Unsupervised cell segmentation and labelling in neural tissue images

Neurodegenerative diseases are a group of largely incurable disorders characterised by the progressive loss of neurons and for which often the molecular mechanisms are poorly understood. To bridge this gap, researchers employ a range of techniques. A very prominent and useful technique adopted across many different fields is imaging and the analysis of histopathological and fluorescent label tissue samples. Although image acquisition has been efficiently automated recently, automated analysis still presents a bottleneck. Although various methods have been developed to automate this task, they tend to make use of single-purpose machine learning models that require extensive training, imposing a significant workload on the experts and introducing variability in the analysis. Moreover, these methods are impractical to audit and adapt, as their internal parameters are difficult to interpret and change. Here, we present a novel unsupervised automated schema for object segmentation of images, exemplified on a dataset of tissue images. Our schema does not require training data, can be fully audited and is based on a series of understandable biological decisions. In order to evaluate and validate our schema, we compared it with a state-of-the-art automated segmentation method for post-mortem tissues of ALS patients

Transition thresholds and transition operators for binarization and edge detection

The transition method for image binarization is based on the concept of t-transition pixels, a generalization of edge pixels, and t-transition sets. We introduce a novel unsupervised thresholding for unimodal histograms to estimate the transition sets. We also present dilation and incidence transition operators to refine the transition set. Afterward, we propose the simple edge transition operator for detecting edges. Our experiments show that the new approach increases the effectiveness of OCR applications outperforming several top-ranked binarization algorithms. � 2010 Elsevier Ltd. All rights reserved

Transition thresholds and transition operators for binarization and edge detection

Acoustic harassment and deterrent devices have become increasingly popular mitigation tools for negotiating the impacts of marine mammals on fisheries. The rationale for their variable effectiveness remains unexplained, but high variability in the surrounding acoustic field may be relevant. In the present study, the sound fields of one acoustic harassment device and three acoustic deterrent devices were measured at three study sites along the Scandinavian coast. Superimposed onto an overall trend of decreasing sound exposure levels with increasing range were large local variations in the sound level for all sources in each of the environments. This variability was likely caused by source directionality, inter-ping source level variation and multipath interference. Rapid and unpredictable variations in the sound level as a function of range deviated from expectations derived from spherical and cylindrical spreading models and conflicted with the classic concept of concentric zones of increasing disturbance with decreasing range. Under such conditions, animals may encounter difficulties when trying to determine the direction to and location of a sound source, which may complicate or jeopardize avoidance responses. " 2008 by the Society for Marine Mammalogy.",,,,,,"10.1111/j.1748-7692.2008.00243.x",,,"http://hdl.handle.net/20.500.12104/45450","http://www.scopus.com/inward/record.url?eid=2-s2.0-58349099098&partnerID=40&md5=2155719fc067f3fa33d0fa7fc0663403",,,,,,"1",,"Marine Mammal Science",,"5