|
|
|
|
| LEADER |
01000caa a22002652c 4500 |
| 001 |
NLM200026925 |
| 003 |
DE-627 |
| 005 |
20250211201333.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231223s2010 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TUFFC.2010.1613
|2 doi
|
| 028 |
5 |
2 |
|a pubmed25n0667.xml
|
| 035 |
|
|
|a (DE-627)NLM200026925
|
| 035 |
|
|
|a (NLM)20679005
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Xu, Robert Sheng
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Information tracking approach to segmentation of ultrasound imagery of the prostate
|
| 264 |
|
1 |
|c 2010
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Completed 05.01.2011
|
| 500 |
|
|
|a Date Revised 25.11.2016
|
| 500 |
|
|
|a published: Print
|
| 500 |
|
|
|a Citation Status MEDLINE
|
| 520 |
|
|
|a The volume of the prostate is known to be a pivotal quantity used by clinicians to assess the condition of the gland during prostate cancer screening. As an alternative to palpation, an increasing number of methods for estimation of the volume of the prostate are based on using imagery data. The necessity to process large volumes of such data creates a need for automatic segmentation tools which would allow the estimation to be carried out with maximum accuracy and efficiency. In particular, the use of transrectal ultrasound (TRUS) imaging in prostate cancer screening seems to be becoming a standard clinical practice because of the high benefit-to-cost ratio of this imaging modality. Unfortunately, the segmentation of TRUS images is still hampered by relatively low contrast and reduced SNR of the images, thereby requiring the segmentation algorithms to incorporate prior knowledge about the geometry of the gland. In this paper, a novel approach to the problem of segmenting the TRUS images is described. The proposed approach is based on the concept of distribution tracking, which provides a unified framework for modeling and fusing image-related and morphological features of the prostate. Moreover, the same framework allows the segmentation to be regularized by using a new type of weak shape priors, which minimally bias the estimation procedure, while rendering the procedure stable and robust. The value of the proposed methodology is demonstrated in a series of in silico and in vivo experiments
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a Research Support, Non-U.S. Gov't
|
| 700 |
1 |
|
|a Michailovich, Oleg
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Salama, Magdy
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on ultrasonics, ferroelectrics, and frequency control
|d 1986
|g 57(2010), 8 vom: 01. Aug., Seite 1748-61
|w (DE-627)NLM098181017
|x 1525-8955
|7 nnas
|
| 773 |
1 |
8 |
|g volume:57
|g year:2010
|g number:8
|g day:01
|g month:08
|g pages:1748-61
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TUFFC.2010.1613
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_22
|
| 912 |
|
|
|a GBV_ILN_24
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 57
|j 2010
|e 8
|b 01
|c 08
|h 1748-61
|