T Bergmann1, U Maeder, M Fiebich, M Dickob, T W Nattkemper, D Anselmetti. 1. Institute of Bioprocess Engineering and Pharmaceutical Technology, Technische Hochschule Mittelhessen, Wiesenstraße 14, Giessen, Germany. Thorsten.Bergmann@kmub.thm.de
Abstract
OBJECTIVE: The degeneration of articular cartilage is part of the clinical syndrome of osteoarthritis (OA) and one of the most common causes of pain and disability in middle-aged and older people(1). However, the objective detection of an initial state of OA is still challenging. In order to categorize cartilage into states of OA, an algorithm is presented which offers objective categorization on the basis of two-photon laser-scanning microscopy (TPLSM) images. METHODS: The algorithm is based on morphological characteristics of the images and results in a topographical visualization. This paper describes the algorithm and shows the result of a categorization of human cartilage samples. RESULTS: The resulting map of the analysis of TPLSM images can be divided into areas which correspond to the grades of the Outerbridge-Categorization. The algorithm is able to differentiate the samples in coincidence with the macroscopic impression. CONCLUSION: The method is promising for early OA detection and categorization. In order to achieve a higher benefit for the physician the method must be transferred to an endoscopic setup for an application in surgery.
OBJECTIVE: The degeneration of articular cartilage is part of the clinical syndrome of osteoarthritis (OA) and one of the most common causes of pain and disability in middle-aged and older people(1). However, the objective detection of an initial state of OA is still challenging. In order to categorize cartilage into states of OA, an algorithm is presented which offers objective categorization on the basis of two-photon laser-scanning microscopy (TPLSM) images. METHODS: The algorithm is based on morphological characteristics of the images and results in a topographical visualization. This paper describes the algorithm and shows the result of a categorization of humancartilage samples. RESULTS: The resulting map of the analysis of TPLSM images can be divided into areas which correspond to the grades of the Outerbridge-Categorization. The algorithm is able to differentiate the samples in coincidence with the macroscopic impression. CONCLUSION: The method is promising for early OA detection and categorization. In order to achieve a higher benefit for the physician the method must be transferred to an endoscopic setup for an application in surgery.