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DSPs See Gains in Their Impact on New Medical Imaging Designs [Special Reports]
IEEE Signal Processing Magazine
Cited by: Papers (3)
Digital signal processing (DSP) is having a major impact on advancing the state of the art of medical imaging. The advantages of DSP are well established: They operate in real time, they're highly reliable, and they are very energy efficient. They're also relatively inexpensive. But the medical imaging market continues to push for more technical innovation. That's putting more focus on higher imag...Show More
Regularization of diffusion tensor field using coupled robust anisotropic diffusion filters
Songyuan Tang;Yong Fan;Hongtu Zhu;Pew-Thian Yap;Wei Gao;Weili Lin;Dinggang Shen
2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Year: 2009 | Conference Paper |
This paper presents a method to simultaneously regularize diffusion weighted images and their estimated diffusion tensors, with the goal of suppressing noise and restoring tensor information. We enforce a data fidelity constraint, using coupled robust anisotropic diffusion filters, to ensure consistency of the restored diffusion tensors with the regularized diffusion weighted images. The filters a...Show More
Fast histogram equalization for medical image enhancement
2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Year: 2008 | Conference Paper |
Cited by: Papers (15)
To overcome the problem that the histogram equalization can fail for discrete images, a local-mean based strict pixel ordering method has been proposed recently, although it is unpractical for 3D medical image enhancement due to its complex computation. In this paper, a novel histogram mapping method is proposed. It uses a fast local feature generation technique to establish a combined histogram t...Show More
Learning-based deformation estimation for fast non-rigid registration
2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Year: 2008 | Conference Paper |
Cited by: Papers (3)
This paper presents a learning-based deformation estimation method for fast non-rigid registration. First, a PCA-based statistical deformation model is constructed using the deformation fields obtained by conventional registration algorithms between a template image and training subject images. Then, the constructed statistical model is used to generate a large number of sample deformation fields ...Show More
This paper presents a learning-based method for deformable registration of magnetic resonance (MR) brain images. There are two novelties in the proposed registration method. First, a set of best-scale geometric features are selected for each point in the brain, in order to facilitate correspondence detection during the registration procedure. This is achieved by optimizing an energy function that ...Show More
Detecting Cognitive States from fMRI Images by Machine Learning and Multivariate Classification
2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06)
Year: 2006 | Conference Paper |
The major obstacle in building classifiers that robustly detect a particular cognitive state across different subjects using fMRI images has been the high inter-subject functional variability in brain activation patterns. To overcome this obstacle, firstly, the brain regions that are relevant to the problem under study are determined from the training data; then, statistical information of each br...Show More
Statistically-Constrained High-Dimensional Warping Using Wavelet-Based Priors
2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06)
Year: 2006 | Conference Paper |
Cited by: Papers (1)
In this paper, a Statistical Model of Deformation (SMD) that captures the statistical prior distribution of highdimensional deformations more accurately and effectively than conventional PCA-based statistical shape models is used to regularize deformable registration. SMD utilizes a wavelet-based representation of statistical variation of a deformation field and its Jacobian, and it is able to cap...Show More
Improve brain registration using machine learning methods
3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006.
Year: 2006 | Conference Paper |
A machine learning method is introduced here to improve the accuracy of brain registration. Generally, different brain regions might need different types or sets of features for registration, which actually can be determined and learned from the brain samples by a machine learning method. In this paper, we focus on investigating the best geometric features required by different brain regions, to m...Show More
Automated segmentation of white matter lesions in 3D brain MR images, using multivariate pattern classification
Zhiqiang Lao;Dinggang Shen;A. Jawad;B. Karacali;Dengfeng Liu;E.R. Melhem;R.N. Bryan;C. Davatzikos
3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006.
