Stochastic Image Segmentation Research Articles

Cardiac image segmentation using generalized polynomial chaos expansion and level set function.

Cardiovascular Magnetic Resonance (CMR) images involves a great amount of uncertainties. Such uncertainties may originate from either intrinsic measurement limitations or heterogeneities among patients. Without properly considering these uncertainties, image analysis may provide inaccurate estimations of cardiac functions, and ultimately lead to false diagnosis and inappropriate treatment strategy. In this work, a stochastic image segmentation algorithm is developed to separate cardiac chambers from the background of CMR images. To account for noise and uncertainties in pixel values, a generalized polynomial chaos (gPC) expansion is integrated with a level set function to dynamically evolve boundaries of cardiac chambers. Two consecutive steps are developed: a deterministic segmentation to identify an immediate neighborhood of boundary, of which pixel values are used to calibrate the gPC model; and a stochastic segmentation applied to the neighborhood region to evolve boundaries of cardiac chambers in a stochastic manner. The proposed method can provide a probabilistic description of the segmented heart boundary, which will greatly improve the reliability of image analysis, and potentially enhanced cardiac function evaluation.

A Generalized Metaphor of Chinese Restaurant Franchise to Fusing Both Panchromatic and Multispectral Images for Unsupervised Classification

Two-step ways are often used for fusing both panchromatic (PAN) and multispectral (MS) images for classification, e.g., classifying MS images sharpened by PAN images or directly pouring fine spatial details of PAN images into a classification result of MS images. In this paper, we present a unified Bayesian framework to iteratively discovering semantic segments from PAN images and allocating cluster labels for the segments using MS images. Specifically, the probabilistic generative process of both PAN and MS images is explained with a generalized metaphor of the Chinese restaurant franchise (CRF) (gCRF), in which the two iterative random processes, i.e., table selection and dish selection , are adapted to discovering semantic segments in PAN images and inferring cluster labels for the discovered segments using MS images, respectively. Our major contributions are twofold: 1) The CRF is generalized into an image fusion framework by elegantly decomposing its two random processes, and 2) the random process of table selection in the CRF is transformed into stochastic image segmentation by enforcing spatial constraints over adjacent pixels. The qualitative analysis of experimental results shows that the gCRF can effectively utilize both the spatial details of the PAN images and the spectral information of the MS images. In terms of quantitative evaluation, the gCRF is comparable with support vector machine-based supervised classification methods.

Improved Stochastic Random Walker Segmentation based on Gaussian Filtering

Image segmentation is the process to capture the object from the background and it is a difficult task when a vision of the object is in stochastic region. Here introduce in this paper extension of stochastic random walker segmentation method. In stochastic random walker segmentation, a weighted graph is built from the image, where the each pixel considered as a node and edge weights depend on the image gradient between the pixels. For given seed regions, the probability are evaluated for a stochastic random walk on this graph starting at a pixel to end in one of the seed regions. The problem associated with existing method is the number of random variable (gray-level value in random order) in stochastic images. These random variables increase the graph sizes of stochastic images. If the graph size will increase, consequently execution time would also increase. To overcome these problems, the proposed “Improved stochastic random walker segmentation based on Gaussian filtering” for stochastic image segmentation. In proposed method Gaussian filter has been used for the removal of uncertain gray level and which in turn reduce the noise level and the resultant graph size of corresponding stochastic image, then apply stochastic random walker segmentation method which may help to decrease the execution time of the segmentation process.

Image block classification using stochastic imagesegmentation

The authors propose a new image block classification method. The proposed algorithm incorporates image context into the classification via pixel-based segmentation. To obtain a segmented image they adopt the stochastic model-based unsupervised image segmentation algorithm. Since the block classifier considers the grey level distribution in the block, it can differentiate edges from textures. Also, since the segmentation is executed independently at each small block, a parallel processor can be applied to obtain a real-time block classification.

A Parallel Algorithm for Stochastic Image Segmentation

A parallel algorithm for syntactic image segmentation is introduced. Stochastic tree grammar is used as a context-generating model. It is shown that when this context-generating process is in the equilibrium state, a matched filter can be designed and applied in parallel to the image. This process can be used for image segmentation in a syntactic pattern recognition system to enhance the performance of the succeeding recognition process.