1. Cube Kohonen Self-Organizing Map (CKSOM) Model
With New Equations in Organizing Unstructured Data
Presenter : Yu-Ting LU Authors : Seng Poh Lim and Habibollah Haron TNNLS

2. Outlines Motivation Objectives Methodology Experiments Conclusions
Comments

3. Motivation
For unstructured data, there is no connectivity information between data points. As a result, incorrect shapes will be obtained during the imaging process.
2-D Kohonen maps are limited because they are unable to cover the whole surface of closed 3-D surface data.

4. closed surface
open surfaces

5. Objectives
The aim of this paper is to use KSOM to organize unstructured data for closed surfaces.
Enhancements to the KSOM for organizing unstructured data for closed 3-D surfaces and solving the problems of 2-D and 3-D KSOM.
本研究的目的在使用KSOM來組織封閉表面的非結構化資料
本文提出增強KSOM組織封閉3-D表面的非結構化資料的方法，和解決2-D和3-D的問題

6. Methodology

7. Methodology – Acquiring data
Talus bone data
5,235 points.

8. Methodology – Acquiring data

9. Methodology – Initializing parameters

10. Methodology – Merging neurons

11. Methodology – Merging neurons

12. Methodology – Merging neurons

13. Methodology – Merging neurons

14. Methodology – Detecting neighbors

15. Methodology – Generating weights, learning process and producing output

16. Experiments - Analysis and validation of images

17. Experiments - Analysis and validation of images

18. Experiments - Analysis and validation of metric evaluation

19. Experiments - Analysis and validation of equations

20. Experiments - Analysis and validation of equations

21. Quantization errors
=0.0001
Quantization errors =

22. Conclusions
The model solved 2-D KSOM problems by covering the whole surface of a closed surface and handled connectivity problems of 3-D KSOM.
The model also contained fewer quantization errors compared to 2-D and 3-D KSOM.

23. Comments Advantages Fewer quantization errors Applications
Self-Organization Map
Organization medical image data