@article {168,
	title = {Automatic skin lesion segmentation via iterative stochastic region merging},
	journal = {IEEE Transactions on Information Technology in Biomedicine},
	volume = {15},
	year = {2011},
	pages = {929 - 936},
	abstract = {<p>An automatic method for segmenting skin lesions in conventional macroscopic images is presented. The images are acquired with conventional cameras, without the use of a dermoscope. Automatic segmentation of skin lesions from macroscopic images is a very challenging problem due to factors such as illumination variations, irregular structural and color variations, the presence of hair, as well as the occurrence of multiple unhealthy skin regions. To address these factors, a novel iterative stochastic region-merging approach is employed to segment the regions corresponding to skin lesions from the macroscopic images, where stochastic region merging is initialized first on a pixel level, and subsequently on a region level until convergence. A region merging likelihood function based on the regional statistics is introduced to determine the merger of regions in a stochastic manner. Experimental results show that the proposed system achieves overall segmentation error of under 10\% for skin lesions in macroscopic images, which is lower than that achieved by existing methods.</p>
},
	keywords = {iterative, lesion, region merging, skin cancer, stochastic},
	issn = {1089-7771},
	doi = {http://dx.doi.org/10.1109/TITB.2011.2157829},
	author = {A Wong and J Scharcanski and P Fieguth}
}
@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}
}
