Browsing by Author "Jalab, Hamid A."

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Citation Count: Jalab, Hamid A...et al. (2021). "A New Medical Image Enhancement Algorithm Based on Fractional Calculus", CMC-Computers Materials & Continua, Vol. 68, No. 2, pp. 1467-1483.
A New Medical Image Enhancement Algorithm Based on Fractional Calculus
(2021) Jalab, Hamid A.; Ibrahim, Rabha W.; Hasan, Ali M.; Karim, Faten Khalid; Al-Shamasneh, Ala'a R.; Baleanu, Dumitru; 56389
The enhancement of medical images is a challenging research task due to the unforeseeable variation in the quality of the captured images. The captured images may present with low contrast and low visibility, which might influence the accuracy of the diagnosis process. To overcome this problem, this paper presents a new fractional integral entropy (FITE) that estimates the unforeseeable probabilities of image pixels, posing as the main contribution of the paper. The proposed model dynamically enhances the image based on the image contents. The main advantage of FITE lies in its capability to enhance the low contrast intensities through pixels? probability. Initially, the pixel probability of the fractional power is utilized to extract the illumination value from the pixels of the image. Next, the contrast of the image is then adjusted to enhance the regions with low visibility. Finally, the fractional integral entropy approach is implemented to enhance the low visibility contents from the input image. Tests were conducted on brain MRI, lungs CT, and kidney MRI scans datasets of different image qualities to show that the proposed model is robust and can withstand dramatic variations in quality. The obtained comparative results show that the proposed image enhancement model achieves the best BRISQUE and NIQE scores. Overall, this model improves the details of brain MRI, lungs CT, and kidney MRI scans, and could therefore potentially help the medical staff during the diagnosis process.
Citation Count: Jalab, Hamid A...et al. (2021). "Fractional Rényi Entropy Image Enhancement for Deep Segmentation of Kidney MRI", Computers, Materials and Continua, Vol. 67, no. 2, pp. 2061-2075.
Fractional Rényi Entropy Image Enhancement for Deep Segmentation of Kidney MRI
(2021) Jalab, Hamid A.; Al-Shamasneh, Ala'a R.; Shaiba, Hadil; Ibrahim, Rabha W.; Baleanu, Dumitru; 56389
Recently, many rapid developments in digital medical imaging have made further contributions to health care systems. The segmentation of regions of interest in medical images plays a vital role in assisting doctors with their medical diagnoses. Many factors like image contrast and quality affect the result of image segmentation. Due to that, image contrast remains a challenging problem for image segmentation. This study presents a new image enhancement model based on fractional Rényi entropy for the segmentation of kidney MRI scans. The proposed work consists of two stages: enhancement by fractional Rényi entropy, and MRI Kidney deep segmentation. The proposed enhancement model exploits the pixel’s probability representations for image enhancement. Since fractional Rényi entropy involves fractional calculus that has the ability to model the non-linear complexity problem to preserve the spatial relationship between pixels, yielding an overall better details of the kidney MRI scans. In the second stage, the deep learning kidney segmentation model is designed to segment kidney regions in MRI scans. The experimental results showed an average of 95.60% dice similarity index coefficient, which indicates best overlap between the segmented bodies with the ground truth. It is therefore concluded that the proposed enhancement model is suitable and effective for improving the kidney segmentation performance. © 2021 Tech Science Press. All rights reserved.
Citation Count: Al-Azawi, Razi J...et al. (2021). "Image Splicing Detection Based on Texture Features with Fractal Entropy", Computers, Materials and Continua, Vol. 69, No. 3, pp. 3903-3915.
Image Splicing Detection Based on Texture Features with Fractal Entropy
(2021) Al-Azawi, Razi J.; Al-Saidi, Nadiam.G.; Jalab, Hamid A.; Ibrahim, Rabhaw; Baleanu, Dumitru; 56389
Over the past years, image manipulation tools have become widely accessible and easier to use, whichmade the issue of image tampering farmore severe. As a direct result to the development of sophisticated image-editing applications, it has become near impossible to recognize tampered images with naked eyes. Thus, to overcome this issue, computer techniques and algorithms have been developed to help with the identification of tampered images. Research on detection of tampered images still carries great challenges. In the present study, we particularly focus on image splicing forgery, a type of manipulation where a region of an image is transposed onto another image. The proposed study consists of four features extraction stages used to extract the important features from suspicious images, namely, Fractal Entropy (FrEp), local binary patterns (LBP), Skewness, and Kurtosis. The main advantage of FrEp is the ability to extract the texture information contained in the input image. Finally, the "support vector machine" (SVM) classification is used to classify images into either spliced or authentic. Comparative analysis shows that the proposed algorithm performs better than recent state-of-the-art of splicing detection methods. Overall, the proposed algorithm achieves an ideal balance between performance, accuracy, and efficacy, which makes it suitable for real-world applications. This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2021 Tech Science Press. All rights reserved.