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Boosting is a versatile machine learning technique that has numerous applications including but not limited to image processing, computer vision, data mining etc. It is based on the premise that the classification performance of a set of weak learners can be boosted by some weighted combination of them. There have been a number of boosting methods proposed in the literature, such as the AdaBoost,...
In multi-atlas based segmentation, a new image is segmented by registering multiple atlas images and propagating the corresponding atlas segmentations. These propagated segmentations are then combined in a process called label fusion. This paper presents a new, local method that divides the propagated segmentations in multiple, user-definable regions. A label fusion process can then be applied to...
We describe a scatter and randoms weighted (SRW) iterative PET reconstruction algorithm. The SRW method is based on the estimation of the trues fraction (TF) within the prompts. Once the TF is estimated, it is then incorporated into the weighting component of the system matrix, and the net result is a scatter and randoms weighting in the sensitivity image similar to the attenuation correction weighting...
In radiotherapy, patient setup error is one of the important factors that impact the final therapeutic effect. To help operators correct the patient setup accurately, this paper proposes a method to real-time calculate the offset of patient's positions automatically. In this method, the mean of Graphics Processing Unit (GPU) is employed to help fast generate the digitally reconstructed radiographs...
We address the problem of building detailed models of the shape and appearance of complex structures, given only a training set of representative images and some minimal manual intervention. We focus on objects with repeating structures (such as bones in the hands), which can cause normal deformable registration techniques to fall into local minima and fail. Using a sparse annotation of a single image...
Color variation in medical images degrades the classification performance of computer aided diagnosis systems. Traditionally, color segmentation algorithms mitigate this variability and improve performance. However, consistent and robust segmentation remains an open research problem. In this study, we avoid the tenuous phase of color segmentation by adapting a bag-of-features approach using scale...
We consider the parallel interpretation of relaxation method for algebraic reconstruction of tomographic images and a mathematical model of method parallel interpretation and its advantages over direct reconstruction methods.
Artificial Neural Networks (ANN) is gaining significant importance for pattern recognition applications particularly in the medical field. A hybrid neural network such as Counter Propagation Neural Network (CPN) is highly desirable since it comprises the advantages of supervised and unsupervised training methodologies. Even though it guarantees high accuracy, the network is computationally non-feasible...
An improved iterative quadtree decomposition (IQD) algorithm is proposed: starting from a seed point or a ranking order of liver area, a segmentation result of liver in MR image is obtained by a quadtree decomposition, regional morphology operation and ordering of ROI. The IQD algorithm overcomes unfavorable condition of small proportion of liver area in the MR image which makes the segmentation difficult...
We describe an evaluation of an iterative cascade gamma ray correction algorithm which was developed to improve the quantitative accuracy of small animal PET imaging with non-standard PET nuclides. The cascade correction algorithm uses the emission image and the attenuation map of the object to compute the shape or the spatial distribution of the coincidences caused by the cascade gamma rays, and...
Brain-computer interface (BCI) is a system that allows its users to control external devices with brain activity. This paper presents a new method for classifying the off-line experimental electroencephalogram (EEG) signals from the BCI Competition 2003..which achieved higher accuracy. The method has three main steps. First, wavelet coefficient was reconstructed by using wavelet transform in order...
We recently introduced a family of optimized interpolators to approximate the non-uniform Fourier transform of a finitely supported function. Theoretical comparisons indicated a significant improvement in performance over conventional approximations. In this paper, we study the utility of this approximation in the inversion of non-Cartesian MRI data. Using numerical simulations, we show that the new...
This paper presents a 3D non-rigid registration algorithm between histological and MR images of the prostate with cancer. To compensate for the loss of 3D integrity in the histology sectioning process, series of 2D histological slices are first reconstructed into a 3D histological volume. After that, the 3D histology-MRI registration is obtained by maximizing a) landmark similarity and b) cancer region...
In computational anatomy variability among medical images is encoded by a large deformation diffeomorphic mapping matching each instance with a template. The set of diffeomorphisms is usually endowed with a Riemannian manifold structure and parameterized by non-stationary velocity vector fields. An alternative parameterization based on stationary vector fields has been proposed, where paths of diffeomorphisms...
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