&lt;title&gt;Pattern recognition in pulmonary computerized tomography images using Markovian modeling&lt;/title&gt;

Prêteux, Françoise; Moubarak, Michel; Greniér, Philippe

doi:10.1117/12.44286

Cited by 3 publications

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“…The general usage of MRF for image segmentation and restoration problem solving opened a wide perspectives for applying of models based on the MRF conception for images from different parts of human's life such as: binary [1], gray- [2] and color [3,4] images processing; stereo images compression [5]; motion estimation in image sequences [6,7]; medical [8,9], air-to-ground [10] and document analysis [11] applications; the field ofdiagnostic information's determination in thermal [12] and geological [13] images; 3D acoustic image construction [14] , magnetic resonance images segmentation [9, 1 5-17]; tomography images analysis [18,19] etc.…”

Section: Introductionmentioning

confidence: 99%

<title>Split method for assessing real images as Markov random field</title>

Sineva¹,

Louniakova²

1998

SPIE Proceedings

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Deterministic and stochastic methods such as MPM and MAP have been extensively used for successfully solving problems related to image segmentation, restoration, texture analysis and motion estimation. Within this framework, Markov random field (MRF) is the most popular and powerful model used for describing and analyzing images.Nevertheless the question that arises is to know if MRF is able to model real images. In this paper we address the classification of real images into two model families, namely MRF and non-MRF families. Within this mathematical statistical framework, we propose a novel method based on parameter estimation techniques and hypothesis verification. The main steps of the approach are:(i) Estimating transition probabilities of MRF for various partitions of the image. This stage depends on following parameters: number of gray levels denoted by k, number of components in the partition denoted by L and number of the considered partitions denoted by R. We established that L depends on k, the neighborhood system and the size ofthe initial image.(ii) Testing the homogeneity hypothesis over the set of all the transition matrix estimators. When R =1 ,a one-way analysis of variance is applied. When R >1, the dependence on the two factors L and R leads to a twoway analysis ofthe variances.Such a procedure was applied to different simulated images with the presence of of exact MRF among them and to real images. Performances on the non supervised classification into MRF and non-MRF families are discussed in terms of accuracy and robustness. Application of the developed procedure to the lossy compression is presented in details.

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Section: Introductionmentioning

confidence: 99%

<title>Split method for assessing real images as Markov random field</title>

Sineva¹,

Louniakova²

1998

SPIE Proceedings

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Using kriging for 3d medical imaging

Stytz

Parrott

1993

Computerized Medical Imaging and Graphics

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Tumor detection in MR liver images by integrating edge and region information

Albu¹,

Laurendeau²,

Moisan³

2002

ESAIM: Proc.

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Abstract. This paper describes a segmentation technique for 2D interventional MR images of liver tumours. Two features of MR data were likely to challenge existing segmentation methods. The first one is the inhomogeneous intratumoral texture, while the second one is the "blurred" appearance and the non-uniform sharpness of the tumour boundary. In order to detect the region of interest, we create the tumour contour map using a multithresholding technique and a measure of similarity between successive contours. Tumours presenting boundaries with non-uniform sharpness are segmented with an algorithm based on pixel aggregation and local textural information.Résumé. Cet article décrit une méthode précise et fiable de segmentation pour des images RM des tumeurs de foie. Les approches traditionnelles de segmentation d'images ne donnent pas de bons résultats dans ce cas-là,à cause de deux caractéristiques particulières des données d'entrée : l'inhomogénéité de la texture intratumorale et l'apparence floue de la paroi des tumeurs. Pour détecter la région d'intérêt, nous créons une carte des contours de la tumeur en utilisant une technique de multi-seuillage et une mesure de similarité entre les contours successifs. Les tumeurs qui présentent des contours flous de contraste variable sont détectées avec un algorithme basé sur l'agrégation des pixels et sur l'information locale de texture.

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<title>Pattern recognition in pulmonary computerized tomography images using Markovian modeling</title>

Cited by 3 publications

References 0 publications

<title>Split method for assessing real images as Markov random field</title>

<title>Split method for assessing real images as Markov random field</title>

Using kriging for 3d medical imaging

Tumor detection in MR liver images by integrating edge and region information

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