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Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is a sensitive tool used for the detection of breast cancer. Automated segmentation of breast lesions in DCE-MR images is challenging due to the inherent low signal-to-noise ratios and high inter- patient variability. A lesion segmentation method based on supervised classification is proposed in this study. In this method, a DCE-MR image...
We propose a region-based segmentation method based on local statistics. The adaptive spatial locality is defined using the Intersection of Confidence Intervals (ICI) approach. This pixel dependent local scale is estimated, conditionally on the current segmentation, in the sense of minimizing the mean-square error of a Local Polynomials Approximation (LPA). In other words, the scale is ‘optimal’ since...
Airport runway debris detection is very important, and result of detection decides security of plane and passengers. Therefore, it will describe the technologies that are available for detecting foreign objects on an airport runway, considering the relative merits of the various types of sensor, and the effects of adverse environmental conditions on their performance, this paper addresses a novel...
Color and texture have been widely used in image segmentation; however, their performance is often hindered by scene ambiguities, overlapping objects, or missing parts. In this paper, we propose an interactive image segmentation approach with shape prior models within a Bayesian framework. Interactive features, through mouse strokes, reduce ambiguities, and the incorporation of shape priors enhances...
In this paper, we present a novel active contour model, in which the traditional gradient descent optimization is replaced by graph cut optimization. The basic idea is to first define an energy function according to curve evolution and then construct a graph with well selected edge weights based on the objective energy function, which is further optimized via graph cut algorithm. In this fashion,...
3D cell culture assays have emerged as the basis of an improved model system for evaluating therapeutic agents, molecular probes, and exogenous stimuli. However, there is a gap in robust computational techniques for segmentation of image data that are collected through confocal or deconvolution microscopy. The main issue is the volume of data, overlapping subcellular compartments, and variation in...
This paper proposes a new iterative approach for digital image matting. It combines pre-segmentation and matting into an unified approach and extracts good matte iteratively within a well-defined Bayesian framework based on a few user strokes on foreground and background regions. This method does not need a well specified trimap, which refers to a pre-segmented image with definitely foreground, definitely...
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