Numerical recipes in C: the art of scientific computing
Numerical recipes in C: the art of scientific computing
Compound Extraction and Fitting Method for Detecting Cardiac Ventricle in SPECT Data
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Intelligent control of the hierarchical agglomerative clustering process
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Assessment of right ventricle (RV) performance plays an important role in diagnosing cardiopulmonary diseases. Shape-based statistics describing the RV (e.g., RV volume (RVV)) are very important for developing a good comprehension of the performance of the RV. Automatic determination of the RV's location, position, and volume from medical images (e.g., CT, MRI, Echocardiography) can improve speed and consistency of diagnosis since most current procedures for obtaining such statistics rely on substantial human involvement. An RV geometric model can be very useful in guiding automated procedures, although only a few methods of describing and exploiting the RV's shape have been presented, in part due to the RV's complex shape. Most existing models imprecisely describe the RV shape. In this extended abstract, our new RV geometric model and a computational framework that exploits this new model for automatic detection and extraction of the RV from Gated Blood Pool (GBP) Single Photon Emission Computed Tomography (SPECT) datasets are presented.