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We present a convex variational active contour model with shape priors, for spatio-temporal segmentation of the endocardium in 2D B-mode ultrasound sequences, which can be solved by Continuous Cuts. A four component (signal dropout, echocardiographic artifacts, blood and tissue) Rayleigh mixture model is proposed for modeling the inside and outside of the endocardium. The parameters of the mixture...
We develop a new algorithm to segment the hippocampus from MR images. Our method uses a new classifier ensemble algorithm to correct segmentation errors produced by a multi-atlas based segmentation method. Our classifier ensemble algorithm searches for the maximum likelihood solution via gradient ascent optimization. Compared to the additive regression based algorithm, LogitBoost, our algorithm avoids...
In this paper, we propose a new segmentation algorithm that combines a graph-based shape model with image cues based on boosted features. The landmark-based shape model encodes prior constraints through the normalized Euclidean distances between pairs of control points, alleviating the need of a large database for the training. Moreover, the graph topology is deduced from the dataset using manifold...
We introduce a 3D segmentation framework which uses principal shapes. The probabilistic energy function of the method is defined based on intensity, tissue type, and location information of the structures using a multiple atlas method. For intensity information, nonparametric probability density function is used which considers intensity relation of different structures. To find a local minimum of...
Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. As a result, machine learning is frequently used in cancer diagnosis and detection. In this paper, support vector machines, K-nearest...
The registration of multi-modal images is the process of finding a transformation which maps one image to the other according to a given similarity metric. In this paper, we introduce a novel approach for metric learning, aiming to address highly non functional correspondences through the integration of statistical regression and multi-label classification. We developed a position-invariant method...
The method proposed here uses Bayesian non-linear classifier to select optimal subset of attributes to avoid redundant variables and reduce data uncertainty in the classification process often used in medical diagnosis. The method also exploits the structural reasoning ability of Bayesian Networks (BN) to optimize large number of attributes to prevent overfitting, meanwhile it maintains the high classification...
The Common Spatial Pattern (CSP) algorithm is a popular method for efficiently calculating spatial filters. However, several previous studies show that CSP's performance deteriorates especially when the number of channels is large compared to small number of training datasets. As a result, it is necessary to choose an optimal subset of the whole channels to save computational time and retain high...
In this paper, authors propose a new learning based method for medical image retrieval which is based on fusing different features by linearly combining different similarities. Considering the abundant classes of medical images, this paper avoid to train a classifier for each class by using large amount training data. Instead, by using optimization method to combine different features' similarity,...
Training a shape prior has been potent scheme for anatomical object segmentations, especially for images with noisy or weak intensity patterns. When the shape representation lives in a high dimensional space, principal component analysis is often used to calculate a low dimensional variation subspace from frequently limited number of training samples. However, the eigenmodes of the sub-space tend...
Based on the non-homogeneity validation method, the hyper-parameter optimization of general regress model on fMRI brain data is analyzed. To acknowledge the abilities of different regression methods we compare ridge regression, support vector regression and Elman recurrent neural network over several feature rating sequences. The original data is obtained from PBAIC2006. Experiment results show that...
Intravascular ultrasound (IVUS) is a catheter-based medical imaging technique that produces cross-sectional images of blood vessels and is particularly useful for studying atherosclerosis. In this paper, we present a probabilistic approach for the semi-automatic identification of the luminal border on IVUS images. Specifically, we parameterize the lumen contour using a mixture of Gaussian that is...
A computer-aided diagnosis (CADx) system improves the initial and evolutional precision of disease identification, reduces the doctorpsilas level of incertitude regarding some diseases, allows monitoring the health status of the patient during new treatment methods, stores images in digital format and generates diagnoses databases that can be used in research, medical practice and specialized teaching...
We propose an extension of the Filtered Back-Projection (FBP) algorithm for reconstruction of attenuation images in Computed Tomography (CT). In our scheme, the standard filtering of projections with windowed ramp kernel is replaced by an adaptive, spatially-variant linear filter, based on a structured bank of 2D convolution kernels. In addition, the proposed scheme includes a post-processing step...
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