Medical Image Zooming by Using Rational Bicubic Ball Function

Medical Image Zooming by Using Rational Bicubic Ball Function

Samsul Ariffin Abdul Karim (Universiti Teknologi PETRONAS, Malaysia), Nur Atiqah Binti Zulkifli (Universiti Teknologi PETRONAS, Malaysia), A'fza Binti Shafie (Universiti Teknologi PETRONAS, Malaysia), Muhammad Sarfraz (Kuwait University, Kuwait), Abdul Ghaffar (Ton Duc Thang University, Vietnam) and Kottakkaran Sooppy Nisar (Prince Sattam bin Abdulaziz University, Saudi Arabia)
DOI: 10.4018/978-1-7998-4444-0.ch008
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Abstract

This chapter deals with image processing in the specific area of image zooming via interpolation. The authors employ bivariate rational cubic ball function defined on rectangular meshes. These bivariate spline have six free parameters that can be used to alter the shape of the surface without needed to change the data. It also can be used to refine the resolution of the image. In order to cater the image zomming, they propose an efficient algorithm by including image downscaling and upscaling procedures. To measure the effectiveness of the proposed scheme, they compare the performance based on the value of peak signal-to-noise ratio (PSNR) and root mean square error (RMSE). Comparison with existing schemes such as nearest neighbour (NN), bilinear (BL), bicubic (BC), bicubic Hermite (BH), and existing scheme Karim and Saaban (KS) have been made in detail. From all numerical results, the proposed scheme gave higher PSNR value and smaller RMSE value for all tested images.
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1. Introduction

The technology of image processing in medical field is vigorously grow in the industry as the demand increases from time to time. Foremost among them have been explored the digital medical imaging modalities for past two decades. For instance, the technology in film scanners, ultrasound, Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), Single Positron Emission Computed Tomography (SPECT), Digital Subtraction Angiography (DSA), and Magnetic Source Imaging (MSI) etc. These modalities constitute about 30% of the radiologic imaging examinations and 70% of examinations on skull, chest, breast, abdomen and bone which has been done in conventional x-rays and digital luminescent radiography (Wong, 2012). The image within the patient’s body have different kind of film digitizers such as, laser scanner, solid-state camera, drumscanner, and video camera that capable to convert X-ray films into digital format for image processing. Therefore, image zooming on the selected parts are important especially when the expert want to detect any anomalies at the body paertss. Usually in imaze zooming, a two-dimensional (2-D) specifically grayscale medical image such as X-ray film with a size of 256 x 256 bits are employed as a tested image.

Image zooming is one of application in image interpolation. It is process of magnifying or reducing the size of the image and interpolation activity takes place during the process. Usually medical digital images produced have low resolution images due to the nature of the acquisition. Moreover, when the medical image is zoomed at certain part will cause the reduction of resolution if its done without interpolation. There are numbers of studies proposed a method and solution to improve the visual and objective quality of a medical image such as image upscaling, image zooming and image rotation. From the previous works, spline is the most common method for image interpolation that used to attain high quality of medical images. For example, Gao et al. (2008) proposed trigonometric spline with control parameters for medical image interpolation. Another study, they introduced the bivariate rational spline to interpolate medical image in respective activity such resizing, zooming and enhancement (Gao et al., 2008; Gao et al., 2009; Zhang et al., 2009; Zhang et al., 2012). Meanwhile, in Pal (2016), the authors has studied the zooming operation based on the principle of analog clock and utilizing the combination point and eighbourhood in image processing.

Table 1 summariz some related literature review including our recent study i.e. Zulkifli et al. (2019). Meanwhile Table 1 shows some abbreviations used in this study.

Table 1.
Literature review
SchemeFeatures
Abbas et al. (2017)Advantage: The model have free parameters to modify the final resolution of image, easy to understand and implement, and the results are presented in both subjective and objective measurements.
Implication: They used Bernstein Bézier cubic trigonometric basis functions with no free parameters in description.
Gao et al. (2008)Advantage: The model can pass through the known data, simple and explicit expression, and the expression is piecewise with free parameters.
Implication: The results only display visually and image of the comparison method is not very noticeable.
Gao et al. (2009)Advantage: The model can pass through the known data, simple and explicit expression, and the expression is piecewise with free parameters.
Implication: Extent work from Gao et al. (2008) in different application.
Zhang et al. (2009)Advantage: The model can pass through the known data, simple and explicit expression, and the expression is piecewise with free parameters.
Implication: The results only display visually and image of the comparison method is not very noticeable.
Zhang et al. (2012)Advantage: Have free parameters, can preserve high frequency of image on region edge, keep the contour clear.
Implication: Have only one parameter.
Pal (2016)Advantage: Can evaluates many medical image from various sources such as CT- scan, MRI and X-ray.
Disadvantage: Using polynomial with bicubic interpolation basis which is not very familiar method and have no free parameter in description.
Zulkifli et al. (2019)Discussed the application of bivariate Ball spline function in image interpolation with factors 2 and 4. Their scheme is better than some existing scheme.

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