PubMed Health⌕ Search

Biomedical subjects

I Boniatis

Publications and source records attributed to I Boniatis.

2 recordsLinked to original sources

Osteoarthritis severity of the hip by computer-aided grading of radiographic images.

A computer-aided classification system was developed for the assessment of the severity of hip osteoarthritis (OA). Sixty-four radiographic images of normal and osteoarthritic hips were digitized and enhanced. Employing the Kellgren and Lawrence scale, the hips were grouped by three experienced orthopaedists into three OA-severity categories: Normal, Mild/Moderate and Severe. Utilizing custom-developed software, 64 ROIs corresponding to the radiographic Hip Joint Spaces were manually segmented and novel textural features were generated. These features were used in the design of a two-level classification scheme for characterizing hips as normal or osteoarthritic (1st level) and as of Mild/Moderate or Severe OA (2nd level). At each classification level, an ensemble of three classifiers was implemented. The proposed classification scheme discriminated correctly all normal hips from osteoarthritic hips (100% accuracy), while the discrimination accuracy between Mild/Moderate and Severe osteoarthritic hips was 95.7%. The proposed system could be used as a diagnosis decision-supporting tool.

Aged↗

Assessing hip osteoarthritis severity utilizing a probabilistic neural network based classification scheme.

A computer-based classification system is proposed for the characterization of hips from pelvic radiographs as normal or osteoarthritic and for the discrimination among various grades of osteoarthritis (OA) severity. Pelvic radiographs of 18 patients with verified unilateral hip OA were evaluated by three experienced physicians, who assessed OA severity employing the Kellgren and Lawrence scale as: normal, mild/moderate and severe. Five run-length, 75 Laws' and 5 novel textural features were extracted from the digitized radiographic images of each patient's osteoarthritic and contralateral normal hip joint spaces (HJSs). Each one of the three sets of textural features (run-lengths, Laws' and novel features) was separately utilized for assigning hips into the three OA severity categories, by means of a probabilistic neural network (PNN) classifier based hierarchical tree structure. The highest classification accuracy (100%) for characterizing hips as normal, of mild/moderate or of severe OA was obtained for the novel textural features set. Additionally, the novel textural features were used to design a mathematical regression model for providing a quantitative estimation of OA severity. Measured OA severity values, as expressed by HJS-narrowing, correlated highly (r=0.85, p<0.001) with the predicted values by the mathematical regression model. The proposed system may be valuable in OA-patient management.

Aged↗