Year: 2006 | Conference Paper |
Cited by: Papers (4)
This paper presents a fully automatic white matter lesion (WML) segmentation method, based on local features determined by combining multiple MR acquisition protocols, including T1-weighted, T2-weighted, proton density (PD)-weighted and fluid attenuation inversion recovery (FLAIR) scans. Support vector machines (SVMs) are used to integrate features from these 4 acquisition types, thereby identifyi...Show More
Registration of brain images with tumors: towards the construction of statistical atlases for therapy planning
3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006.
Year: 2006 | Conference Paper |
Cited by: Papers (3)
A deformable registration method is proposed to register a brain atlas with tumor-bearing brain scans. The tumor mass effect is first simulated in the (normal) atlas, using a biomechanical model of mass effect. The tumor-bearing atlas is subsequently warped to the patient's scan by a deformable registration method, built upon the idea of HAMMER registration algorithm developed for normal brains. T...Show More
Deformable segmentation of 3-D ultrasound prostate images using statistical texture matching method
This paper presents a novel deformable model for automatic segmentation of prostates from three-dimensional ultrasound images, by statistical matching of both shape and texture. A set of Gabor-support vector machines (G-SVMs) are positioned on different patches of the model surface, and trained to adaptively capture texture priors of ultrasound images for differentiation of prostate and nonprostat...Show More
Morphological classification of medical images using nonlinear support vector machines
C. Davatzikos;Dinggang Shen;Zhiqiang Lao;Zhong Xue;B. Karacali
2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821)
Year: 2004 | Conference Paper |
Cited by: Papers (1)
The wavelet decomposition of a high-dimensional shape transformation posed in a mass-preserving framework is used as a morphological signature of a brain image. Population differences with complex spatial patterns are then determined by applying a nonlinear support vector machine pattern classification method to the morphological signatures. By considering measurements from the entire image, and n...Show More
4D image warping for measurement of longitudinal brain changes
2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821)
Year: 2004 | Conference Paper |
This paper presents a method for robustly measuring temporal morphological brain changes, by means of a 4D image warping mechanism. Longitudinal stability is achieved by considering all temporal MR images of an individual simultaneously in image warping, rather than by individually warping a 3D template to an individual, or by warping the images of one time-point to those of another time-point. Mo...Show More
Determining correspondence in 3-D MR brain images using attribute vectors as morphological signatures of voxels
Finding point correspondence in anatomical images is a key step in shape analysis and deformable registration. This paper proposes an automatic correspondence detection algorithm for intramodality MR brain images of different subjects using wavelet-based attribute vectors (WAVs) defined on every image voxel. The attribute vector (AV) is extracted from the wavelet subimages and reflects the image s...Show More
Segmentation of prostate boundaries from ultrasound images using statistical shape model
Presents a statistical shape model for the automatic prostate segmentation in transrectal ultrasound images. A Gabor filter bank is first used to characterize the prostate boundaries in ultrasound images in both multiple scales and multiple orientations. The Gabor features are further reconstructed to be invariant to the rotation of the ultrasound probe and incorporated in the prostate model as im...Show More
Hierarchical active shape models, using the wavelet transform
Active shape models (ASMs) are often limited by the inability of relatively few eigenvectors to capture the full range of biological shape variability. This paper presents a method that overcomes this limitation, by using a hierarchical formulation of active shape models, using the wavelet transform. The statistical properties of the wavelet transform of a deformable contour are analyzed via princ...Show More
A B-snake model using statistical and geometric information-applications to medical images
7th International Conference on Control, Automation, Robotics and Vision, 2002. ICARCV 2002.
Year: 2002 | Conference Paper |
A B-snake model using statistics information for segmenting 2D objects from medical images is presented in this paper. Based on our previous research work, a statistical model is proposed for our B-snake model, in order to use available priori knowledge about the object shape being studied. This method allows the deformation of B-snake to be influenced primarily by the most reliable matches. Exper...Show More
A novel Bayesian shape model for facial feature extraction
7th International Conference on Control, Automation, Robotics and Vision, 2002. ICARCV 2002.
