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Interpreting Low Resolution CT Scan Images using Interpolation Functions
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 74 - Number 3 |
| Year of Publication: 2013 |
| Authors: Tarun Gulati, Maninder Pal |
10.5120/12869-9831
|
Tarun Gulati, Maninder Pal . Interpreting Low Resolution CT Scan Images using Interpolation Functions. International Journal of Computer Applications. 74, 3 ( July 2013), 50-58. DOI=10.5120/12869-9831
Abstract
This paper focuses on interpreting low resolution computed tomography (CT) scan medical images using interpolation functions. Image processing operations such as zooming and segmentation are very commonly performed on these images in medical sciences. However, it is very challenging to perform such operations because of poor resolution of these images. Over the last several years; significant improvements have been made in this area; however, it is still very challenging. In particularly, zooming of such images is very complicated. For zooming, the process of re-sampling is normally employed. Therefore, this paper focuses on investigating the effect of interpolation functions on zooming low resolution images. For this purpose, ideally, an ideal low-pass filter is preferred; however, the same is difficult to realize in practice. Therefore, four interpolation functions (nearest neighbor, linear, cubic B-spline and high-resolution cubic spline with edge enhancement (-2?a?0)) are investigated in this paper for the low resolution medical CT scan images. From the results, it is found that cubic B-spline and high-resolution cubic spline have a better frequency response than nearest neighbor and linear interpolation functions. When these functions are applied for the purpose of zooming digital images, the best response was obtained with the high-resolution cubic spline functions; however, at the expense of increase in computation time.
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