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Automated kidney detection for 3D ultrasound using scan line searching

: Noll, Matthias; Nadolny, Anne; Wesarg, Stefan


Duric, N.; Heyde, B. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Ultrasonic Imaging and Tomography : Medical Imaging 2016, 28-29 February 2016, San Diego, California, United States
Bellingham, WA: SPIE, 2016 (Proceedings of SPIE 9790)
ISBN: 978-1-5106-0025-6
Paper 97901B, 6 S.
Medical Imaging Conference <2016, San Diego/Calif.>
Fraunhofer IGD ()
3D Ultrasound; detection; scanning methods; region growing; Guiding Theme: Individual Health; Research Area: Computer vision (CV)

Ultrasound (U/S) is a fast and non-expensive imaging modality that is used for the examination of various anatomical structures, e.g. the kidneys. One important task for automatic organ tracking or computer-aided diagnosis is the identification of the organ region. During this process the exact information about the transducer location and orientation is usually unavailable. This renders the implementation of such automatic methods exceedingly challenging. In this work we like to introduce a new automatic method for the detection of the kidney in 3D U/S images. This novel technique analyses the U/S image data along virtual scan lines. Here, characteristic texture changes when entering and leaving the symmetric tissue regions of the renal cortex are searched for. A subsequent feature accumulation along a second scan direction produces a 2D heat map of renal cortex candidates, from which the kidney location is extracted in two steps. First, the strongest candidate as well as its counterpart are extracted by heat map intensity ranking and renal cortex size analysis. This process exploits the heat map gap caused by the renal pelvis region. Substituting the renal pelvis detection with this combined cortex tissue feature increases the detection robustness. In contrast to model based methods that generate characteristic pattern matches, our method is simpler and therefore faster. An evaluation performed on 61 3D U/S data sets showed, that in 55 cases showing none or minor shadowing the kidney location could be correctly identified.