• International Journal of Computer Science & Network Security
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• 제24권3호
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• pp.93-100
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• 2024

In this paper, we propose a novel robust blind crypto-watermarking method for medical images security based on hiding of DICOM patient information (patient name, age...) in the medical imaging. The DICOM patient information is encrypted using the AES standard algorithm before its insertion in the medical image. The cover image is divided in blocks of 8x8, in each we insert 1-bit of the encrypted watermark in the hybrid transform domain by applying respectively the 2D-LWT (Lifting wavelet transforms), the 2D-DCT (discrete cosine transforms), and the SVD (singular value decomposition). The scheme is tested by applying various attacks such as noise, filtering and compression. Experimental results show that no visible difference between the watermarked images and the original images and the test against attack shows the good robustness of the proposed algorithm.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.75-82
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• 2022

Medical data is one of the data that must be kept in safe containers, far from intrusion, viewing and modification. With the technological developments in hospital systems and the use of cloud computing, it has become necessary to save, encrypt and even hide data from the eyes of attackers. Medical data includes medical images, whether they are x-ray images of patients or others, or even documents that have been saved in the image format. In this review, we review the latest research and the latest tools and algorithms that are used to protect, encrypt and hide these images, and discuss the most important challenges facing these areas.

• International Journal of Computer Science & Network Security
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• 제21권3호
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• pp.83-93
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• 2021

COVID-19 poses a major risk to global health, highlighting the importance of faster and proper diagnosis. To handle the rise in the number of patients and eliminate redundant tests, healthcare information exchange and medical data are transmitted between healthcare centres. Medical data sharing helps speed up patient treatment; consequently, exchanging healthcare data is the requirement of the present era. Since healthcare professionals share data through the internet, security remains a critical challenge, which needs to be addressed. During the COVID-19 pandemic, computed tomography (CT) and X-ray images play a vital part in the diagnosis process, constituting information that needs to be shared among hospitals. Encryption and image steganography techniques can be employed to achieve secure data transmission of COVID-19 images. This study presents a new encryption with the image steganography model for secure data transmission (EIS-SDT) for COVID-19 diagnosis. The EIS-SDT model uses a multilevel discrete wavelet transform for image decomposition and Manta Ray Foraging Optimization algorithm for optimal pixel selection. The EIS-SDT method uses a double logistic chaotic map (DLCM) is employed for secret image encryption. The application of the DLCM-based encryption procedure provides an additional level of security to the image steganography technique. An extensive simulation results analysis ensures the effective performance of the EIS-SDT model and the results are investigated under several evaluation parameters. The outcome indicates that the EIS-SDT model has outperformed the existing methods considerably.

• 한국정보통신학회논문지
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• 제9권6호
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• pp.1309-1315
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• 2005

의료영상은 디지털이라는 속성으로 인해서 일상생활에 적용되는 저작권법으로 저작권을 보호한다는 것이 어렵다. 특히 의료 영상은 복사를 하면 또 하나의 원본이 생성되므로, 의료 영상 이미지를 생산해 내는 사람의 입장에서는 똑같은 원본을 자신도 모르는 사이에 다른 사람에게 전달하게 된다. 그렇게 되면 과연 누가 이 디지털 작품을 만들었는지에 대한 의료 분쟁이 생길 수밖에 없다. 디지털시대의 의료 환경에서 필수 불가결하게 제기될 수 있는 의료영상보안은 특히 우리나라처럼 의료보험의 부당 청구가 사회적인 큰 물의를 일으키고 있는 시점에서 많은 관심과 연구가 필요하다. 따라서 이 분야의 핵심적인 문제점을 도출하고, 문제점 개선을 위해서 워터 마크를 통해 영상 보안 기법을 제안하였다. 그러나 의료영상의 대표적인 특징인 무결성을 보장 받지 못해 법적인 인증에는 한계가 있음을 알 수 있었다.

• International Journal of Computer Science & Network Security
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• 제24권3호
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• pp.59-70
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• 2024

Background: The COVID-19 pandemic (the form of coronaviruses) developed at the end of 2019 and spread rapidly to almost every corner of the world. It has infected around 25,334,339 of the world population by the end of September 1, 2020 [1] . It has been spreading ever since, and the peak specific to every country has been rising and falling and does not seem to be over yet. Currently, the conventional RT-PCR testing is required to detect COVID-19, but the alternative method for data archiving purposes is certainly another choice for public departments to make. Researchers are trying to use medical images such as X-ray and Computed Tomography (CT) to easily diagnose the virus with the aid of Artificial Intelligence (AI)-based software. Method: This review paper provides an investigation of a newly emerging machine-learning method used to detect COVID-19 from X-ray images instead of using other methods of tests performed by medical experts. The facilities of computer vision enable us to develop an automated model that has clinical abilities of early detection of the disease. We have explored the researchers' focus on the modalities, images of datasets for use by the machine learning methods, and output metrics used to test the research in this field. Finally, the paper concludes by referring to the key problems posed by identifying COVID-19 using machine learning and future work studies. Result: This review's findings can be useful for public and private sectors to utilize the X-ray images and deployment of resources before the pandemic can reach its peaks, enabling the healthcare system with cushion time to bear the impact of the unfavorable circumstances of the pandemic is sure to cause

