting meshes must also be conforming. The main objective of this PhD Thesis is designing a novel and robust method for generating conforming hexahedral meshes for the aortic wall and the thrombus. These meshes are built using largely high-quality elements, especially at the bifurcation, that are suitable for FE analysis of tissue stresses. The method accounts for the evolution of the vessel’s centerline which may develop outside a single plane, and generates the mesh directly from segmented images without the requirement to reconstruct triangular surfaces. In order to reduce the user intervention in the mesh generation process is also a goal of this PhD. Thesis to develop a semiautomatic segmentation method for the structures of interest. The segmentation is performed from magnetic resonance image (MRI) sequences that have tuned to provide high contrast for the arterial tissue against the surrounding soft tissue, so that we determine the required information reliably. The main contributions of this PhD Thesis are: 1. The design, implementation and evaluation of an algorithm for generating hexahedral conforming meshes of the arterial wall and the ILT from the segmented contours. A quality inspection has been applied to the meshes in order to determine their suitability for FE methods. Results show that the developed algorithm generates high quality conforming hexahedral meshes even at the bifurcation region. Thus, these meshes are suitable for FE analysis. 2. The design, implementation and evaluation of a semiautomatic segmentation method for the structures of interest. The lumen is segmented in a semiautomatic way from contrast filled MRI using an available software. The results obtained from this process are used to initialize the automatic segmentation of the internal and external faces of the aortic wall. These segmentations are performed by methods based on texture and shape models from MRI with no contrast. The results show that the algorithm provides faithful segmentations of the structures of interest requiring minimal user intervention. In conclusion, the work undertaken in this PhD. Thesis verifies the investigation hypotheses. It intends to serve as basis for future physical model generation of proper biological anatomies used by numerical methods.

Generating Segmented Quality Meshes from Images

Techniques devoted to generating triangular meshes from intensity images either take as input a segmented image or generate a mesh without distinguishing individual structures contained in the image. These facts may cause difficulties in using such techniques in some applications, such as numerical simulations. In this work we reformulate a previously developed technique for mesh generation from intensity images called Imesh. This reformulation makes Imesh more versatile due to an unified framework that allows an easy change of refinement metric, rendering it effective for constructing meshes for applications with varied requirements, such as numerical simulation and image modeling. Furthermore, a deeper study about the point insertion problem and the development of geometrical criterion for segmentation is also reported in this paper. Meshes with theoretical guarantee of quality can also be obtained for each individual image structure as a post-processing step, a characteristic not usually found in other methods. The tests demonstrate the flexibility and the effectiveness of the approach.

Generating segmented meshes from textured color images

This paper presents a new framework for generating triangular meshes from textured color images. The proposed framework combines a texture classification technique, called W-operator, with Imesh, a method originally conceived to generate simplicial meshes from gray scale images. An extension of W-operators to handle textured color images is proposed, which employs a combination of RGB and HSV channels and Sequential Floating Forward Search guided by mean conditional entropy criterion to extract features from the training data. The W-operator is built into the local error estimation used by Imesh to choose the mesh vertices. Furthermore, the W-operator also enables to assign a label to the triangles during the mesh construction, thus allowing to obtain a segmented mesh at the end of the process. The presented results show that the combination of W-operators with Imesh gives rise to a texture classification-based triangle mesh generation framework that outperforms pixel based methods. Crown Copyright (C) 2009 Published by Elsevier Inc. All rights reserved.

A segmentation-based and partial-volume-compensated method for an accurate measurement of lateral ventricular volumes on T-1-weighted magnetic resonance images

Lateral ventricular volumes based on segmented brain MR images can be significantly underestimated if partial volume effects are not considered. This is because a group of voxels in the neighborhood of lateral ventricles is often mis-classified as gray matter voxels due to partial volume effects. This group of voxels is actually a mixture of ventricular cerebro-spinal fluid and the white matter and therefore, a portion of it should be included as part of the lateral ventricular structure. In this note, we describe an automated method for the measurement of lateral ventricular volumes on segmented brain MR images. Image segmentation was carried in combination of intensity correction and thresholding. The method is featured with a procedure for addressing mis-classified voxels in the surrounding of lateral ventricles. A detailed analysis showed that lateral ventricular volumes could be underestimated by 10 to 30% depending upon the size of the lateral ventricular structure, if mis-classified voxels were not included. Validation of the method was done through comparison with the averaged manually traced volumes. Finally, the merit of the method is demonstrated in the evaluation of the rate of lateral ventricular enlargement. (C) 2001 Elsevier Science Inc. All rights reserved.

