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.
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