A Moment Based Fractional Fourier Transform Scheme for MR Image Classification

Mastromichalakis, Stamatis; Chountasis, Spiros

doi:10.3103/s0146411621040052

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…2 shows the fractional Fourier transform. This approach is widely used in optical problems (Hricha et al, 2020;Saad et al, 2018;Zhao et al, 2018;Dai et al, 2021), image processing (Faragallah et al, 2021;Kaur et al, 2021;Massihi and Rashidi, 2021;Zhang et al, 2021), robotics (Lopez et al, 2021), and medical signal processing (Gupta et al, 2021;Alqahtani et al, 2022;Mastromichalakis and Chountasis, 2021).…”

Section: Fractional Fourier Transformmentioning

confidence: 99%

Adaptive Image Super-Resolution Algorithm Based on Fractional Fourier Transform

2022

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Super-resolution imaging is a critical image processing stage that improves visual image quality. Super-resolution imaging has a wide array of use in different fields, such as medical, satellite, and astronomical images. The correct execution of this stage could increase the accuracy and quality of any available processes in different executive fields. Learning methods, especially deep learning, have become much more popular in recent years for performing the super-resolution imaging process. Methods with this approach have high-quality levels but lack appropriate performance times. This study intends to perform super-resolution imaging using an algorithmic approach based on the particle swarm optimization algorithm and the fractional Fourier transform. The test results on a dataset show the 92.16% accuracy of this proposed method.

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Section: Fractional Fourier Transformmentioning

confidence: 99%

Adaptive Image Super-Resolution Algorithm Based on Fractional Fourier Transform

2022

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Bendlets and Ensemble Learning Based MRI Brain Classification System

Muthaiyan¹,

Malleswaran²

2022

Intelligent Automation &Amp; Soft Computing

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Brain tumours are composed of cells where the growth is unrestrained. Though the incidence rate is lower, it is a serious threatening disease to human lives. For effective treatment, an accurate and quick method to classify Magnetic Resonance Imaging (MRI) is required. To identify the meaningful patterns and to interpret images, pattern recognition algorithms are developed. In this work, an extension of Shearlet transform named Bendlets is employed to interpret MRI images and decision making is done by ensemble learning using k-Nearest Neighbor (kNN), Naive Bayesian and Support Vector Machine (SVM) classifiers. The Bendlet and Ensemble Learning (BEL) based system utilizes Bendlet Co-Occurrence Features (BCFs) and Histograms of Positive and Negative Bendlet Coefficients (HPBC & HNBC) from the dominant sub-band as texture descriptors. The rate of classification by the BEL system for the 200 images from REpository of Molecular BRAin Neoplasia DaTa (REMBRANDT) is 99.5% at the initial stage (normal/abnormal classification) and 99% at the final stage (low-risk/high-risk classification). Based on the results, the implementation of BEL system could provide continuous monitoring of the progress of brain tumour very effectively and also offers a real-time response.

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A Moment Based Fractional Fourier Transform Scheme for MR Image Classification

Cited by 2 publications

References 16 publications

Adaptive Image Super-Resolution Algorithm Based on Fractional Fourier Transform

Adaptive Image Super-Resolution Algorithm Based on Fractional Fourier Transform

Bendlets and Ensemble Learning Based MRI Brain Classification System

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