Medical Image Magnification Based on Original and Estimated Pixel Selection Models

O Akbarzadeh, M R Khosravi, B Khosravi, P Halvaee


Objective: Our aim in this paper is to evaluate different types of pixel selection models in terms of pixel originality in medical image reconstruction problems. A previous investigation showed that selecting far original pixels has highly better performance than using near unoriginal/estimated pixels while magnifying some benchmarks in digital image processing. Therefore, this finding could be a guide in the design of new zooming algorithm. Thus, we wish to reevaluate this approach in the application of medical image reconstruction, which significantly has different features in comparison to the dataset used in the first research.

Methods: In the current study, we apply two classical interpolators, cubic convolution (CC) and bi-linear (BL), in order to reconstruct medical images in spatial domain. In addition to the interpolators, we use some geometrical image transforms for creating the reconstruction models.

Results: The results clearly demonstrate that despite the absolute preference of the original pixel selection model in the first research, we cannot see this preference in medical dataset in which the results of BL interpolator for both tested models (original and estimated pixel selection models) are approximately the same as each other and for CC interpolator, we only see a relatively better preference for the original pixel selection model.

Conclusion: The current research reveals the fact that selection models are not a general factor in reconstruction problems, and the structure of the basic interpolators is also a main factor which affects the final results. Therefore, in designing new magnification algorithms for direct zoom of medical images, it is possible that selecting original pixels might not have a considerable impact on the algorithm performance. In other words, some interpolators in medical dataset can be affected by the selection models, while, some cannot.


Image Interpolation; Image Magnification and Resizing; Direct Reconstruction; Cubic Convolution (CC); Bi-Linear (BL); Medical Images

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eISSN: 2251-7200        JBPE NLM ID: 101589641

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                                                                        Chairman and Editor in Chief

                                                                              Dr. Alireza Mehdizadeh

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