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Proceedings Paper

Automated kidney morphology measurements from ultrasound images using texture and edge analysis
Author(s): Hariharan Ravishankar; Pavan Annangi; Michael Washburn; Justin Lanning
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Paper Abstract

In a typical ultrasound scan, a sonographer measures Kidney morphology to assess renal abnormalities. Kidney morphology can also help to discriminate between chronic and acute kidney failure. The caliper placements and volume measurements are often time consuming and an automated solution will help to improve accuracy, repeatability and throughput. In this work, we developed an automated Kidney morphology measurement solution from long axis Ultrasound scans. Automated kidney segmentation is challenging due to wide variability in kidney shape, size, weak contrast of the kidney boundaries and presence of strong edges like diaphragm, fat layers. To address the challenges and be able to accurately localize and detect kidney regions, we present a two-step algorithm that makes use of edge and texture information in combination with anatomical cues. First, we use an edge analysis technique to localize kidney region by matching the edge map with predefined templates. To accurately estimate the kidney morphology, we use textural information in a machine learning algorithm framework using Haar features and Gradient boosting classifier. We have tested the algorithm on 45 unseen cases and the performance against ground truth is measured by computing Dice overlap, % error in major and minor axis of kidney. The algorithm shows successful performance on 80% cases.

Paper Details

Date Published: 1 April 2016
PDF: 7 pages
Proc. SPIE 9790, Medical Imaging 2016: Ultrasonic Imaging and Tomography, 97901A (1 April 2016); doi: 10.1117/12.2216802
Show Author Affiliations
Hariharan Ravishankar, GE Global Research (India)
Pavan Annangi, GE Global Research (India)
Michael Washburn, GE Healthcare (United States)
Justin Lanning, GE Healthcare (United States)

Published in SPIE Proceedings Vol. 9790:
Medical Imaging 2016: Ultrasonic Imaging and Tomography
Neb Duric; Brecht Heyde, Editor(s)

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