scholarly journals Quantitative Comparison of Sinc-Approximating Kernels for Medical Image Interpolation

1999 ◽  
pp. 210-217 ◽  
Author(s):  
Erik H. W. Meijering ◽  
Wiro J. Niessen ◽  
Josien P. W. Pluim ◽  
Max A. Viergever
Keyword(s):  
Medical Image ◽  
Image Interpolation ◽  
Quantitative Comparison

scholarly journals Trigonometric spline for medical image interpolation

2017 ◽  
Vol 45 (1) ◽  
pp. 33
Author(s):  
Samreen Abbas ◽  
Malik Zawwar Hussain ◽  
Misbah Irshad
Keyword(s):  
Medical Image ◽  
Image Interpolation ◽  
Trigonometric Spline

Medical image interpolation based on 3D Lanczos filtering

2019 ◽  
Vol 8 (3) ◽  
pp. 294-300
Author(s):  
Thiago Moraes ◽  
Paulo Amorim ◽  
Jorge Vicente Da Silva ◽  
Helio Pedrini
Keyword(s):  
Medical Image ◽  
Image Interpolation

scholarly journals Analysis of Medical Image Resizing Using Bicubic Interpolation Algorithm

2021 ◽  
Vol 14 (1) ◽  
pp. 20
Author(s):  
Bambang Krismono Triwijoyo ◽  
Ahmat Adil
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Nearest Neighbor ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Image Interpolation ◽  
Interpolation Algorithm ◽  
Image Size ◽  
Pixel Value ◽  
Bicubic Interpolation

Image interpolation is the most basic requirement for many image processing tasks such as medical image processing. Image interpolation is a technique used in resizing an image. To change the image size, each pixel in the new image must be remapped to a location in the old image to calculate the new pixel value. There are many algorithms available for determining the new pixel value, most of which involve some form of interpolation between the closest pixels in the old image. In this paper, we use the Bicubic interpolation algorithm to change the size of medical images from the Messidor dataset and then analyze it by measuring it using three parameters Mean Square Error (MSE), Root Mean Squared Error (RMSE), and Peak Signal-to-Noise Ratio (PSNR), and compare the results with Bilinear and Nearest-neighbor algorithms. The results showed that the Bicubic algorithm is better than Bilinear and Nearest-neighbor and the larger the image dimensions are resized, the higher the degree of similarity to the original image, but the level of computation complexity also increases.

scholarly journals Quantitative evaluation of convolution-based methods for medical image interpolation

2001 ◽  
Vol 5 (2) ◽  
pp. 111-126 ◽  
Author(s):  
Erik H.W. Meijering ◽  
Wiro J. Niessen ◽  
Max A. Viergever
Keyword(s):  
Quantitative Evaluation ◽  
Medical Image ◽  
Image Interpolation

Medical Image Interpolation Algorithm Based on the Organization of Correlation

2014 ◽  
Vol 513-517 ◽  
pp. 3744-3749
Author(s):  
Yue Zhou ◽  
Jia Xin Chen
Keyword(s):  
Computational Complexity ◽  
3D Reconstruction ◽  
Medical Image ◽  
Image Interpolation ◽  
Interpolation Algorithm ◽  
Lower Efficiency ◽  
Interpolation Methods

According to the problem such as blurred border of images and lower efficiency caused by present interpolation methods, an interslice interpolation based on the relativity for medical image is presented in this paper. This algorithm makes good use of voxel relativity and structure relativity and then the different methods are adopted to interpolate the different points, In addition, error checkout is introduced to check the mismatching points.The experiments show that the proposed algorithm has less computational complexity and improves the quality of image, at the same time, the result can be used to 3D reconstruction effectively.

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