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Proceedings Paper

Segmentation of prostate biopsy needles in transrectal ultrasound images
Author(s): Dagmar Krefting; Barbara Haupt; Thomas Tolxdorff; Carsten Kempkensteffen; Kurt Miller
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Paper Abstract

Prostate cancer is the most common cancer in men. Tissue extraction at different locations (biopsy) is the gold-standard for diagnosis of prostate cancer. These biopsies are commonly guided by transrectal ultrasound imaging (TRUS). Exact location of the extracted tissue within the gland is desired for more specific diagnosis and provides better therapy planning. While the orientation and the position of the needle within clinical TRUS image are limited, the appearing length and visibility of the needle varies strongly. Marker lines are present and tissue inhomogeneities and deflection artefacts may appear. Simple intensity, gradient oder edge-detecting based segmentation methods fail. Therefore a multivariate statistical classificator is implemented. The independent feature model is built by supervised learning using a set of manually segmented needles. The feature space is spanned by common binary object features as size and eccentricity as well as imaging-system dependent features like distance and orientation relative to the marker line. The object extraction is done by multi-step binarization of the region of interest. The ROI is automatically determined at the beginning of the segmentation and marker lines are removed from the images. The segmentation itself is realized by scale-invariant classification using maximum likelihood estimation and Mahalanobis distance as discriminator. The technique presented here could be successfully applied in 94% of 1835 TRUS images from 30 tissue extractions. It provides a robust method for biopsy needle localization in clinical prostate biopsy TRUS images.

Paper Details

Date Published: 8 March 2007
PDF: 8 pages
Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65122Y (8 March 2007); doi: 10.1117/12.709549
Show Author Affiliations
Dagmar Krefting, Charité - Universitätsmedizin Berlin (Germany)
Barbara Haupt, Charité - Universitätsmedizin Berlin (Germany)
Thomas Tolxdorff, Charité - Universitätsmedizin Berlin (Germany)
Carsten Kempkensteffen, Charité - Universitätsmedizin Berlin (Germany)
Kurt Miller, Charité - Universitätsmedizin Berlin (Germany)

Published in SPIE Proceedings Vol. 6512:
Medical Imaging 2007: Image Processing
Josien P. W. Pluim; Joseph M. Reinhardt, Editor(s)

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