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The process of Nuclei detection in high-grade breast cancer images is quite challenging in the case of image processing techniques due to certain heterogeneous characteristics of cancer nuclei such as enlarged and irregularly shaped nuclei, highly coarse chromatin marginalized to the nuclei periphery and visible nucleoli. Recent reviews state that existing techniques show appreciable segmentation accuracy on breast histopathology images whose nuclei are dispersed and regular in texture and shape; however, typical cancer nuclei are often clustered and have irregular texture and shape properties. This paper proposes a novel segmentation algorithm for detecting individual nuclei from Hematoxylin and Eosin (H&E) stained breast histopathology images. This detection framework estimates a nuclei saliency map using tensor voting followed by boundary extraction of the nuclei on the saliency map using a Loopy Back Propagation (LBP) algorithm on a Markov Random Field (MRF). The method was tested on both whole-slide images and frames of breast cancer histopathology images. Experimental results demonstrate high segmentation performance with efficient precision, recall and dice-coefficient rates, upon testing high-grade breast cancer images containing several thousand nuclei. In addition to the optimal performance on the highly complex images presented in this paper, this method also gave appreciable results in comparison with two recently published methods-Wienert et al. (2012) and Veta et al. (2013), which were tested using their own datasets., Competing Interests: The authors have declared that no competing interests exist.

Non-recurrent congestion or incidents are detrimental to the operability and efficiency of busy urban transport networks. There exist multiple automatic incident detection algorithms (AIDAs) to remotely detect the occurrence of an incident in highway or freeway scenarios; however, very little research has been performed to automatically detect incidents in signalized urban arterials. This limited research attention has mostly been focused on developing new urban arterial specific algorithms, rather than identifying alternative methods to synthesize existing freeway-based algorithms for urban conditions. The main hindrance to such synthesis is that the traffic patterns on the signalized urban arterials are significantly different from the same on highways/freeways due to the presence of traffic intersections. This article introduces a new strategy of customizing the existing AIDAs (freeway based or otherwise) to significantly improve their adaptability to signalized urban arterial transport networks. The new strategy focuses on preprocessing the traffic information before being used as input to a freeway/highway-based AIDA to lessen the effect of traffic signals and to imitate the input patterns in highway/freeway-based incident conditions. The effectiveness of this new strategy has been established with the help of four existing AIDAs. The proposed strategy is a simple solution to implement existing algorithms to signalized urban networks without any further instrumentation or operational cost. [ABSTRACT FROM AUTHOR]

Predictions of fundamental traffic variables in the short-term or near-term future are vital for any successful dynamic traffic management application. Univariate short-term traffic flow prediction algorithms are popular in literature. However, to facilitate the operationalities of advanced adaptive traffic management systems, there is a necessity of developing multivariate traffic condition prediction algorithms. A new multivariate short-term traffic flow and speed prediction methodology is proposed in this paper where the traffic flow and speed observations from uncongested (or linear) and congested (or nonlinear) regimes are regime-adjusted to ensure consistent system dynamics. The prediction methodology is developed by using artificial neural networks (ANN) algorithms in conjunction with adaptive learning rules. These learning rules demonstrate significantly improved accuracy and simultaneous reduction in computation times. Additionally, the paper attempts to identify the most suitable adaptive learning rule from a chosen pool of rules. The validation of the prediction methodology is performed by using traffic data from multiple locations in the United Kingdom (U.K.). The results indicate that the proposed multivariate forecasting algorithm is effective and computationally parsimonious to simultaneously predict traffic flow and speed in freeway or highway networks. [ABSTRACT FROM AUTHOR]