KEYWORDS: Image segmentation, Magnetic resonance imaging, Tissues, Brain, 3D modeling, 3D image processing, Data modeling, Neuroimaging, Optical engineering, Cerebral cortex
KEYWORDS: Image segmentation, RGB color model, Data modeling, Image processing algorithms and systems, Color image processing, Image processing, Chromium, Signal to noise ratio, Electronic imaging, Image analysis
An adaptive Bayesian segmentation algorithm for color images is presented, which extends the adaptive clustering approach of Pappas to multichannel images. A scalar segmentation label field is generated for the multichannel data, which is modeled as a vector field, where the components of the vector field (each individual channel) are assumed to be conditionally independent given the segmentation labels. The class conditional probability model for the vector image field is taken as a multivariate Gaussian with a space-varying mean function. A Gibbs random field is employed as the a priori probability model for the segmentation label field that imposes a spatial connectivity constraint on the labels. The space-varying class means associated with the image segments can be used to form an estimate of the actual image from noisy observations. Experimental results are provided to demonstrate the benefits of using adaptivity via the space-varying means and the spatial connectivity constraint. We also discuss the effects of the color space within which the clustering is performed on resulting segmentations.
KEYWORDS: Image segmentation, Image processing algorithms and systems, RGB color model, Color image segmentation, Data modeling, Image analysis, Image processing, Color image processing, Multispectral imaging, Signal to noise ratio
A Bayesian segmentation algorithm to separate color images into regions of distinct colors is presented. The algorithm takes into account the local color variations in the image in an adaptive manner. A Gibbs random field (GRF) is used as the a priori probability model for the segmentation process to impose a spatial connectivity constraint. We study the performance of the proposed algorithm in different color spaces and its application in reduced data rendering of color images. Experimental results and discussion are included.
KEYWORDS: Image segmentation, 3D modeling, 3D image processing, Magnetic resonance imaging, Tissues, Data modeling, Image processing algorithms and systems, Video processing, 3D displays, Neuroimaging
A Bayesian approach for segmentation of three-dimensional (3-D) magnetic resonance imaging (MRI) data of the human brain is presented. Connectivity and smoothness constraints are imposed on the segmentation in 3 dimensions. The resulting segmentation is suitable for 3-D display and for volumetric analysis of structures. The algorithm is based on the maximum a posteriori probability (MAP) criterion, where a 3-D Gibbs random field (GRF) is used to model the a priori probability distribution of the segmentation. The proposed method can be applied to a spatial sequence of 2-D images (cross-sections through a volume), as well as 3-D sampled data. We discuss the optimization methods for obtaining the MAP estimate. Experimental results obtained using clinical data are included.
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