3D Active Shape Models Using Gradient Descent Optimization of Description Length

Abstract. Active Shape Models are a popular method for segmenting three-dimensional medical images. To obtain the required landmark correspondences, various automatic approaches have been proposed. In this work, we present an improved version of minimizing the description length (MDL) of the model. To initialize the algorithm, we describe a method to distribute landmarks on the training shapes using a conformal parameterization function. Next, we introduce a novel procedure to modify landmark positions locally without disturbing established correspondences. We employ a gradient descent optimization to minimize the MDL cost function, speeding up automatic model building by several orders of magnitude when compared to the original MDL approach. The necessary gradient information is estimated from a singular value decomposition, a more accurate technique to calculate the PCA than the commonly used eigendecomposition of the covariance matrix. Finally, we present results for several synthet...

• Type: Conference
• Year: 2005
• Where: IPMI
• Authors: Tobias Heimann, Ivo Wolf, Tomos G. Williams, Hans-Peter Meinzer
• Keywords: IPMI 2005, MDL Cost Function, Medical Imaging, Original Mdl Approach, Required Landmark Correspondences