• International Journal of Computer Science & Network Security
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• 제23권10호
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• pp.209-213
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• 2023

In recent years, the image processing mechanisms are used widely in several medical areas for improving earlier detection and treatment stages, in which the time factor is very important to discover the disease in the patient as possible as fast, especially in various cancer tumors such as the liver cancer. Liver cancer has been attracting the attention of medical and sciatic communities in the latest years because of its high prevalence allied with the difficult treatment. Statistics indicate that liver cancer, throughout world, is the one that attacks the greatest number of people. Over the time, study of MR images related to cancer detection in the liver or abdominal area has been difficult. Early detection of liver cancer is very important for successful treatment. There are few methods available to detect cancerous cells. In this paper, an automatic approach that integrates the intensity-based segmentation and k-means clustering approach for detection of cancer region in MRI scan images of liver.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.1-6
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• 2021

Recently, pre-trained convolutional neural network CNNs have been widely used and applied for medical image classification. These models can utilised in three different ways, for feature extraction, to use the architecture of the pre-trained model and to train some layers while freezing others. In this study, the ResNet18 pre-trained CNNs model is used for feature extraction, followed by the support vector machine for multiple classes to classify medical images from multi-classes, which is used as the main classifier. Our proposed classification method was implemented on Kvasir and PH2 medical image datasets. The overall accuracy was 93.38% and 91.67% for Kvasir and PH2 datasets, respectively. The classification results and performance of our proposed method outperformed some of the related similar methods in this area of study.

• International Journal of Computer Science & Network Security
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• 제21권5호
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• pp.1-8
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• 2021

Prostate cancer is one of the most diagnosed malignancies found across the world today. American cancer society in recent research predicted that over 174,600 new prostate cancer cases found and nearly 31,620 death cases recorded. Researchers are developing modest and accurate methodologies to detect and diagnose prostate cancer. Recent work has been done in radiology to detect prostate tumors using ultrasound imaging and resonance imaging techniques. Transrectal ultrasound and Magnetic resonance images of the prostate gland help in the detection of cancer in the prostate gland. The proposed paper is based on comparison and analysis between two novel image segmentation approaches. Seed region growing and cluster based image segmentation is used to extract the region from trans-rectal ultrasound prostate and MR prostate images. The region of extraction represents the abnormality area that presents in men's prostate gland. Detection of such abnormalities in the prostate gland helps in the identification and treatment of prostate cancer

• 한국멀티미디어학회논문지
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• 제11권5호
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• pp.588-596
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• 2008

환자의 신변보호를 위하여 의료 영상에 비인가 된 사용자의 접근을 제한하는 것은 필수적이다. 본 논문에서는 의료 영상의 접근 제어를 위한 관심 영역(ROI, Region of Interest) 기반의 선택적 암호 기법들을 제안한다. 첫 번째 제안 기법은 웨이블릿 서브밴드들 중 관심영역에 속한 계수의 비트를 랜덤하게 변경하여 암호화를 수행하며, 이미지의 압축과 암호화가 동시에 수행되고 암호화로 인한 압축률에 대한 손실이 적은 특징을 가진다. 두 번째 제안 기법은 안전성의 향상을 위하여 압축된 코드스트림의 관심 영역에 대칭키 기반 암호 알고리즘을 적용하며, 암호화 과정에서 압축률에 영향을 미치지 않는 특징이 있다. 또한 제안 기법들은 JPEG2000의 코드스트림 표준 디코더와 호환성을 제공한다.

• International Journal of Computer Science & Network Security
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• 제24권5호
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• pp.53-63
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• 2024

Alzheimer's disease is a brain disorder that worsens over time and affects millions of people around the world. It leads to a gradual deterioration in memory, thinking ability, and behavioral and social skills until the person loses his ability to adapt to society. Technological progress in medical imaging and the use of artificial intelligence, has provided the possibility of detecting Alzheimer's disease through medical images such as magnetic resonance imaging (MRI). However, Deep learning algorithms, especially convolutional neural networks (CNNs), have shown great success in analyzing medical images for disease diagnosis and classification. Where CNNs can recognize patterns and objects from images, which makes them ideally suited for this study. In this paper, we proposed to compare the performances of Alzheimer's disease detection by using two deep learning methods: You Only Look Once (YOLO), a CNN-enabled object recognition algorithm, and Visual Geometry Group (VGG16) which is a type of deep convolutional neural network primarily used for image classification. We will compare our results using these modern models Instead of using CNN only like the previous research. In addition, the results showed different levels of accuracy for the various versions of YOLO and the VGG16 model. YOLO v5 reached 56.4% accuracy at 50 epochs and 61.5% accuracy at 100 epochs. YOLO v8, which is for classification, reached 84% accuracy overall at 100 epochs. YOLO v9, which is for object detection overall accuracy of 84.6%. The VGG16 model reached 99% accuracy for training after 25 epochs but only 78% accuracy for testing. Hence, the best model overall is YOLO v9, with the highest overall accuracy of 86.1%.