Improved partial differential equation-based total variation approach to non-subsampled contourlet transform for medical
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Improved partial differential equation-based total variation approach to non-subsampled contourlet transform for medical image denoising Sreedhar Kollem1,4
· Katta Ramalinga Reddy2 · Duggirala Srinivasa Rao3
Received: 14 March 2020 / Revised: 9 July 2020 / Accepted: 26 August 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract This article proposes an improved partial differential equation (PDE)-based total variation (TV) model that enhances grey and coloured brain tumour images obtained by magnetic resonance imaging. A nonsubsampled contourlet transform was applied to images from standard databases that converted into lowpass and highpass (or bandpass) contourlet coefficients. An improved version of the power-law transform method was used on the lowpass contourlet coefficients, and an adaptive threshold method was applied to the highpass (or bandpass) contourlet coefficients. The inverse contourlet transform was performed on all the enhanced contourlet coefficients to generate a complete brain tumour image. Finally, the PDE-based TV model was applied to this image to produce the denoised image. The performance of the suggested method was calculated in terms of the peak signal-to-noise ratio, mean square error, and structural similarity index. This method achieved the best peak signalto-noise ratio, mean square error, and structural similarity index of 77.9846 dB, 0.00012612, and 97.895%, respectively, compared to the conventional PDE+modified transform-based gamma correction, adaptive PDE+generalized cross-validation, parallel magnetic resonance imaging, and Berkeley wavelet transform+support vector machine methods. Keywords Nonsubsampled contourlet transform · Power-law transform method · Adaptive threshold method · Partial differential equations · Directional filter bank · Total variation model
Sreedhar Kollem
[email protected] 1
Department of ECE, School of Engineering, SR University, Warangal 506371, Telangana, India
2
Department of ETM, G. Narayanamma Institute of Technology and Science, Hyderabad 500104, Telangana, India
3
Department of ECE, JNTUH college of engineering, Kukatpally 500085, Hyderabad, Telangana, India
4
Research Scholar, Department of ECE, JNTUH University, Hyderabad 500085, Telangana, India
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1 Introduction Medical image denoising is important for various image processing applications, such as medical imaging and computer vision [16]. Image denoising is a preprocessing method that improves images by removing noise while preserving the image edges and is utilized in many applications, such as image segmentation, image restoration, and image classification. In particular, image denoising is employed in medical image processing for proper diagnosis [13]. Although many algorithms have been proposed for image denoising, noise reduction remains an open challenge, especially in situations in which images are acquired under high-noise conditions. An effective image denoising model should eliminate noise as much as
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