@inproceedings {366,
	title = {A medical texture local binary pattern for TRUS prostate segmentation},
	booktitle = {29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
	year = {2007},
	abstract = {<p>Prostate cancer diagnosis and treatment rely on segmentation of Transrectal Ultrasound (TRUS) prostate images. This is a challenging and difficult task dut to weak prostate boundaries, speckle noise and the short range of gray levels. Advances in digital imaging techniques have made it possible the acquisition of large volumes of TRUS prostate images so that there is considerable demand for automated segmentation systems. Local binary pattern (LBP) has been used for texture segmentation and analysis. Despite its promising performance for texture classification it has not yet been considered for TRUS prostate segmentation. In this paper we introduce a medical texture local binary pattern operator designed for applications of medical imaging where different tissues or micro organisms might maintain extremely weak underlying textures that make it impossible or very difficult for ordinary texture analysis approaches to classify them. In the proposed method the deformations of a level set contour are controlled based on the medical texture local binary pattern operator.</p>
},
	keywords = {automated segmentation systems, biomedical ultrasonics, cancer, digital imaging techniques, image classification, image segmentation, image texture, LBP, level set contour deformations, medical image processing, medical texture local binary pattern, prostate cancer diagnosis, prostate cancer treatment, short gray level range, speckle noise, texture analysis, Texture classification, texture segmentation, transrectal ultrasound prostate image segmentation, TRUS prostate segmentation},
	doi = {http://dx.doi.org/10.1109/IEMBS.2007.4353617},
	author = {N Nezamoddin Kachouie and P Fieguth}
}
@inproceedings {749,
	title = {An elliptical level set method for automatic TRUS prostate image segmentation},
	booktitle = {IEEE International Symposium on Signal Processing and Information Technology},
	year = {2006},
	abstract = {<p>One of the most important tasks in prostate cancer diagnosis and treatment is segmentation of transrectal ultrasound (TRUS) prostate images. Due to the large volumes of TRUS prostate images, automatic segmentation systems are mandatory. Weak prostate boundaries, speckle noise and the short range of gray levels make the task more challenging and difficult. Deformable models have been considered as an effective approach for semi-automatic prostate segmentation. However the main problem toward a fully automatic segmentation system using deformable models is initialization of seed or control points. In this paper an automatic level set prostate segmentation is presented. A classification method is employed to locate the approximate location of the prostate which is used to initiate the proposed elliptical level set contour. The deformations of the level set are guided by a velocity function which is derived using the TRUS prostate image histogram</p>
},
	keywords = {automatic level set prostate segmentation, automatic TRUS prostate image segmentation, biomedical ultrasonics, cancer, classification method, elliptical level set method, image classification, image segmentation, medical image processing, prostate cancer diagnosis, speckle noise, transrectal ultrasound, velocity function, weak prostate boundaries},
	doi = {http://dx.doi.org/10.1109/ISSPIT.2006.270795},
	author = {N Kachouie and N Nezamoddin and P Fieguth and S Rahnamayan}
}
