Cube Kohonen Self-Organizing Map (CKSOM) Model With New Equations in Organizing Unstructured Data

• Published in: IEEE transaction on neural networks and learning systems 2013-09, Vol.24 (9), p.1414-1424
• Main Authors: Lim, Seng Poh, Haron, Habibollah
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial intelligence; Biomedical imaging; Bones; Computer science; control theory; systems; Connectionism. Neural networks; Exact sciences and technology; Imaging; Iterative methods; Kohonen self-organizing map (KSOM); Mathematical analysis; Mathematical model; Mathematical models; medical image; Neurons; Organizing; Pattern recognition. Digital image processing. Computational geometry; Self-organizing feature maps; Shape; Software; Surface reconstruction; Three dimensional; Unstructured data
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2013.2259259

Surface reconstruction by using 3-D data is used to represent the surface of an object and perform important tasks. The type of data used is important and can be described as either structured or unstructured. For unstructured data, there is no connectivity information between data points. As a result, incorrect shapes will be obtained during the imaging process. Therefore, the data should be reorganized by finding the correct topology so that the correct shape can be obtained. Previous studies have shown that the Kohonen self-organizing map (KSOM) could be used to solve data organizing problems. However, 2-D Kohonen maps are limited because they are unable to cover the whole surface of closed 3-D surface data. Furthermore, the neurons inside the 3-D KSOM structure should be removed in order to create a correct wireframe model. This is because only the outside neurons are used to represent the surface of an object. The aim of this paper is to use KSOM to organize unstructured data for closed surfaces. KSOM isused in this paper by testing its ability to organize medical image data because KSOM is mostly used in constructing engineering field data. Enhancements are added to the model by introducing class number and the index vector, and new equations are created. Various grid sizes and maximum iterations are tested in the experiments. Based on the results, the number of redundancies is found to be directly proportional to the grid size. When we increase the maximum iterations, the surface of the image becomes smoother. An area formula is used and manual calculations are performed to validate the results. This model is implemented and images are created using Dev C++ and GNUPlot.