François Chung, Ph.D.

Tag: modelització de l’aparença

CVIU 2013 - Article de revista científica

CVIU 2013 – Article de revista científica

|

Publicació

François Chung, Hervé Delingette; Regional appearance modeling based on the clustering of intensity profiles; In: Computer Vision and Image Understanding (CVIU), 117 (6), pp. 705-717, 2013.

Abstract

Model-based image segmentation is a popular approach for the segmentation of anatomical structures from medical images because it includes prior knowledge about the shape and appearance of structures of interest. This paper focuses on the formulation of a novel appearance prior that can cope with large variability between subjects, for instance due to the presence of pathologies. Instead of relying on Principal Component Analysis (PCA) such as in Statistical Appearance Models (SAMs), our approach relies on a multimodal intensity profile atlas from which a point may be assigned to several profile modes consisting of a mean profile and its covariance matrix. These profile modes are first estimated without any intra-subject registration through a boosted Expectation-Maximization (EM) classification based on spectral clustering. Then, they are projected on a reference mesh whose role is to store the appearance information in a common geometric representation. We show that this prior leads to better performance than the classical monomodal PCA approach while relying on fewer profile modes.

Paraules clau

  • appearance modeling
  • medical imaging
  • model-based image segmentation
  • unsupervised clustering

Referències

Publicació

Versió publicada (ScienceDirect)
Versió pre-print (PDF)
Referència bibliogràfica (BibTeX)

Articles relacionats

3D Anatomical Human (projecte INRIA)
Ph.D. Thesis 2011 (tesi doctoral)

LAP 2011 - Llibre

LAP 2011 – Llibre

|

Publicació

François Chung; Regional appearance modeling for model-based image segmentation: Methodological approaches to improve the accuracy of model-based image segmentation; Lambert Academic Publishing (LAP), Saarbrücken, 2011; ISBN: 978-3844322095.

Abstract

This thesis presents a novel appearance prior for model-based image segmentation. This appearance prior, denoted as Multimodal Prior Appearance Model (MPAM), is built upon an Expectation–Maximization (EM) clustering of intensity profiles with model order selection to automatically select the number of profile classes. Unlike classical approaches based on Principal Component Analysis (PCA), the clustering is considered as regional because intensity profiles are classified for each mesh and not for each vertex. Comparative results on liver profiles from Computed Tomography (CT) images show that MPAM outperforms PCA-based appearance models. Finally, methods for the analysis of lower limb structures from Magnetic Resonance (MR) images are presented. A first part deals with the creation of subject-specific models for kinematic simulations of the lower limbs. In a second part, the performance of statistical models is compared in the context of lower limb bone segmentation when only a small number of datasets is available for training.

Referències

Publicació

Llibre (Amazon)
Llibre (MoreBooks)
Referència bibliogràfica (BibTeX)

Articles relacionats

3D Anatomical Human (projecte INRIA)
Ph.D. Thesis 2011 (tesi doctoral)

Més informació

LAP – Lambert Academic Publishing

Ph.D. Thesis 2011 - Tesi doctoral

Ph.D. Thesis 2011 – Tesi doctoral

|

Publicació

François Chung; Regional appearance modeling for deformable model-based image segmentation; Tesi doctoral (Ph.D. Thesis), Mines ParisTech, Centre de Mathématiques Appliquées, 2011.

Abstract

This thesis presents a novel appearance prior for model-based image segmentation. This appearance prior, denoted as Multimodal Prior Appearance Model (MPAM), is built upon an Expectation-Maximization (EM) clustering of intensity profiles with model order selection to automatically select the number of profile classes. Unlike classical approaches based on Principal Component Analysis (PCA), the clustering is considered as regional because intensity profiles are classified for each mesh and not for each vertex.

First, we explain how to build a MPAM from a training set of meshes and images. The clustering of intensity profiles and the determination of the number of appearance regions by a novel model order selection criterion are explained. A spatial regularization approach to spatially smooth the clustering of profiles is presented and the projection of the appearance information from each dataset on a reference mesh is described.

