Prostate tissue texture feature extraction for suspicious regions identification on TRUS images.
In this work, two different approaches are proposed for region of interest (ROI) segmentation using transrectal ultrasound (TRUS) images. The two methods aim to extract informative features that are able to characterize suspicious regions in the TRUS images. Both proposed methods are based on multi-...
| Publicado en: | Journal of Digital Imaging Vol. 22; no. 5; pp. 503 - 519 |
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| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2009
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105434955&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105434955 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2009 vid: 22 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 105434955 2010413167 10.1007/s10278-008-9124-1 NLM18473140 105434955 ppf: 503 ppct: 16 formats: fmt: @attributes: type: P tig: atl: Prostate tissue texture feature extraction for suspicious regions identification on TRUS images. aug: au: Mohamed S Li J Salama M Freeman G affil: ECE Department, University of Waterloo, 200 University Avenue West Waterloo Canada N2T 1x5 sug: subj: Diagnosis, Computer Assisted Image Processing, Computer Assisted Prostate Ultrasonography Prostatic Neoplasms Diagnosis Algorithms Comparative Studies Human ab: In this work, two different approaches are proposed for region of interest (ROI) segmentation using transrectal ultrasound (TRUS) images. The two methods aim to extract informative features that are able to characterize suspicious regions in the TRUS images. Both proposed methods are based on multi-resolution analysis that is characterized by its high localization in both the frequency and the spatial domains. Being highly localized in both domains, the proposed methods are expected to accurately identify the suspicious ROIs. On one hand, the first method depends on a Gabor filter that captures the high frequency changes in the image regions. On the other hand, the second method depends on classifying the wavelet coefficients of the image. It is shown in this paper that both methods reveal details in the ROIs which correlate with their pathological representations. It was found that there is a good match between the regions identified using the two methods, a result that supports the ability of each of the proposed methods to mimic the radiologist's decision in identifying suspicious regions. Studying two ROI segmentation methods is important since the only available dataset is the radiologist's suspicious regions, and there is a need to support the results obtained by either one of the proposed methods. This work is mainly a preliminary proof of concept study that will ultimately be expanded to a larger scale study whose aim will be introducing an assisting tool to help the radiologist identify the suspicious regions. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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