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This paper describes a scene invariant crowd counting algorithm that uses local features to monitor crowd size. Unlike previous algorithms that require each camera to be trained separately, the proposed method uses camera calibration to scale between viewpoints, allowing a system to be trained and tested on different scenes. A pre-trained system could therefore be used as a turn-key solution for crowd...
Accurate segmentation of heterogeneous carcinoma lesions in medical images is vital to the treatment planning, assessment of therapy response and other oncological applications. With current state-of-the-art imaging modalities, the CT images enhance the interpretation of cancer functional abnormalities. We applied the variational Bayes inference (VBI) model on both anatomical and functional information...
In this paper, six existing image fusion algorithms, and their application to medical images are studied. Quantitative measurements used in the literature were tested on synthetic data to verify their consistency with qualitative analysis. Each of these algorithms was optimized for quantitative measures which our experiments found consistent with visual qualitative analysis. Finally, algorithms with...
Positron emission tomography (PET), as functional imaging, provides in-vivo spatial distribution of physiological or biochemical processes. The kinetic modelling process to derive quantitative functional parameter usually requires invasive frequent blood sampling. We proposed a new approach to use static imaging derived information to produce non-invasive input function (SID-IF). The performance of...
PET-MRI fusion is widely used in oncology for early tumour diagnosis, localisation and monitoring of therapy effects. Automatic extraction of the lesions on PET images is desirable, but remains problematic. Manual segmentation of PET images is time consuming, and restricts the definition of the tumour extent to some arbitrary threshold. This can be sub-optimal in brain tumour for instance, where tumour...
The variational expectation maximization (VEM) algorithm has recently been increasingly used to replace the expectation maximization (EM) algorithm in Gaussian mixture model (GMM) based statistical image segmentation. However, the VEM algorithm, similar to its traditional counterpart, suffers from the sensitiveness to initializations, and hence is prone to be trapped into local minima. In this paper,...
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