Adaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture Analysis

no code implementations • 21 Aug 2019 • Neslihan Bayramoglu, Aleksei Tiulpin, Jukka Hirvasniemi, Miika T. Nieminen, Simo Saarakkala

Compared to the current state-of-the-art approaches, our results suggest that the proposed adaptive ROI approach in texture analysis of subchondral bone can increase the diagnostic performance for detecting the presence of radiographic OA.

Texture Classification

Bone Texture Analysis for Prediction of Incident Radio-graphic Hip Osteoarthritis Using Machine Learning: Data from the Cohort Hip and Cohort Knee (CHECK) study

no code implementations • 13 Feb 2019 • Jukka Hirvasniemi, Willem Paul Gielis, Saeed Arbabi, Rintje Agricola, Willem Evert van Spil, Vahid Arbabi, Harrie Weinans

Our aim was to assess the ability of radiography-based bone texture parameters in proximal femur and acetabulum to predict incident radiographic hip osteoarthritis (rHOA) over a 10 years period.

Texture Classification