@inproceedings {330,
	title = {Improved interactive medical image segmentation using Enhanced Intelligent Scissors (EIS)},
	booktitle = {30th Annual Conference of the IEEE Engineering in Medicine and Biology Society},
	year = {2008},
	month = {08/2008},
	address = {Vancouver, British Columbia, Canada},
	abstract = {<p>A novel interactive approach called Enhanced Intelligent Scissors (EIS) is presented for segmenting regions of interest in medical images. The proposed interactive medical image segmentation algorithm addresses the issues associated with segmenting medical images and allows for fast, robust, and flexible segmentation without requiring accurate manual tracing. A robust complex wavelet phase-based representation is used as an external local cost to address issues associated with contrast non-uniformities and noise typically found in medical images. The boundary extraction problem is formulated as a Hidden Markov Model (HMM) and the novel approach to the second-order Viterbi algorithm with state pruning is used to find the optimal boundary in a robust and efficient manner based on the extracted external and internal local costs, thus handling much inexact user boundary definitions than existing methods. Experimental results using MR and CT images show that the proposed algorithm achieves accurate segmentation in medical images without the need for accurate boundary definition as per existing Intelligent Scissors methods. Furthermore, usability testing indicate that the proposed algorithm requires significantly less user interaction than Intelligent Scissors.</p>
},
	keywords = {Algorithms, Artificial Intelligence, Automated, Computer Simulation, Computer-Assisted, Diagnostic Imaging, Humans, image enhancement, image interpretation, Information Storage and Retrieval, Markov Chains, Models, Pattern Recognition, Reproducibility of Results, Statistical, Visible Human Projects},
	doi = {http://dx.doi.org/10.1109/IEMBS.2008.4649855},
	author = {A Mishra and A Wong and W Zhang and D A. Clausi and P Fieguth}
}
@article {690,
	title = {Gaussian MRF rotation-invariant features for image classification},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {26},
	year = {2004},
	pages = {951 - 955},
	abstract = {<p>Features based on Markov random field (MRF) models are sensitive to texture rotation. This paper develops an anisotropic circular Gaussian MRF (ACGMRF) model for retrieving rotation-invariant texture features. To overcome the singularity problem of the least squares estimate method, an approximate least squares estimate method is designed and implemented. Rotation-invariant features are obtained from the ACGMRF model parameters using the discrete Fourier transform. The ACGMRF model is demonstrated to be a statistical improvement over three published methods. The three methods include a Laplacian pyramid, an isotropic circular GMRF (ICGMRF), and gray level cooccurrence probability features.</p>
},
	keywords = {Algorithms, anisotropic circular Gaussian MRF model, Artificial Intelligence, Automated, Computer Simulation, Computer-Assisted, discrete Fourier transform, discrete Fourier transforms, Gaussian MRF rotation-invariant features, gray level cooccurrence probability features, image classification, image enhancement, image interpretation, image texture, Laplacian pyramid, Markov Chains, Markov processes, Markov Random Field models, Models, Normal Distribution, Pattern Recognition, Rotation, rotation-invariant texture features, Statistical, statistical improvement, texture rotation},
	issn = {0162-8828},
	doi = {http://dx.doi.org/10.1109/TPAMI.2004.30},
	author = {H Deng and D A. Clausi}
}
