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Topological features are very seldom exploited in image processing, also due to the complexity of their extraction. When topological features are used, densitometric information are usually not considered at the same time. The simultaneous exploitation of both kinds of features, as proposed in this paper, allows a more appropriate automatic processing of digital images. A novel image segmentation...
In infrared (IR) based non-destructive and evaluation tests (NDT&E) for automated fault detection and identification processes, the segmentation task is a crucial stage. In fact, thermal imaging gives vital condition information of equipment and structures. So, pattern recognition algorithms can perform an accurate diagnosis, through an adequate segmentation. In this paper the Expectation Maximization...
Quantitative analysis of dynamic cardiac PET studies provides useful diagnostic information regarding different heart diseases. To correctly perform the kinetic analysis, tracer time-activity curves (TAC) must be precisely extracted from the chosen tissue on the imaging data. In this work we present an alternative clustering algorithm for segmentation of dynamic studies using a leader-follower clustering...
Conventional kinetic parameter estimation based on compartmental models requires an accurate estimation of arterial blood input function. To avoid invasive blood sampling, an image-derived input function can be obtained by manually defining a Region of Interest. Here we propose a new and simple, iterative method for automatic segmentation and input function calculation of PET cardiac studies using...
This paper presents a graph based scheme for color text recognition in images and videos, which is particularly robust to complex background, low resolution or video coding artifacts. This scheme is based on a novel method named the image text recognition graph (iTRG) composed of five main modules: an image text segmentation module, a graph connection builder module, a character recognition module,...
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