Algorithms for clustering data
Algorithms for clustering data
A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov random field modeling in computer vision
Markov random field modeling in computer vision
Image processing and data analysis: the multiscale approach
Image processing and data analysis: the multiscale approach
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Approximate Bayes Factors for Image Segmentation: The Pseudolikelihood Information Criterion (PLIC)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast automatic unsupervised image segmentation and curve detection in spatial point patterns
Fast automatic unsupervised image segmentation and curve detection in spatial point patterns
Probabilistic Space-Time Video Modeling via Piecewise GMM
IEEE Transactions on Pattern Analysis and Machine Intelligence
A reconfigurable, power-efficient adaptive Viterbi decoder
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Hardware-software co-synthesis of hard real-time systems with reconfigurable FPGAs
Computers and Electrical Engineering
Bayesian inference for multiband image segmentation via model-based cluster trees
Image and Vision Computing
Multiband segmentation based on a hierarchical Markov model
Pattern Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Adaptive integrated image segmentation and object recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation using a texture gradient based watershed transform
IEEE Transactions on Image Processing
3D medical volume segmentation using hybrid multiresolution statistical approaches
Advances in Artificial Intelligence - Special issue on machine learning paradigms for modeling spatial and temporal information in multimedia data mining
Artificial neural network-based system for PET volume segmentation
Journal of Biomedical Imaging
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Positron emission tomography (PET) imaging is an emerging medical imaging modality. Due to its high sensitivity and ability to model function, it is effective in identifying active regions that may be associated with various types of tumours. Increasing numbers of patient scans have led to an urgent need for efficient data archival and the development of new image analysis techniques to aid clinicians in the diagnosis of disease. Additionally, to handle the large volumes of data generated using complex processing algorithms, it is becoming evident that co-processing solutions are essential. In this paper, an automated system for the segmentation of oncological PET data is developed. Initially, the Bayesian information criterion (BIC) is utilised for optimal segmentation level selection. Expectation maximisation (EM) based mixture modelling is then performed, using a k-means clustering procedure which varies voxel order for initialisation. A multiscale Markov model is then used to refine this segmentation by modelling spatial correlations between neighbouring image voxels. A field programmable gate array (FPGA) based co-processing solution is also proposed to offload the most complex computations onto hardware, in order to achieve high performance.