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In this paper, we present a novel methodology for computing statistical shape models (SSM's) by leveraging the medial axis model to determine shape variations between objects. Landmark based SSM's (LSSM's) are a popular approach to describing valid shape variation in an object of interest by applying principal component analysis to a set of landmarks on the surface of the object. However, defining...
We present a method for prediction of atherosclerotic growth based on a training set of 229 2D manually annotated baseline and corresponding follow-up calcifications from lateral X-ray images over an 8 year period. The prediction uses affine shape analysis based on singular value decomposition where non-rigid shapes are modeled as projections of rigid high-dimensional shapes. The SVD prediction was...
In this paper, a novel tree-based registration framework is proposed for achieving fast and accurate registration by providing a more appropriate initial deformation field for the image under registration. Specifically, in the training stage, all training real images and a selected portion of simulated images are organized into a combinative tree with the template as the root, and then each training...
Image-based morphometry of cells, tissues, and organs is an important topic in biomedical image analysis. We propose a novel method to characterize the morphological information that discriminates between two populations of morphological exemplars (cells, organs). We first demonstrate that the application of standard techniques such as Fisher linear discriminant analysis (FDA) can lead to undesirable...
A general framework is proposed for solving groupwise pose normalization problems and is analyzed in detail under different feature spaces. The analysis shows that using principal component analysis for pose normalization is a special case of using the proposed framework under a special feature space. The experimental results on two cranio-facial datasets show the proposed method achieved promising...
According to the level of information provided in images, segmentation techniques can be categorized into two groups. One is region-labeling, which obeys the intensity-based classification methods. Although modeling the tissue intensity is straightforward by applying local statistical methods and spatial dependencies, the results might suffer from noise and incomplete data. The second group of techniques...
This study focuses on quantification of probe concentration in major organs of small animal micro-PET images. In order to delineate organ ROIs, a statistical mouse atlas is registered to the micro-PET image. This statistical atlas is trained from 22 organ-labeled micro-CT images using Principle Component Analysis (PCA). By tuning the shape-controlling parameters of the atlas, we are able to adapt...
In this paper, we present a 3D X-Ray Transform based feature extraction and classification method for Digital Multi-focal Images (DMI). In such images, morphological information for a transparent specimen can be captured in the form of a stack of high-quality images, representing individual focal planes through the specimen's body. We present a method that can effectively exploit the entire information...
In this paper we propose a supervised 3D segmentation algorithm to locate the esophagus in thoracic CT scans using a variational framework. To address challenges due to low contrast, several priors are learned from a training set of segmented images. Our algorithm first estimates the centerline based on a spatial model learned at a few manually marked anatomical reference points. Then an implicit...
In this paper, a statistical texture modeling method is proposed for medical volumes. As the shapes of the human organ are very different from one case to another, 3D volume morphing is applied to normalize all the volume datasets to a same shape for removing shape variations. In order to deal with the problems of high-dimension and small number of medial samples, we propose an effective image compression...
We present a general methodology that aims to learn multi-variate statistics of high dimensional images, in order to capture the inter-individual variability of imaging data from a limited number of training images. The statistical learning procedure is used for identifying abnormalities as deviations from the normal variation. In most practical applications, learning an accurate statistical model...
The diagnosis of left ventricular mechanical dyssynchrony (LVMD) and identifying cardiac resynchronization therapy (CRT) candidates are challenging problems due to the limitation of the currently applied regional volume-curve analysis. In this study, four-dimensional (4D, 3D+time) left ventricle (LV) regional shape models of 26 LVMD patients were constructed from pre- and/or post- CRT real-time 3D...
We propose a shape-based variational framework to curve evolution for the segmentation of tongue contours from MRI mid-sagittal images. In particular, we first build a PCA model on tongue contours of different articulations of a reference speaker, and use it as shape priors. The parameters of the curve representation are then manipulated to minimize an objective function. The designed energy integrates...
In this paper, we report our current progress results on computer assisted diagnostic (CAD) system, which consists of three units: database unit (statistical atlas of human anatomy), image processing unit (image enhancement, image segmentation, image registration), and visualization unit (volume rendering). In the database unit, we proposed a new method called generalized N-dimensional principal component...
In computational anatomy, statistical shape model is used for quantitative evaluation of the variations of an organ shape. This paper is focused on construction of Statistical Shape Model of the liver and its application to computer assisted diagnosis. We prove the potential application of statistical shape models in classification of normal and cirrhosis livers. First, statistical shape model of...
X-ray image segmentation is an important issue in medical image analysis. Due to inconsistent X-ray absorption, the intensities are usually unevenly distributed and noisy in the processed organ, thus the object segmentation becomes difficult. In this paper we propose a new segmentation method for patella from the lateral knee X-ray images based on the active shape model (ASM). At first, a patella...
Statistical shape model (SSM) is to model the shape variation of an object. In this paper, we propose an efficient shape representation method and a new 2D-PCA based statistical shape modeling. In our proposed method, we used the radii of these surface points as shape feature instead of their coordinates, and the shape is represented by a 2D matrices. We then apply 2D-PCA to construct a statistical...
Finding point correspondences plays an important role in automatically building statistical shape models from a training set of 3D surfaces. Davies et al. assumed the projected coefficients have a multivariate Gaussian distributions and derived an objective function for the point correspondence problem that uses minimum description length to balance the training errors and generalization ability....
Many segmentation problems in medical imaging rely on accurate modeling and estimation of tissue intensity probability density functions. Gaussian mixture modeling, currently the most common approach, has several drawbacks, such as reliance on a specific model and iterative optimization. It also does not take advantage of substantially larger amount of data provided by 3D acquisitions, which are becoming...
Statistical shape model (SSM) is to model the shape variation of an object. The statistical shape models are constructed by analysis of the positions of a set of landmark points based and use the surface information. In this paper, we propose a new PCA based statistical shape modeling technique and its application to medical applications. In the proposed method, boundary points of each slice are used...
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