Author(s)

Wisan Dhammatorn¹, Hiroyuki Shioya²

¹Department of Information and Electronic Engineering, Graduate School, Muroran Institute of Technology, Hokkaido, Japan.
²College of Information and Systems, Muroran Institute of Technology, Hokkaido, Japan.

Computer Tomography in medical imaging provides human internal body pictures in the digital form. The more quality images it provides, the better information we get. Normally, medical imaging can be constructed by projection data from several perspectives. In this paper, our research challenges and describes a numerical method for refining the image of a Region of Interest (ROI) by constructing support within a standard CT image. It is obvious that the quality of tomographic slice is affected by artifacts. CT using filter and K-means clustering provides a way to reconstruct an ROI with minimal artifacts and improve the degree of the spatial resolution. Experimental results are presented for improving the reconstructed images, showing that the approach enhances the overall resolution and contrast of ROI images. Our method provides a number of advantages: robustness with noise in projection data and support construction without the need to acquire any additional setup.

Sparse CT Reconstruction, K-Means Clustering, Total Variation Filtering, Maximum Entropy Thresholding

Dhammatorn, W. and Shioya, H. (2017) A Support Construction for CT Image Based on K-Means Clustering. Journal of Computer and Communications, 5, 137-151. doi: 10.4236/jcc.2017.51011.