Braun Juergen, Bernarding Johannes, Koennecke Hans-Christian, Wolf Karl-Juergen, Tolxdorff Thomas
Department for Medical Informatics, University Hospital Benjamin Franklin, Free University of Berlin, Hindenburgdamm 30, 12200 Berlin, Germany.
Comput Methods Biomech Biomed Engin. 2002 Dec;5(6):411-20. doi: 10.1080/1025584021000011082.
Diffusion-weighted imaging enables the diagnosis of cerebral ischemias very early, thus supporting therapies such as thrombolysis. However, morphology and tissue-characterizing parameters (e.g. relaxation times or water diffusion) may vary strongly in ischemic regions, indicating different underlying pathologic processes. As the determination of the parameters by a supervised segmentation is very time consuming, we evaluated whether different infarct patterns may be segmented by an automated, multidimensional feature-based method using a unified segmentation procedure. Ischemias were classified into 5 characteristic patterns. For each class, a 3D histogram based on T(2)- and diffusion-weighted images as well as calculated apparent diffusion coefficients (ADC) was generated from a representative data set. Healthy and pathologic tissue classes were segmented in the histogram as separate, local density maxima with freely shaped borders. Segmentation control parameters were optimized in a 3-step procedure. The method was evaluated using synthetic images as well as results of a supervised segmentation. For the analysis of cerebral ischemias, the optimal control parameter set led to sensitivities and specificities between 1.0 and 0.9.
