2,947 research outputs found
Template-Cut: A Pattern-Based Segmentation Paradigm
We present a scale-invariant, template-based segmentation paradigm that sets
up a graph and performs a graph cut to separate an object from the background.
Typically graph-based schemes distribute the nodes of the graph uniformly and
equidistantly on the image, and use a regularizer to bias the cut towards a
particular shape. The strategy of uniform and equidistant nodes does not allow
the cut to prefer more complex structures, especially when areas of the object
are indistinguishable from the background. We propose a solution by introducing
the concept of a "template shape" of the target object in which the nodes are
sampled non-uniformly and non-equidistantly on the image. We evaluate it on
2D-images where the object's textures and backgrounds are similar, and large
areas of the object have the same gray level appearance as the background. We
also evaluate it in 3D on 60 brain tumor datasets for neurosurgical planning
purposes.Comment: 8 pages, 6 figures, 3 tables, 6 equations, 51 reference
Deformable Prototypes for Encoding Shape Categories in Image Databases
We describe a method for shape-based image database search that uses deformable prototypes to represent categories. Rather than directly comparing a candidate shape with all shape entries in the database, shapes are compared in terms of the types of nonrigid deformations (differences) that relate them to a small subset of representative prototypes. To solve the shape correspondence and alignment problem, we employ the technique of modal matching, an information-preserving shape decomposition for matching, describing, and comparing shapes despite sensor variations and nonrigid deformations. In modal matching, shape is decomposed into an ordered basis of orthogonal principal components. We demonstrate the utility of this approach for shape comparison in 2-D image databases.Office of Naval Research (Young Investigator Award N00014-06-1-0661
A Framework of Vertebra Segmentation Using the Active Shape Model-Based Approach
We propose a medical image segmentation approach based on the Active Shape Model theory. We apply this method for cervical vertebra detection. The main advantage of this approach is the application of a statistical model created after a training stage. Thus, the knowledge and interaction of the domain expert intervene in this approach. Our application allows the use of two different models, that is, a global one (with several vertebrae) and a local one (with a single vertebra). Two modes of segmentation are also proposed: manual and semiautomatic. For the manual mode, only two points are selected by the user on a given image. The first point needs to be close to the lower anterior corner of the last vertebra and the second near the upper anterior corner of the first vertebra. These two points are required to initialize the segmentation process. We propose to use the Harris corner detector combined with three successive filters to carry out the semiautomatic process. The results obtained on a large set of X-ray images are very promising
From 4D medical images (CT, MRI, and Ultrasound) to 4D structured mesh models of the left ventricular endocardium for patient-specific simulations
With cardiovascular disease (CVD) remaining the primary cause of death worldwide, early detection of CVDs becomes essential. The intracardiac flow is an important component of ventricular function, motion kinetics, wash-out of ventricular chambers, and ventricular energetics. Coupling between Computational Fluid Dynamics (CFD) simulations and medical images can play a fundamental role in terms of patient-specific diagnostic tools. From a technical perspective, CFD simulations with moving boundaries could easily lead to negative volumes errors and the sudden failure of the simulation. The generation of high-quality 4D meshes (3D in space + time) with 1-to-l vertex becomes essential to perform a CFD simulation with moving boundaries. In this context, we developed a semiautomatic morphing tool able to create 4D high-quality structured meshes starting from a segmented 4D dataset. To prove the versatility and efficiency, the method was tested on three different 4D datasets (Ultrasound, MRI, and CT) by evaluating the quality and accuracy of the resulting 4D meshes. Furthermore, an estimation of some physiological quantities is accomplished for the 4D CT reconstruction. Future research will aim at extending the region of interest, further automation of the meshing algorithm, and generating structured hexahedral mesh models both for the blood and myocardial volume
Ball-Scale Based Hierarchical Multi-Object Recognition in 3D Medical Images
This paper investigates, using prior shape models and the concept of ball
scale (b-scale), ways of automatically recognizing objects in 3D images without
performing elaborate searches or optimization. That is, the goal is to place
the model in a single shot close to the right pose (position, orientation, and
scale) in a given image so that the model boundaries fall in the close vicinity
of object boundaries in the image. This is achieved via the following set of
key ideas: (a) A semi-automatic way of constructing a multi-object shape model
assembly. (b) A novel strategy of encoding, via b-scale, the pose relationship
between objects in the training images and their intensity patterns captured in
b-scale images. (c) A hierarchical mechanism of positioning the model, in a
one-shot way, in a given image from a knowledge of the learnt pose relationship
and the b-scale image of the given image to be segmented. The evaluation
results on a set of 20 routine clinical abdominal female and male CT data sets
indicate the following: (1) Incorporating a large number of objects improves
the recognition accuracy dramatically. (2) The recognition algorithm can be
thought as a hierarchical framework such that quick replacement of the model
assembly is defined as coarse recognition and delineation itself is known as
finest recognition. (3) Scale yields useful information about the relationship
between the model assembly and any given image such that the recognition
results in a placement of the model close to the actual pose without doing any
elaborate searches or optimization. (4) Effective object recognition can make
delineation most accurate.Comment: This paper was published and presented in SPIE Medical Imaging 201
Deformable Convolutional Networks
Convolutional neural networks (CNNs) are inherently limited to model
geometric transformations due to the fixed geometric structures in its building
modules. In this work, we introduce two new modules to enhance the
transformation modeling capacity of CNNs, namely, deformable convolution and
deformable RoI pooling. Both are based on the idea of augmenting the spatial
sampling locations in the modules with additional offsets and learning the
offsets from target tasks, without additional supervision. The new modules can
readily replace their plain counterparts in existing CNNs and can be easily
trained end-to-end by standard back-propagation, giving rise to deformable
convolutional networks. Extensive experiments validate the effectiveness of our
approach on sophisticated vision tasks of object detection and semantic
segmentation. The code would be released
Left-ventricle myocardium segmentation using a coupled level-set with a priori knowledge
This paper presents a coupled level-set segmentation of the myocardium of the left ventricle of the heart using a priori information. From a fast marching initialisation, two fronts representing the endocardium and epicardium boundaries of the left ventricle are evolved as the zero level-set of a higher dimension function. We introduce a novel and robust stopping term using both gradient and region-based information. The segmentation is supervised both with a coupling function and using a probabilistic model built from training instances. The robustness of the segmentation scheme is evaluated by performing a segmentation on four unseen data-sets containing high variation and the performance of the segmentation is quantitatively assessed
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