Active shape model segmentation with optimal features

Ginneken, Bram van; Frangi, Alejandro F.; Staal, Joes; Romeny, Bart M. ter Haar; Viergever, Max A.

doi:10.1109/tmi.2002.803121

Cited by 478 publications

(287 citation statements)

References 37 publications

Supporting

Mentioning

284

Contrasting

Unclassified

Order By: Relevance

“…However, there are many more possibilites to model the local appearance (e.g. [11]) which will most likely improve the obtained results. Our future work will focus on evaluating these alternative appearance models and on improving the deformable model search with more sophisticated search and relaxation schemes.…”

Section: Discussionmentioning

confidence: 99%

Active Shape Models for a Fully Automated 3D Segmentation of the Liver – An Evaluation on Clinical Data

Heimann

Wolf

Meinzer

2006

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006

View full text Add to dashboard Cite

Abstract. This paper presents an evaluation of the performance of a three-dimensional Active Shape Model (ASM) to segment the liver in 48 clinical CT scans. The employed shape model is built from 32 samples using an optimization approach based on the minimum description length (MDL). Three different gray-value appearance models (plain intensity, gradient and normalized gradient profiles) are created to guide the search. The employed segmentation techniques are ASM search with 10 and 30 modes of variation and a deformable model coupled to a shape model with 10 modes of variation. To assess the segmentation performance, the obtained results are compared to manual segmentations with four different measures (overlap, average distance, RMS distance and ratio of deviations larger 5mm). The only appearance model delivering usable results is the normalized gradient profile. The deformable model search achieves the best results, followed by the ASM search with 30 modes. Overall, statistical shape modeling delivers very promising results for a fully automated segmentation of the liver.

show abstract

Section: Discussionmentioning

confidence: 99%

Active Shape Models for a Fully Automated 3D Segmentation of the Liver – An Evaluation on Clinical Data

Heimann

Wolf

Meinzer

2006

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006

View full text Add to dashboard Cite

show abstract

“…As with many other clinical shape studies, the goal of the hip fracture study is to develop an image-based assessment of the risk of the occurrence of the pathology. Active Shape Modeling, which is a technology developed by Cootes and Taylor [21], has also been heavily used in model-guided image segmentation algorithms for a wide variety of biomedical problems (e.g., [92,110]). Computationally-derived models of shape may also be promising tools for the study of anthropology and paleontology.…”

Section: Other Applicationsmentioning

confidence: 99%

Shape Modeling and Analysis with Entropy-Based Particle Systems

Cates

Fletcher

Styner

et al. 2007

Lecture Notes in Computer Science

139

224

View full text Add to dashboard Cite

Many important fields of basic research in medicine and biology routinely employ tools for the statistical analysis of collections of similar shapes. Biologists, for example, have long relied on homologous, anatomical landmarks as shape models to characterize the growth and development of species. Increasingly, however, researchers are exploring the use of more detailed models that are derived computationally from three-dimensional images and surface descriptions. While computationally-derived models of shape are promising new tools for biomedical research, they also present some significant engineering challenges, which existing modeling methods have only begun to address.In this dissertation, I propose a new computational framework for statistical shape modeling that significantly advances the state-of-the-art by overcoming many of the limitations of existing methods. The framework uses a particle-system representation of shape, with a fast correspondence-point optimization based on information content. The optimization balances the simplicity of the model (compactness) with the accuracy of the shape representations by using two commensurate entropy metrics and no free parameters. The idea is to maximize both the geometric accuracy and the statistical simplicity of the shape model, in accordance with the principle of parsimony in model selection. The nonparametric representation allows the method to be applied to a larger class of problems than existing methods, including nonspherical surfaces, open surfaces, and sets of multiple surfaces.The relative simplicity of the surface representation and the low number of free parameters results in a framework that is easy to use and can operate directly on image segmentations. In collaboration with scientists from several important areas of biomedicine, I have demonstrated that the proposed method is indeed an e↵ective tool for scientific research.The specific research contributions of this dissertation are as follows. First, I describe a mathematical framework and a robust numerical algorithm for computing optimized correspondence-point shape models using an entropy-based optimization and particle-system technology. Second, I develop a series of extensions of the framework to more general classes of shape analysis problems, including the analysis of multiple-object complexes, the generalization to correspondence based on generic functions of position, an extension to handle surfaces with open boundaries, and shape modeling with simple regression. Third, I describe the application of statistical hypothesis testing, regression analysis, and multiple-analysis of covariance to the proposed shape models. I also introduce new techniques for visualization and interpretation of these statistics. Finally, in cooperation with biomedical researchers, I present validation of the above research contributions by their successful application to real-world research problems.

show abstract

“…In order to fit a model to an image. Van Ginneken et al learned local objective functions from annotated training images [18]. In this work, image features are obtained by approximating the pixel values in a region around a pixel of interest The learning algorithm use to map images features to objective values is a k-Nearest-Neighbor classifier (kNN) learned from the data.…”

Section: Related Workmentioning

confidence: 99%

“…In this work, image features are obtained by approximating the pixel values in a region around a pixel of interest The learning algorithm use to map images features to objective values is a k-Nearest-Neighbor classifier (kNN) learned from the data. We used similar methodology developed by Wimmer et al [4] which combines multitude of qualitatively different features [19], determines the most relevant features using machine learning and learns objective functions from annotated images [18]. To extract discriptive features from the image, Michel et al [14] extracted the location of 22 feature points within the face and determine their motion between an image that shows the neutral state of the face and an image that represents a facial expression.…”

Section: Related Workmentioning

confidence: 99%

Model Based Analysis of Face Images for Facial Feature Extraction

Riaz

Mayer

Beetz

et al. 2009

Computer Analysis of Images and Patterns

View full text Add to dashboard Cite

Abstract. This paper describes a comprehensive approach to extract a common feature set from the image sequences. We use simple features which are easily extracted from a 3D wireframe model and efficiently used for different applications on a benchmark database. Features verstality is experimented on facial expressions recognition, face reognition and gender classification. We experiment different combinations of the features and find reasonable results with a combined features approach which contain structural, textural and temporal variations. The idea follows in fitting a model to human face images and extracting shape and texture information. We parametrize these extracted information from the image sequences using active appearance model (AAM) approach. We further compute temporal parameters using optical flow to consider local feature variations. Finally we combine these parameters to form a feature vector for all the images in our database. These features are then experimented with binary decision tree (BDT) and Bayesian Network (BN) for classification. We evaluated our results on image sequences of Cohn Kanade Facial Expression Database (CKFED). The proposed system produced very promising recognition rates for our applications with same set of features and classifiers. The system is also realtime capable and automatic.

show abstract

Active shape model segmentation with optimal features

Cited by 478 publications

References 37 publications

Active Shape Models for a Fully Automated 3D Segmentation of the Liver – An Evaluation on Clinical Data

Active Shape Models for a Fully Automated 3D Segmentation of the Liver – An Evaluation on Clinical Data

Shape Modeling and Analysis with Entropy-Based Particle Systems

Model Based Analysis of Face Images for Facial Feature Extraction

Contact Info

Product

Resources

About