This paper reports a novel method for fully automated segmentation that is based on description of shape and its variation using Point Distribution Models (PDM). An improvement of the Active Shape procedure introduced by Cootes and Taylor to find new examples of previously learned shapes using PDMs is presented. The new method for segmentation and interpretation of deep neuroanatomic structures such as thalamus, putamen, ventricular system, etc. incorporates a priori knowledge about shapes of the neuroanatomic structures to provide their robust segmentation and labeling in MR brain images. The method was trained in 8 MR brain images and tested in 19 brain images by comparison to observer-defined independent standards. Neuroanatomic structures in all testing images were successfully identified. Computer-identified and observer-defined neuroanatomic structures agreed well. The average labeling error was 7 \Sigma 3%. Border positioning errors were quite small, with the average border posi...

We propose an approach for boundary finding where the correspondence of a subset of boundary points to a model is simultaneously determined. Global shape parameters derived from the statistical variation of object boundary points in a training set are used to model the object. A Bayesian formulation, based on this prior knowledge and the edge information of the input image, is employed to find the object boundary with its subset points in correspondence with boundaries in the training set or the mean boundary. We compared the use of a generic smoothness prior and a uniform independent prior with the training set prior in order to demonstrate the power of this statistical information. A number of experiments were performed on both synthetic and real medical images of the brain and heart to evaluate the approach, including the validation of the dependence of the method on image quality, different initialization and prior information. 1 Introduction Locating the boundary of structures i...

An active shape model segmentation scheme is presented that is steered by optimal local features, contrary to normalized first order derivative profiles, as in the original formulation [Cootes and Taylor, 1995, 1999, and 2001]. A nonlinear kNN-classifier is used, instead of the linear Mahalanobis distance, to find optimal displacements for landmarks. For each of the landmarks that describe the shape, at each resolution level taken into account during the segmentation optimization procedure, a distinct set of optimal features is determined. The selection of features is automatic, using the training images and sequential feature forward and backward selection. The new approach is tested on synthetic data and in four medical segmentation tasks: segmenting the right and left lung fields in a database of 230 chest radiographs, and segmenting the cerebellum and corpus callosum in a database of 90 slices from MRI brain images. In all cases, the new method produces significantly better results in terms of an overlap error measure ( p < 0.001 using a paired T-test) than the original active shape model scheme.

AbstractÐA method for deformable shape detection and recognition is described. Deformable shape templates are used to partition the image into a globally consistent interpretation, determined in part by the minimum description length principle. Statistical shape models enforce the prior probabilities on global, parametric deformations for each object class. Once trained, the system autonomously segments deformed shapes from the background, while not merging them with adjacent objects or shadows. The formulation can be used to group image regions obtained via any region segmentation algorithm, e.g., texture, color, or motion. The recovered shape models can be used directly in object recognition. Experiments with color imagery are reported. Index TermsÐImage segmentation, region merging, object detection and recognition, deformable templates, nonrigid shape models, statistical shape models. 1

We present ratio contour, a novel graph-based method for extracting salient closed boundaries from noisy images. This method operates on a set of boundary fragments that are produced by edge detection. Boundary extraction identifies a subset of these fragments and connects them sequentially to form a closed boundary with the largest saliency. We encode the Gestalt laws of proximity and continuity in a novel boundary-saliency measure based on the relative gap length and average curvature when connecting fragments to form a closed boundary. This new measure attempts to remove a possible bias toward short boundaries. We present a polynomial-time algorithm for finding the most-salient closed boundary. We also present supplementary preprocessing steps that facilitate the application of ratio contour to real images. We compare ratio contour to two closely related methods for extracting closed boundaries: Elder and Zucker's method based on the shortest-path algorithm and Williams and Thornber's method based on spectral analysis and a strongly-connected-components algorithm. This comparison involves both theoretic analysis and experimental evaluation on both synthesized data and real images.

The paper proposes a novel, pose-invariant face recogI#TfA system based on a deformable,geform 3D face model, that is a composite of: (1) anedg model, (2) a color regrf model and (3) a wireframe model for jointlydescribing the shape and important features of the face. The #rst two submodels are used forimag analysis and the third mainly for face synthesis. In order to match the model to faceimagy in arbitrary poses, the 3D model can be projected onto di#erent 2D viewplanes based on rotation, translation and scale parameters, therebygrebyf:Ik multipleface-imag templates (in di#erent sizes and orientations). Face shape variationsamong people are taken into account by the deformation parameters of the model. Given an unknown face, its pose is estimated by modelmatching and the system synthesizes faceimagj of known subjects in the same pose. The face is then classi#ed as the subject whose synthesizedimag is most similar. The synthesizedimagh are gref#k#j using a 3D face representation scheme which encodes the 3D shape and texture characteristics of the faces. This face representation is automatically derived fromtraining faceimag: of the subject. Experimental results show that the method is capable ofdetermining pose and recog##fA: faces accurately over a wide rang ofposes and with naturallyvarying liging conditions. Recogions. rates of92.3% have been achieved by the method with 10training faceimagk per person.

This paper describes a new approach to adaptive estimation of parametric deformable contours based on B-spline representations. The problem is formulated in a statistical framework with the likelihood function being derived from a region -based image model. The parameters of the image model, the contour parameters, and the B-spline parameterization order (i.e., the number of control points) are all considered unknown. The parameterization order is estimated via a minimum description length (MDL) type criterion. A deterministic iterative algorithm is developed to implement the derived contour estimation criterion. The result is an unsupervised parametric deformable contour: it adapts its degree of smoothness/complexity (number of control points) and it also estimates the observation (image) model parameters. The experiments reported in the paper, performed on synthetic and real (medical) images, confirm the adequacy and good performance of the approach.

One of the main challenges faced by the current face recognition techniques lies in the difficulties of collecting samples. Fewer samples per person mean less laborious effort for collecting them, lower costs for storing and processing them. Unfortunately, many reported face recognition techniques rely heavily on the size and representative of training set, and most of them will suffer serious performance drop or even fail to work if only one training sample per person is available to the systems. This situation is called “one sample per person ” problem: given a stored database of faces, the goal is to identify a person from the database later in time in any different and unpredictable poses, lighting, etc from just one image. Such a task is very challenging for most current algorithms due to the extremely limited representative of training sample. Numerous techniques have been developed to attack this problem, and the purpose of this paper is to categorize and evaluate these algorithms. The prominent algorithms are described and critically analyzed. Relevant issues such as data collection, the influence of the small sample size, and system evaluation are discussed, and several promising directions for future research are also proposed in this paper.

Abstract not found

Retrieval efficiency and accuracy are two important issues in designing a content-based database retrieval system. We propose a new image database retrieval method based on shape information. This system achieves both the desired efficiency and accuracy using a two-stage hierarchy: in the first stage, simple and easily computable statistical shape features are used to quickly browse through the database to generate a moderate number of plausible retrievals; in the second stage, the outputs from the first stage are screened using a deformable template matching process to discard spurious matches. We have tested the algorithm using hand drawn queries on a trademark database containing 1; 100 images. Each retrieval takes a reasonable amount of computation time. The top most retrieved image from the system agrees with that obtained by human subjects, but there are significant differences between the top 10 retrieved images by our system and that provided by human subjects. This demonstra...