Int J Comput Assist Radiol Surg - Statistical shape and texture model of quadrature phase information for prostate segmentation.

Tópicos

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Resumo

RPOSE: Prostate volume estimation from segmentation of transrectal ultrasound (TRUS) images aids in diagnosis and treatment of prostate hypertrophy and cancer. Computer-aided accurate and computationally efficient prostate segmentation in TRUS images is a challenging task, owing to low signal-to-noise ratio, speckle noise, calcifications, and heterogeneous intensity distribution in the prostate region.METHOD: A multi-resolution framework using texture features in a parametric deformable statistical model of shape and appearance was developed to segment the prostate. Local phase information of log-Gabor quadrature filter extracted texture of the prostate region in TRUS images. Large bandwidth of log-Gabor filter ensures easy estimation of local orientations, and zero response for a constant signal provides invariance to gray level shift. This aids in enhanced representation of the underlying texture information of the prostate unaffected by speckle noise and imaging artifacts. The parametric model of the propagating contour is derived from principal component analysis of prior shape and texture information of the prostate from the training data. The parameters were modified using prior knowledge of the optimization space to achieve segmentation.RESULTS: The proposed method achieves a mean Dice similarity coefficient value of 0.95 ? 0.02 and mean absolute distance of 1.26 ? 0.51 millimeter when validated with 24 TRUS images of 6 data sets in a leave-one-patient-out validation framework.CONCLUSIONS: The proposed method for prostate TRUS image segmentation is computationally efficient and provides accurate prostate segmentations in the presence of intensity heterogeneities and imaging artifacts.

Resumo Limpo

rpose prostat volum estim segment transrect ultrasound trus imag aid diagnosi treatment prostat hypertrophi cancer computeraid accur comput effici prostat segment trus imag challeng task owe low signaltonois ratio speckl nois calcif heterogen intens distribut prostat regionmethod multiresolut framework use textur featur parametr deform statist model shape appear develop segment prostat local phase inform loggabor quadratur filter extract textur prostat region trus imag larg bandwidth loggabor filter ensur easi estim local orient zero respons constant signal provid invari gray level shift aid enhanc represent under textur inform prostat unaffect speckl nois imag artifact parametr model propag contour deriv princip compon analysi prior shape textur inform prostat train data paramet modifi use prior knowledg optim space achiev segmentationresult propos method achiev mean dice similar coeffici valu mean absolut distanc millimet valid trus imag data set leaveonepatientout valid frameworkconclus propos method prostat trus imag segment comput effici provid accur prostat segment presenc intens heterogen imag artifact

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