AI Article Synopsis
- A two-stage segmentation algorithm is developed to address challenges in MRI images, such as inhomogeneity, weak edges, and artifacts.
- The first stage uses K-mean clustering for object classification, followed by defining a speed function to identify rough boundaries, while the second stage improves boundary accuracy using gradient intensity.
- A deformable model reconstructs the shape of the left ventricle (LV) by considering external forces from MRI slices and using the level set method to solve the governing dynamic equations, proving the algorithm's effectiveness through experiments.
Article Abstract
A two-stage segmentation algorithm is presented to solve the problems of inhomogeneity, weak edges and artifacts exhibited in the magnetic resonance imaging (MRI) images. First, the K-mean clustering algorithm is applied to classify the objects. Then, a speed function based on the clustering results is defined in order to search the rough boundary. Secondly, a speed function of the gradient intensity is constructed to locate the boundary accurately. Due to the lack of deformation information of the boundaries between MR slices, a deformable model is used to reconstruct the shape of the LV: a dynamic equation governing the surface deformation is given; from the slice data, external forces are constructed and elastic forces are provided with mean curvatures of the deformation surface. The level set method is applied to solve the dynamic equation for the LV shape. Experimental results demonstrate the effectiveness of the algorithm listed in the paper.
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