ECTS2016 Poster Presentations Osteoporosis: evaluation and imaging (39 abstracts)
A novel feature selection algorithm based on bone micro architecture analysis to identify osteoarthritis
Richard Ljuhar 1 , Benjamin Norman 1 , Davul Ljuhar 1 , Tobias Haftner 1 , Jiri Hladuvka 4 , Mary Bui Thi Mai 4 , Helena Canhão 5 , Ana MariaRodrigues 5 , Nelia Gouveia 5 , Jaime Branco 5 , Stefan Nehrer 3 , Astrid Fahrleitner-Pammer 2 & Hans-Peter Dimai 2
1Braincon Technologies, Vienna, Austria; 2Department of Internal Medicine, Division of Endocrinology and Metabolism, Medical University of Graz, Graz, Austria; 3Center for Regenerative Medicine & Orthopedics, Danube University, Krems, Austria; 4VRVis Research Competence Center, Vienna, Austria; 5Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal.
Objective: Texture information of the subchondral bone area (SBA) of 2D radiographs represents a promising possibility for evaluating the state of osteoarthritis (OA). However such features are likely to vary within the SBA and therefore the selection of the region of interest (ROI) plays a crucial role. Thus, a feature selection algorithm (FSA) is being applied in order to determine ROIs that enable an optimum discrimination between patients with and without OA.
Methods: The study included 152 standardized knee radiographs from 66 cases and 86 controls. SBA was assessed by using both fractal analysis (Bone Structure Vale – BSV) and a Shannon Entropy (SE) algorithm at predefined regions of the proximal tibia and the distal femur. The selected area of the proximal tibia involved a matrix of 3×8 ROIs, whereas a 2×2 matrix was defined for each condyle of the distal femur. SE and the BSV were calculated for each of the 32 ROIs, respectively. Based on these 64 variables, a FSA was applied to determine the variables that showed the best discrimination power.
Results: Combining the BSV and SE, the odds ratio increased significantly from 3.08 (95% CI: 1.78–5.30) to 14.82 (95% CI: 6.69–32.83) when using 15 features, and to 39.75 (95% CI: 15.41–102.51) based on ten features. By using the selected ten features the accuracy was found to be 0.86. This showed to be a significant improvement compared to the accuracy achieved when calculating a single mean value for the 3×8 ROIs of the proximal tibia alone (0.62 vs 0.86).
Conclusions: The application of a FSA in accordance with the combination of the two texture analysis methods shows a significant improvement with respect to the discrimination power between case and controls. The high odds ratios confirm that reliable results can be achieved by combining the BSV and the SE.
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Bone Abstracts
ISSN 2052-1219 (online)
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