Jia Chen¹, Zhiqiang He¹, Dayong Zhu¹, Bei Hui^1,*, Rita Yi Man Li², Xiao-Guang Yue^3,4,5

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 73-95, 2022, DOI:10.32604/cmes.2022.018565

Abstract Medical image segmentation plays an important role in clinical diagnosis, quantitative analysis, and treatment process. Since 2015, U-Net-based approaches have been widely used for medical image segmentation. The purpose of the U-Net expansive path is to map low-resolution encoder feature maps to full input resolution feature maps. However, the consecutive deconvolution and convolutional operations in the expansive path lead to the loss of some high-level information. More high-level information can make the segmentation more accurate. In this paper, we propose MU-Net, a novel, multi-path upsampling convolution network to retain more high-level information. The MU-Net mainly consists of three parts: contracting… More >

Rizwan Taj¹, Feng Tao^1,*, Shahzada Khurram², Ateeq Ur Rehman³, Syed Kamran Haider⁴, Akber Abid Gardezi⁵, Saima Kanwal¹

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1309-1324, 2022, DOI:10.32604/cmes.2022.017650

Abstract In telemedicine, the realization of reversible watermarking through information security is an emerging research field. However, adding watermarks hinders the distribution of pixels in the cover image because it creates distortions (which lead to an increase in the detection probability). In this article, we introduce a reversible watermarking method that can transmit medical images with minimal distortion and high security. The proposed method selects two adjacent gray pixels whose least significant bit (LSB) is different from the relevant message bit and then calculates the distortion degree. We use the LSB pairing method to embed the secret matrix of patient record into… More >

Abhishek Kumar^1,5, Kamred Udham Singh², Visvam Devadoss Ambeth Kumar³, Tapan Kant⁴, Abdul Khader Jilani Saudagar^5,*, Abdullah Al Tameem⁵, Mohammed Al Khathami⁵, Muhammad Badruddin Khan⁵, Mozaherul Hoque Abul Hasanat⁵, Khalid Mahmood Malik⁶

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3107-3125, 2022, DOI:10.32604/cmc.2022.022817

Abstract Computed Tomography (CT) scan and Magnetic Resonance Imaging (MRI) technologies are widely used in medical field. Within the last few months, due to the increased use of CT scans, millions of patients have had their CT scans done. So, as a result, images showing the Corona Virus for diagnostic purposes were digitally transmitted over the internet. The major problem for the world health care system is a multitude of attacks that affect copyright protection and other ethical issues as images are transmitted over the internet. As a result, it is important to apply a robust and secure watermarking technique to… More >

Baowei Wang^1,2,3,*, Weishen Wang¹, Peng Zhao¹, Naixue Xiong⁴

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2633-2652, 2022, DOI:10.32604/cmc.2022.022291

Abstract Most of the existing zero-watermark schemes for medical images are only appropriate for a single grayscale image. When they protect a large number of medical images, repeating operations will cause a significant amount of time and storage costs. Hence, this paper proposes an efficient zero-watermark scheme for multiple color medical images based on quaternion generalized Fourier descriptor (QGFD). Firstly, QGFD is utilized to compute the feature invariants of each color image, then the representative features of each image are selected, stacked, and reshaped to generate a feature matrix, which is then binarized to get a binary feature image. Copyright information… More >

Jinrong Hu¹, Lujin Li¹, Ying Fu¹, Maoyang Zou¹, Jiliu Zhou¹, Shanhui Sun^2,*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2773-2788, 2022, DOI:10.32604/cmc.2022.017916

Abstract Deformable medical image registration plays a vital role in medical image applications, such as placing different temporal images at the same time point or different modality images into the same coordinate system. Various strategies have been developed to satisfy the increasing needs of deformable medical image registration. One popular registration method is estimating the displacement field by computing the optical flow between two images. The motion field (flow field) is computed based on either gray-value or handcrafted descriptors such as the scale-invariant feature transform (SIFT). These methods assume that illumination is constant between images. However, medical images may not always… More >

