Meng Yang¹, Jingbing Li^1,2,*, Uzair Aslam Bhatti^1,2, Chunyan Shao¹, Yen-Wei Chen³

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5539-5554, 2023, DOI:10.32604/cmc.2023.036904

Abstract With the development of digitalization in healthcare, more and more information is delivered and stored in digital form, facilitating people’s lives significantly. In the meanwhile, privacy leakage and security issues come along with it. Zero watermarking can solve this problem well. To protect the security of medical information and improve the algorithm’s robustness, this paper proposes a robust watermarking algorithm for medical images based on Non-Subsampled Shearlet Transform (NSST) and Schur decomposition. Firstly, the low-frequency subband image of the original medical image is obtained by NSST and chunked. Secondly, the Schur decomposition of low-frequency blocks to get stable values, extracting… More >

S. Mohan Kumar^*, T. Kumanan

Computer Systems Science and Engineering, Vol.44, No.1, pp. 833-844, 2023, DOI:10.32604/csse.2023.022385

Abstract The main cause of skin cancer is the ultraviolet radiation of the sun. It spreads quickly to other body parts. Thus, early diagnosis is required to decrease the mortality rate due to skin cancer. In this study, an automatic system for Skin Lesion Classification (SLC) using Non-Subsampled Shearlet Transform (NSST) based energy features and Support Vector Machine (SVM) classifier is proposed. At first, the NSST is used for the decomposition of input skin lesion images with different directions like 2, 4, 8 and 16. From the NSST’s sub-bands, energy features are extracted and stored in the feature database for training.… More >

S. Aiswarya^*, R. Gomathi

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 969-982, 2022, DOI:10.32604/iasc.2022.025475

Abstract Reversible Data Hiding (RDH) is the process of transferring secret data hidden inside cover media to the recipient so the recipient can securely retrieve both the secret data and cover media. The RDH approach is applied in this study in the field of telemedicine, and medical-secret data is conveyed privately via medical cover video. Morse code-based data encryption technique tends to encrypt the medical-secret data by compression using the Arithmetic coding technique. Discrete Shearlet transform (DST) compresses the selected frame from the medical cover video and the compressed secret data is embedded into the compressed frame using logical operations. On… More >

Hilly Gohain Baruah^*, Vijay Kumar Nath, Deepika Hazarika

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 137-164, 2022, DOI:10.32604/cmes.2022.018339

Abstract With the increasing popularity of high-resolution remote sensing images, the remote sensing image retrieval (RSIR) has always been a topic of major issue. A combined, global non-subsampled shearlet transform (NSST)-domain statistical features (NSSTds) and local three dimensional local ternary pattern (3D-LTP) features, is proposed for high-resolution remote sensing images. We model the NSST image coefficients of detail subbands using 2-state laplacian mixture (LM) distribution and its three parameters are estimated using Expectation-Maximization (EM) algorithm. We also calculate the statistical parameters such as subband kurtosis and skewness from detail subbands along with mean and standard deviation calculated from approximation subband, and… More >

R. Muthaiyan^1,*, Dr M. Malleswaran²

Computer Systems Science and Engineering, Vol.40, No.1, pp. 299-312, 2022, DOI:10.32604/csse.2022.018034

Abstract In this paper, an Automated Brain Image Analysis (ABIA) system that classifies the Magnetic Resonance Imaging (MRI) of human brain is presented. The classification of MRI images into normal or low grade or high grade plays a vital role for the early diagnosis. The Non-Subsampled Shearlet Transform (NSST) that captures more visual information than conventional wavelet transforms is employed for feature extraction. As the feature space of NSST is very high, a statistical t-test is applied to select the dominant directional sub-bands at each level of NSST decomposition based on sub-band energies. A combination of features that includes Gray Level… More >

Peizhou Yan¹, Jiancheng Zou^2,*, Zhengzheng Li¹, Xin Yang³

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 213-225, 2021, DOI:10.32604/iasc.2021.016201

Abstract Within the application of driving assistance systems, the detection of driver’s facial features in the cab for a spectrum of luminosities is mission critical. One method that addresses this concern is infrared and visible image fusion. Its purpose is to generate an aggregate image which can granularly and systematically illustrate scene details in a range of lighting conditions. Our study introduces a novel approach to this method with marked improvements. We utilize non-subsampled shearlet transform (NSST) to obtain the low and high frequency sub-bands of infrared and visible imagery. For the low frequency sub-band fusion, we incorporate the local average… More >

V. Vidya Lakshmi^1,*, J. S. Leena Jasmine²

Computer Systems Science and Engineering, Vol.37, No.2, pp. 233-245, 2021, DOI:10.32604/csse.2021.015700

Abstract Melanoma or skin cancer is the most dangerous and deadliest disease. As the incidence and mortality rate of skin cancer increases worldwide, an automated skin cancer detection/classification system is required for early detection and prevention of skin cancer. In this study, a Hybrid Artificial Intelligence Model (HAIM) is designed for skin cancer classification. It uses diverse multi-directional representation systems for feature extraction and an efficient Exponentially Weighted and Heaped Multi-Layer Perceptron (EWHMLP) for the classification. Though the wavelet transform is a powerful tool for signal and image processing, it is unable to detect the intermediate dimensional structures of a medical… More >