Year: 2002 | Conference Paper |
Cited by: Papers (1)
This paper presents a novel application of the Bayesian shape model (BSM) for facial feature extraction. First, a full-face model is designed to describe the shape of a face, and the PCA is used to estimate the shape variance of the face model. Then, the BSM is applied to match and extract the face patch from input face images. Finally, using the face model, the extracted face patches are easily w...Show More
HAMMER: hierarchical attribute matching mechanism for elastic registration
A new approach is presented for elastic registration of medical images, and is applied to magnetic resonance images of the brain. Experimental results demonstrate very high accuracy in superposition of images from different subjects. There are two major novelties in the proposed algorithm. First, it uses an attribute vector, i.e., a set of geometric moment invariants (GMIs) that are defined on eac...Show More
Statistically-based reorientation of diffusion tensor field
Proceedings IEEE International Symposium on Biomedical Imaging
Year: 2002 | Conference Paper |
Cited by: Papers (1)
Presents a new method for warping of diffusion tensor fields. The proper reorientation of a tensor field requires knowledge of the underlying fiber orientation, which is not known a priori. Accordingly, a probabilistic representation of the fiber direction is adopted and used in a Procrustean estimation of the rotational component of the warping field. The estimated rotational component, along wit...Show More
Automatic segmentation of white matter lesions in T1-weighted brain MR images
Songyang Yu;D.L. Pham;Dinggang Shen;E.H. Herskovits;S.M. Resnick;C. Davatzikos
Proceedings IEEE International Symposium on Biomedical Imaging
Year: 2002 | Conference Paper |
Cited by: Papers (3)
White matter lesions are common brain abnormalities. In this paper, an automatic method for segmentation of white matter lesions in T1-weighted brain magnetic resonance (MR) images is presented. A subject's T1-weighted MR image is first segmented into the three major tissue types, white matter (WM), gray matter (GM) and cerebral spinal fluid (CSF) solely based on each voxel's intensity. Since WM l...Show More
Statistical shape model for automatic skull-stripping of brain images
Proceedings IEEE International Symposium on Biomedical Imaging
Year: 2002 | Conference Paper |
Presents a statistical shape model for automatic skull stripping of MR brain images. A surface model of the brain boundary is hierarchically represented by a set of overlapping surface patches, each of which has elastic properties and a deformation range that is learned from a training set. The model's deformation is hierarchical which adds robustness to local minima. Moreover, the deformation of ...Show More
A framework for modeling and predicting anatomical deformations is presented, and tested on simulated images. Although a variety of deformations can be modeled in this framework, emphasis is placed on surgical planning, and particularly on modeling and predicting changes of anatomy between preoperative and intraoperative positions, as well as on deformations induced by tumor growth. Two methods ar...Show More
Statistically optimized biopsy strategy for the diagnosis of prostate cancer
Dinggang Shen;Zhiqiang Lao;Jianchao Zeng;E.H. Herskovits;G. Fichtinger;C. Davatzikos
Proceedings 14th IEEE Symposium on Computer-Based Medical Systems. CBMS 2001
Year: 2001 | Conference Paper |
Presents a method for optimizing prostate needle biopsy, by creating a statistical atlas of the spatial distribution of prostate cancer from a large patient cohort. In order to remove inter-individual morphological variability and to determine the true variability in the spatial distribution of cancer within the prostate, an adaptive-focus deformable model (AFDM) is first used to register and norm...Show More
Structure-adaptive B-snake for segmenting complex objects
Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205)
Year: 2001 | Conference Paper |
Cited by: Papers (10)
In this paper, we presented a structure-adaptive B-snake model for segmenting the complex structures in medical images. A strategy of automatic control-point insertion, adaptive to the structure of the studied object, has been proposed. Furthermore, a method of minimum mean square energy (MMSE) is developed to iteratively estimate the position of those control points in the B-snake model. By apply...Show More
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