Second, we present a boosted clustering based on spectral clustering, which optimizes the clustering of profiles for segmentation purposes. The representation of the similarity between data points in the spectral space is explained. Comparative results on liver profiles from Computed Tomography (CT) images show that our approach outperforms PCA-based appearance models.

Finally, we present methods for the analysis of lower limb structures from Magnetic Resonance (MR) images. In a first part, our technique to create subject-specific models for kinematic simulations of lower limbs is described. In a second part, the performance of statistical models is compared in the context of lower limb bones segmentation when only a small number of datasets is available for training.

Paraules clau

  • appearance modeling
  • liver
  • lower limbs
  • medical imaging
  • model-based image segmentation
  • unsupervised clustering

Referències

Publicació

Versió publicada (PDF)
Presentació (PDF)
Referència bibliogràfica (BibTeX)
Còpia impresa (Amazon)
Còpia impresa (MoreBooks)

Articles relacionats

3D Anatomical Human (projecte INRIA)
CVIU 2013 (article de revista científica)
LAP 2011 (llibre)

Més informació

PASTEL
Mines ParisTech

MICCAI 2009 - Acta de conferència

MICCAI 2009 – Acta de conferència

|

Publicació

François Chung, Hervé Delingette; Multimodal prior appearance models based on regional clustering of intensity profiles; MICCAI 2009: Medical Image Computing and Computer-Assisted Intervention, London, 2009.

Abstract

Model-based image segmentation requires prior information about the appearance of a structure in the image. Instead of relying on Principal Component Analysis (PCA) such as in Statistical Appearance Models (SAMs), we propose a method based on a regional clustering of intensity profiles that does not rely on an accurate pointwise registration. Our method is built upon the Expectation-Maximization (EM) algorithm with regularized covariance matrices and includes spatial regularization. The number of appearance regions is determined by a novel model order selection criterion. The prior is described on a reference mesh where each vertex has a probability to belong to several intensity profile classes.

Referències

Publicació

Versió publicada (Springer)
Versió pre-print (PDF)
Poster (PDF)
Referència bibliogràfica (BibTeX)

Articles relacionats

Model multimodal (projecte INRIA)
3D Anatomical Human (projecte INRIA)

Més informació

MICCAI – Medical Image Computing and Computer Assisted Intervention

Model multimodal

Model multimodal

|

Projecte INRIA @Sophia-Antipolis, França (2009). La segmentació d'imatges basada en models requereix informació prèvia sobre l'aparença d'una estructura en la imatge. En lloc de basar-se en l'anàlisi de components principals (ACP), com els Statistical Appearance Models (SAM), proposem un model basat en una agrupació regional de perfils d'intensitat que no es basa en un registre precís.

Aquest model multimodal d'aparença, denominat Multimodal Prior Appearance Model (MPAM), es basa en l'algoritme d'expectació-maximització (EM) amb matrius de covariància regularitzats i inclou una regularització espacial. El nombre de regions d'aparença está determinat per un nou criteri de selecció de l'ordre del model. El model es descriu en una malla de referència en la qual cada vèrtex té una probabilitat de pertànyer a diverses classes de perfil d'intensitat.

Vam provar el nostre mètode amb 7 malles del fetge segmentat a partir d'imatges tomogràfiques (TC) i 4 malles del tèbia segmentat a partir d'imatges per ressonància magnètica (IRM). Per ambdues estructures, perfils d'intensitat compostos per 10 mostres extretes cada mm es van generar a partir de malles amb al voltant de 4000 vèrtexs.

El principal avantatge del nostre mètode és que les regions d'aparença són extretes sense necessitat d'un registre punt a punt precís. Un altre avantatge és que un model pot ser construït amb pocs conjunts de dades (de fet un conjunt és suficient). A més, el model és multimodal, i per tant capaç de fer front a grans variacions d'aparença.

Referències

Articles relacionats

MICCAI 2009 (acta de conferència)
Ph.D. Thesis 2011 (tesi doctoral)

Idiomes

Articles recents

Categories

Notícies

Recent (Twitter) & Popular (Reddit)

Cerca