Modified complete synovectomy prevents recurrence in synovial chondromatosis of the hip

Synovial chondromatosis of the hip is a rare disorder with few published reports regarding treatment and outcomes, and therefore, selecting the optimal surgical treatment is difficult. We reviewed eight patients with monoarticular synovial chondromatosis of the hip who had joint débridement and a modified total synovectomy performed through a surgical hip dislocation with a trochanteric flip osteotomy. Patients were evaluated for recurrence of disease, progression of osteoarthritis, clinical outcomes, and subsequent reoperations. The minimum followup was 4 years (mean, 6.5 years). At final review, no patient had recurrence of disease. Two patients had progression of osteoarthritis requiring total hip arthroplasties at 5 and 10 years after the initial surgical intervention. These patients did not show recurrent disease on histologic examination of the synovial membrane at the time of the arthroplasty. The six patients with preserved joints were followed up for a mean of 6.2 years. The mean Merle d'Aubigné and Postel score in this group was 16.5 points (range, 15-18 points) at the latest followup. There were no major or minor complications related to this treatment. Our midterm results suggest that open débridement with modified total synovectomy is an effective treatment that prevents recurrence of disease and provides substantial pain relief. Surgical hip dislocation allows safe and complete access to the joint for débridement and synovectomy with no added morbidity.

Applied Graph Theory in Computer Vision and Pattern Recognition

This book will serve as a foundation for a variety of useful applications of graph theory to computer vision, pattern recognition, and related areas. It covers a representative set of novel graph-theoretic methods for complex computer vision and pattern recognition tasks. The first part of the book presents the application of graph theory to low-level processing of digital images such as a new method for partitioning a given image into a hierarchy of homogeneous areas using graph pyramids, or a study of the relationship between graph theory and digital topology. Part II presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, including a survey of graph based methodologies for pattern recognition and computer vision, a presentation of a series of computationally efficient algorithms for testing graph isomorphism and related graph matching tasks in pattern recognition and a new graph distance measure to be used for solving graph matching problems. Finally, Part III provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks. It includes a critical review of the main graph-based and structural methods for fingerprint classification, a new method to visualize time series of graphs, and potential applications in computer network monitoring and abnormal event detection.