Volume-quantization-based neural network approach to 3D MR angiography image segmentation
Volume visualization of cerebral blood vessels is highly significant for diagnosis of the cerebral diseases. It is because the automated segmentation of the blood vessels from an MR angiography (MRA) image is a knotty problem that there are few works on it. This paper proposes an automated method to segment the blood vessels from 3D time of flight (TOF) MRA volume data. The method consists of: (1) removing the background, (2) volume quantization by watershed segmentation, and (3) classification of primitives by using an artificial neural network (NN). In the proposed method, the NN classifies each primitive, which is a clump of voxels, by evaluating the intensity and the 3D shape. The method was applied to seven MRA data sets. The evaluation was done by comparing with the manual classification results. The average classification accuracy was 80.8%. The method also showed the volume visualizations using target maximum intensity projection (target MIP) and surface shaded display (SSD). The evaluation by a physician showed that unclear regions on the conventional image were clearly depicted on applying the method, and that the produced images were quite interesting for diagnosis of cerebral diseases such as aneurysm and encephaloma. The quantitative and qualitative evaluations showed that the method was appropriate for blood vessel segmentation.
mr angiography image segmentation blood vessel watershed segmentation artificial neural network medical imaging