Segmentation of striatal brain structures from high resolution pet images

Dissertation presented at the Faculty of Science and Technology of the New University of Lisbon in fulfillment of the requirements for the Masters degree in Electrical Engineering and Computers

Automated segmentation, tracking and evaluation of bacteria in microscopy images

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Central and Cortical Gray Mater Segmentation of Magnetic Resonance Images of the Fetal Brain

Motivation. The study of human brain development in itsearly stage is today possible thanks to in vivo fetalmagnetic resonance imaging (MRI) techniques. Aquantitative analysis of fetal cortical surfacerepresents a new approach which can be used as a markerof the cerebral maturation (as gyration) and also forstudying central nervous system pathologies [1]. However,this quantitative approach is a major challenge forseveral reasons. First, movement of the fetus inside theamniotic cavity requires very fast MRI sequences tominimize motion artifacts, resulting in a poor spatialresolution and/or lower SNR. Second, due to the ongoingmyelination and cortical maturation, the appearance ofthe developing brain differs very much from thehomogenous tissue types found in adults. Third, due tolow resolution, fetal MR images considerably suffer ofpartial volume (PV) effect, sometimes in large areas.Today extensive efforts are made to deal with thereconstruction of high resolution 3D fetal volumes[2,3,4] to cope with intra-volume motion and low SNR.However, few studies exist related to the automatedsegmentation of MR fetal imaging. [5] and [6] work on thesegmentation of specific areas of the fetal brain such asposterior fossa, brainstem or germinal matrix. Firstattempt for automated brain tissue segmentation has beenpresented in [7] and in our previous work [8]. Bothmethods apply the Expectation-Maximization Markov RandomField (EM-MRF) framework but contrary to [7] we do notneed from any anatomical atlas prior. Data set &Methods. Prenatal MR imaging was performed with a 1-Tsystem (GE Medical Systems, Milwaukee) using single shotfast spin echo (ssFSE) sequences (TR 7000 ms, TE 180 ms,FOV 40 x 40 cm, slice thickness 5.4mm, in plane spatialresolution 1.09mm). Each fetus has 6 axial volumes(around 15 slices per volume), each of them acquired inabout 1 min. Each volume is shifted by 1 mm with respectto the previous one. Gestational age (GA) ranges from 29to 32 weeks. Mother is under sedation. Each volume ismanually segmented to extract fetal brain fromsurrounding maternal tissues. Then, in-homogeneityintensity correction is performed using [9] and linearintensity normalization is performed to have intensityvalues that range from 0 to 255. Note that due tointra-tissue variability of developing brain someintensity variability still remains. For each fetus, ahigh spatial resolution image of isotropic voxel size of1.09 mm is created applying [2] and using B-splines forthe scattered data interpolation [10] (see Fig. 1). Then,basal ganglia (BS) segmentation is performed on thissuper reconstructed volume. Active contour framework witha Level Set (LS) implementation is used. Our LS follows aslightly different formulation from well-known Chan-Vese[11] formulation. In our case, the LS evolves forcing themean of the inside of the curve to be the mean intensityof basal ganglia. Moreover, we add local spatial priorthrough a probabilistic map created by fitting anellipsoid onto the basal ganglia region. Some userinteraction is needed to set the mean intensity of BG(green dots in Fig. 2) and the initial fitting points forthe probabilistic prior map (blue points in Fig. 2). Oncebasal ganglia are removed from the image, brain tissuesegmentation is performed as described in [8]. Results.The case study presented here has 29 weeks of GA. Thehigh resolution reconstructed volume is presented in Fig.1. The steps of BG segmentation are shown in Fig. 2.Overlap in comparison with manual segmentation isquantified by the Dice similarity index (DSI) equal to0.829 (values above 0.7 are considered a very goodagreement). Such BG segmentation has been applied on 3other subjects ranging for 29 to 32 GA and the DSI hasbeen of 0.856, 0.794 and 0.785. Our segmentation of theinner (red and blue contours) and outer cortical surface(green contour) is presented in Fig. 3. Finally, torefine the results we include our WM segmentation in theFreesurfer software [12] and some manual corrections toobtain Fig.4. Discussion. Precise cortical surfaceextraction of fetal brain is needed for quantitativestudies of early human brain development. Our workcombines the well known statistical classificationframework with the active contour segmentation forcentral gray mater extraction. A main advantage of thepresented procedure for fetal brain surface extraction isthat we do not include any spatial prior coming fromanatomical atlases. The results presented here arepreliminary but promising. Our efforts are now in testingsuch approach on a wider range of gestational ages thatwe will include in the final version of this work andstudying as well its generalization to different scannersand different type of MRI sequences. References. [1]Guibaud, Prenatal Diagnosis 29(4) (2009). [2] Rousseau,Acad. Rad. 13(9), 2006, [3] Jiang, IEEE TMI 2007. [4]Warfield IADB, MICCAI 2009. [5] Claude, IEEE Trans. Bio.Eng. 51(4) (2004). [6] Habas, MICCAI (Pt. 1) 2008. [7]Bertelsen, ISMRM 2009 [8] Bach Cuadra, IADB, MICCAI 2009.[9] Styner, IEEE TMI 19(39 (2000). [10] Lee, IEEE Trans.Visual. And Comp. Graph. 3(3), 1997, [11] Chan, IEEETrans. Img. Proc, 10(2), 2001 [12] Freesurfer,http://surfer.nmr.mgh.harvard.edu.

Multi-Atlas based Segmentation of Head and Neck CT Images using Active Contour

This paper presents the segmentation of bilateral parotid glands in the Head and Neck (H&N) CT images using an active contour based atlas registration. We compare segmentation results from three atlas selection strategies: (i) selection of "single-most-similar" atlas for each image to be segmented, (ii) fusion of segmentation results from multiple atlases using STAPLE, and (iii) fusion of segmentation results using majority voting. Among these three approaches, fusion using majority voting provided the best results. Finally, we present a detailed evaluation on a dataset of eight images (provided as a part of H&N auto segmentation challenge conducted in conjunction with MICCAI-2010 conference) using majority voting strategy.