Intervertebral disc degeneration is an age-associated condition related to chronic back pain, while its consequences are responsible for over 90 % of spine surgical procedures. In clinical practice, MRI is the modality of reference for diagnosing disc degeneration. In this study, we worked toward 2-D semiautomatic segmentation of both normal and degenerated lumbar intervertebral discs from T2-weighted midsagittal MR images of the spine. This task is challenged by partial volume effects and overlapping gray-level values between neighboring tissue classes. To overcome these problems three variations of atlas-based segmentation using a probabilistic atlas of the intervertebral disc were developed and their accuracies were quantitatively evaluated against manually segmented data. The best overall performance, when considering the tradeoff between segmentation accuracy and time efficiency, was accomplished by the atlas-robust-fuzzy c-means approach, which combines prior anatomical knowledge by means of a rigidly registered probabilistic disc atlas with fuzzy clustering techniques incorporating smoothness constraints. The dice similarity indexes of this method were 91.6 % for normal and 87.2 % for degenerated discs. Research in progress utilizes the proposed approach as part of a computer-aided diagnosis system for quantification and characterization of disc degeneration severity. Moreover, this approach could be exploited in computer-assisted spine surgery.

We reviewed 13 patients with infected nonunion of the distal femur and bone loss, who had been treated by radical surgical debridement and the application of an Ilizarov external fixator. All had severely restricted movement of the knee and a mean of 3.1 previous operations. The mean length of the bony defect was 8.3 cm and no patient was able to bear weight. The mean external fixation time was 309.8 days. According to Paley's grading system, eight patients had an excellent clinical and radiological result and seven excellent and good functional results. Bony union, the ability to bear weight fully, and resolution of the infection were achieved in all the patients. The external fixation time was increased when the definitive treatment started six months or more after the initial trauma, the patient had been subjected to more than four previous operations and the initial operation had been open reduction and internal fixation.

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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.

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