Rabha W. Ibrahim^1,*, Husam Yahya², Arkan J. Mohammed³, Nadia M. G. Al-Saidi⁴, Dumitru Baleanu^5,6,7

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 937-950, 2022, DOI:10.32604/iasc.2022.021954

Abstract The interest in using fractal theory and its applications has grown in the field of image processing. Image enhancement is one of the feature processing tools, which aims to improve the details of an image. The enhancement of digital pictures is a challenging task due to the unforeseeable variation in the quality of the captured images. In this study, we present a mathematical model using a local conformable differential operator (LCDO). The proposed model is formulated by the theory of cantor fractal to generalize the definition of LCDO. The main advantage of utilizing LCDO for image enhancement is its capability… More >

Arwa Mashat¹, Surbhi Bhatia^2,*, Ankit Kumar³, Pankaj Dadheech³, Aliaa Alabdali⁴

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 841-857, 2022, DOI:10.32604/iasc.2022.021636

Abstract The transmission of medical records over indiscrete and open networks has caused an increase in fraud involving stealing patients’ information, owing to a lack of security over these links. An individual’s medical documents represent confidential information that demands strict protocols and security, chiefly to protect the individual’s identity. Medical image protection is a technology intended to transmit digital data and medical images securely over public networks. This paper presents some background on the different methods used to provide authentication and protection in medical information security. This work develops a secure cryptography-based medical image reclamation algorithm based on a combination of… More >

V. Thanikachalam^1,*, S. Shanthi², K. Kalirajan³, Sayed Abdel-Khalek^4,5, Mohamed Omri⁶, Lotfi M. Ladhar⁷

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5667-5681, 2022, DOI:10.32604/cmc.2022.020965

Abstract The rapid development of biomedical imaging modalities led to its wide application in disease diagnosis. Tongue-based diagnostic procedures are proficient and non-invasive in nature to carry out secondary diagnostic processes ubiquitously. Traditionally, physicians examine the characteristics of tongue prior to decision-making. In this scenario, to get rid of qualitative aspects, tongue images can be quantitatively inspected for which a new disease diagnosis model is proposed. This model can reduce the physical harm made to the patients. Several tongue image analytical methodologies have been proposed earlier. However, there is a need exists to design an intelligent Deep Learning (DL) based disease… More >

Walid El-Shafai¹, Samy Abd El-Nabi^1,2, El-Sayed M. El-Rabaie¹, Anas M. Ali^1,2, Naglaa F. Soliman^3,*, Abeer D. Algarni³, Fathi E. Abd El-Samie^1,3

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6107-6125, 2022, DOI:10.32604/cmc.2022.020698

Abstract Effective medical diagnosis is dramatically expensive, especially in third-world countries. One of the common diseases is pneumonia, and because of the remarkable similarity between its types and the limited number of medical images for recent diseases related to pneumonia, the medical diagnosis of these diseases is a significant challenge. Hence, transfer learning represents a promising solution in transferring knowledge from generic tasks to specific tasks. Unfortunately, experimentation and utilization of different models of transfer learning do not achieve satisfactory results. In this study, we suggest the implementation of an automatic detection model, namely CADTra, to efficiently diagnose pneumonia-related diseases. This… More >

Aayesha¹, Muhammad Bilal Qureshi², Muhammad Afzaal³, Muhammad Shuaib Qureshi⁴, Jeonghwan Gwak^5,6,7,8,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5601-5630, 2022, DOI:10.32604/cmc.2022.020348

Abstract Detection of epileptic seizures on the basis of Electroencephalogram (EEG) recordings is a challenging task due to the complex, non-stationary and non-linear nature of these biomedical signals. In the existing literature, a number of automatic epileptic seizure detection methods have been proposed that extract useful features from EEG segments and classify them using machine learning algorithms. Some characterizing features of epileptic and non-epileptic EEG signals overlap; therefore, it requires that analysis of signals must be performed from diverse perspectives. Few studies analyzed these signals in diverse domains to identify distinguishing characteristics of epileptic EEG signals. To pose the challenge mentioned… More >