Self-organizing map for segmenting 3D biological images

An image processing method for features extraction and segmentation from three-dimensional (3D) image datasets is presented. Kohonen's self-organizing map (SOM) is used to perform segmentation. Previously, the segmentation method worked on a 2D dataset based on a projection of the three-dimensional dataset (Nguyen et al., 1998). Our 3D approach to segment biological images preserves the 3D object orientations with respect to the surrounding cell volume. A few examples from genetics and brain analysis are provided in order to demonstrate the performance of the proposed method.