@article {181,
	title = {Dynamic Fisher-Tippett Region Merging Approach to Transrectal Ultrasound Prostate Lesion Segmentation},
	journal = {IEEE Transactions on Information Technology in BioMedicine},
	year = {2011},
	abstract = {<p>In this paper, a computerized approach to segmenting prostate lesions in transrectal ultrasound (TRUS) images is presented. The segmentation of prostate lesions from TRUS images is very challenging due to issues such as poor contrast, low signal-to-noise ratios, and irregular shape variations. To address these issues, a novel approach is employed to segment the lesions from the surrounding prostate, where region merging is performed via a region merging likelihood function based on regional statistics as well as Fisher-Tippett statistics. Experimental results using TRUS prostate images demonstrate that the proposed Fisher-Tippett region merging (FTRM) approach achieves more accurate segmentation of prostate lesions when compared to other segmentation methods.</p>
},
	keywords = {lesion, Prostate, region merging, ultrasound},
	author = {A Wong and J Scharcanski}
}
@article {221,
	title = {Generalized probabilistic scale space for image restoration},
	journal = {IEEE Transactions on Image Processing},
	volume = {19},
	year = {2010},
	pages = {2774 - 2780},
	abstract = {<p>A novel generalized sampling-based probabilistic scale space theory is proposed for image restoration. We explore extending the definition of scale space to better account for both noise and observation models, which is important for producing accurately restored images. A new class of scale-space realizations based on sampling and probability theory is introduced to realize this extended definition in the context of image restoration. Experimental results using 2-D images show that generalized sampling-based probabilistic scale-space theory can be used to produce more accurate restored images when compared with state-of-the-art scale-space formulations, particularly under situations characterized by low signal-to-noise ratios and image degradation.</p>
},
	keywords = {2D image, Bayes Theorem, Computer-Assisted, generalized sampling-based probabilistic scale space theory, Humans, image degradation, image processing, image restoration, image sampling, Male, noise model, nonlinear dynamics, Normal Distribution, Photography, probability, Prostate, signal-to-noise ratio, Ultrasonography},
	issn = {1057-7149},
	doi = {http://dx.doi.org/10.1109/TIP.2010.2048973},
	author = {A Wong and A Mishra}
}
