Short communication: The use of Boolean model for texture analysis of grey images
Computer Vision and Image Understanding
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Analysis of Spatially and Temporally Overlapping Events with Application to Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Studying endocytosis in space and time by means of temporal Boolean models
Pattern Recognition
A random set view of texture classification
IEEE Transactions on Image Processing
Non-homogeneous temporal Boolean models to study endocytosis
Pattern Recognition
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Recent advances in microscopy jointly to the development of fluorescent probes have enabled to image dynamic processes with very high spatial-temporal resolution, for instance in Cell Biology. In some applications, the segmented areas associated with different events overlap spatially and temporally forming random clumps. In order to study the shape-size features and durations of the events, it is a usual practice to analyze only isolated episodes. However, this sample is biased, because faster and smaller events tend to be isolated. We model the images as a realization of a cylindrical temporal Boolean model. We evaluate the bias introduced when ruling out non-isolated episodes. We propose an estimator of the duration distribution and perform a simulation study to assess its accuracy. The method is applied to fluorescent-tagged proteins image sequences. Results show that this procedure is effective for analyzing dynamic processes where spatial and temporal overlapping occurs.