An improved algorithm to compute tensor scale and its application to medical image interpolation

2009 ◽  
Author(s):  
Ziyue Xu ◽  
Milan Sonka ◽  
Punam K. Saha
Keyword(s):  
Medical Image ◽  
Image Interpolation ◽  
Tensor Scale ◽  
Improved Algorithm

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

A Novel Edge Detection Algorithm for Medical Image

2012 ◽  
Vol 151 ◽  
pp. 653-656
Author(s):  
Zhan Chun Ma ◽  
Xiao Mei Ning
Keyword(s):  
Edge Detection ◽  
Medical Image ◽  
Detection Algorithm ◽  
Canny Operator ◽  
Edge Detection Algorithm ◽  
Image Edge Detection ◽  
Traditional Algorithm ◽  
Image Edge ◽  
Improved Algorithm

CANNY operator had widely usage for edge detection; however it also had certain deficiencies. So the traditional CANNY operator about this is improved and puts forward a kind of new algorithm used for image edge detection. Compared improved algorithm with traditional algorithm for edge detection, simulations shows that new algorithm is more effective for image edge detection and the clearer detection result is obtained.

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

An Improved Algorithm for Color Medical Image Compression Based on DCT

2014 ◽  
Vol 602-605 ◽  
pp. 3114-3118
Author(s):  
Qiang Yang ◽  
Hua Jun Wang ◽  
Xue Gang Luo
Keyword(s):  
Image Compression ◽  
Medical Image ◽  
Color Image ◽  
Compression Algorithm ◽  
Medical Image Compression ◽  
Current Image ◽  
Dct Transform ◽  
The Mean ◽  
Color Image Compression ◽  
Improved Algorithm

Medical image compression technology has great significance in the field of medical image engineering and clinical application. The current image compression technology is mainly for gray image, Few people study on medical image color compression algorithm. This paper presents a compression algorithm for color image based on DCT. First, the algorithm change the color medical image of RGB space to SAT space, this transformation ensures the medical image without distortion, and effectively reduce the mean image of each dimension of component. Then, the algorithm make the DCT transform for color image compression in the SAT space. Experimental shows that the improved algorithm in color medical image compression has achieved good results.

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