216 research outputs found
ROC performance evaluation of RADSPM technique
The purpose of Functional Magnetic Resonance Imaging (fMRI) is to map areas of increased neuronal activity of the human brain. fMRI has been applied to investigate a variety of neuronal processes from activities in the primary sensory and motor cortices to cognitive functions such as perception or learning. Robust anisotropic diffusion of statistical parametric maps (RADSPM) is a new technique to improve functional Magnetic Resonance Imaging. RADSPM attempts to improve voxel classification based on robust anisotropic diffusion (RAD) to include the spatial relationship between active voxels. This paper compares two fMRI postprocessing techniques used to identify areas of increased neuronal activity, a widely used method, correlation analysis, and RADSPM.
In recent years, the use of ROC analysis has been extended from its original use in communication systems to machine learning, pattern classification and fMRI. We proposed to use ROC curves and the area under the curve (AUC) not only as a final performance evaluation and visualizing technique but as a gauging parameter procedure in RADSPM.
We give a brief review of the main methods and conclude presenting experimental results and suggesting further research alternatives.Workshop de Computación Gráfica, Imágenes y Visualización (WCGIV)Red de Universidades con Carreras en Informática (RedUNCI
ROC performance evaluation of RADSPM technique
The purpose of Functional Magnetic Resonance Imaging (fMRI) is to map areas of increased neuronal activity of the human brain. fMRI has been applied to investigate a variety of neuronal processes from activities in the primary sensory and motor cortices to cognitive functions such as perception or learning. Robust anisotropic diffusion of statistical parametric maps (RADSPM) is a new technique to improve functional Magnetic Resonance Imaging. RADSPM attempts to improve voxel classification based on robust anisotropic diffusion (RAD) to include the spatial relationship between active voxels. This paper compares two fMRI postprocessing techniques used to identify areas of increased neuronal activity, a widely used method, correlation analysis, and RADSPM.
In recent years, the use of ROC analysis has been extended from its original use in communication systems to machine learning, pattern classification and fMRI. We proposed to use ROC curves and the area under the curve (AUC) not only as a final performance evaluation and visualizing technique but as a gauging parameter procedure in RADSPM.
We give a brief review of the main methods and conclude presenting experimental results and suggesting further research alternatives.Workshop de Computación Gráfica, Imágenes y Visualización (WCGIV)Red de Universidades con Carreras en Informática (RedUNCI
Adaptive pre-filtering techniques for colour image analysis
One important step in the process of colour image
segmentation is to reduce the errors caused by image
noise and local colour inhomogeneities. This can be
achieved by filtering the data with a smoothing
operator that eliminates the noise and the weak
textures. In this regard, the aim of this paper is to
evaluate the performance of two image smoothing
techniques designed for colour images, namely
bilateral filtering for edge preserving smoothing and
coupled forward and backward anisotropic diffusion
scheme (FAB). Both techniques are non-linear and
have the purpose of eliminating the image noise,
reduce weak textures and artefacts and improve the
coherence of colour information. A quantitative
comparison between them will be evaluated and also
the ability of such techniques to preserve the edge
information will be investigated
Segmenting neurons in electronic microscopy via geometric tracing
PostprintDescribes a system that is being used for the segmentation of neurons in images obtained from electronic microscopy. These images are extremely noisy, and ordinary active contours techniques detect spurious objects and fail to detect the neuron boundaries. The algorithm here described is based on combining robust anisotropic diffusion with minimal weighted-path computations. After the image is regularized via anisotropic diffusion, the user clicks points on the boundary of the desired object, and the algorithm completes the boundary between those points. This tracing is based on computing paths of minimal weighted distance, where the weight is given by the image edge content. Thanks to advanced numerical algorithms, the algorithm is very fast and accurate. The authors compare their results with those obtained with PictureIt, a commercially available general purpose image processing package developed by Microsoft
Discontinuity Preserving Noise Removal Method based on Anisotropic Diffusion for Band Pass Signals
nonlinear discontinuity-preserving method for noise removal for band pass signals such as signals modulated with Binary Phase-Shift Keying (BPSK) modulation is proposed in this paper. This method is inspired by the anisotropic diffusion algorithm to remove noise and preserve discontinuities in band pass signals modulated with a single frequency. It is demonstrated here that nonlinear noise removal method for a real valued band pass signal requires a solution for a nonlinear partial differential equation which is of fourth order in space and second order in time. The results presented in this work show better performance in nonlinear noise removal for real valued band pass signals in comparison with the previous work in the literature
Color image segmentation using a self-initializing EM algorithm
This paper presents a new method based on the Expectation-Maximization (EM) algorithm that we apply for color image segmentation. Since this algorithm partitions the data based on an initial set of mixtures, the color segmentation provided by the EM algorithm is highly dependent on the starting condition (initialization stage). Usually the initialization procedure selects the color seeds randomly and often this procedure forces the EM algorithm to converge to numerous local minima and produce inappropriate results. In this paper we propose a simple and yet effective solution to initialize the EM algorithm with relevant color seeds. The resulting self initialised EM algorithm has been included in the development of an adaptive image segmentation scheme that has been applied to a large number of color images. The experimental data indicates that the refined initialization procedure leads to improved color segmentation
A Convex Model for Edge-Histogram Specification with Applications to Edge-preserving Smoothing
The goal of edge-histogram specification is to find an image whose edge image
has a histogram that matches a given edge-histogram as much as possible.
Mignotte has proposed a non-convex model for the problem [M. Mignotte. An
energy-based model for the image edge-histogram specification problem. IEEE
Transactions on Image Processing, 21(1):379--386, 2012]. In his work, edge
magnitudes of an input image are first modified by histogram specification to
match the given edge-histogram. Then, a non-convex model is minimized to find
an output image whose edge-histogram matches the modified edge-histogram. The
non-convexity of the model hinders the computations and the inclusion of useful
constraints such as the dynamic range constraint. In this paper, instead of
considering edge magnitudes, we directly consider the image gradients and
propose a convex model based on them. Furthermore, we include additional
constraints in our model based on different applications. The convexity of our
model allows us to compute the output image efficiently using either
Alternating Direction Method of Multipliers or Fast Iterative
Shrinkage-Thresholding Algorithm. We consider several applications in
edge-preserving smoothing including image abstraction, edge extraction, details
exaggeration, and documents scan-through removal. Numerical results are given
to illustrate that our method successfully produces decent results efficiently
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