<title>Knowledge-based deformable surface model with application to segmentation of brain structures in MRI</title>

Abstract

We have developed a knowledge-based deformable surface for segmentation of medical images. This work has been done in the context of segmentation of hippocampus from brain MRI, due to its challenge and clinical importance. The model has a polyhedral discrete structure and is initialized automatically by analyzing brain MRI sliced by slice, and finding few landmark features at each slice using an expert system. The expert system decides on the presence of the hippocampus and its general location in each slice. The landmarks found are connected together by a triangulation method, to generate a closed initial surface. The surface deforms under defined internal and external force terms thereafter, to generate an accurate and reproducible boundary for the hippocampus. The anterior and posterior (AP) limits of the hippocampus is estimated by automatic analysis of the location of brain stem, and some of the features extracted in the initialization process. These data are combined together with a priori knowledge using Bayes method to estimate a probability density function (pdf) for the length of the structure in sagittal direction. The hippocampus AP limits are found by optimizing this pdf. The model is tested on real clinical data and the results show very good model performance 1 .

Initialisation of 3D level set for hippocampus segmentation from volumetric brain MR images

“…Different initialisation approaches have been applied for level set segmentation of medical images. Ghanei et al [1] and Aloui et al [2] used a 3D surface by stitching 2D contours using manual landmarks. This method results in undesirable edge fragments and gaps that lead to boundary leakage.…”