• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.101-106
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• 2021

Breast cancer is the number one cause of deaths from cancer in women, knowing the type of breast cancer in the early stages can help us to prevent the dangers of the next stage. The performance of the deep learning depends on large number of labeled data, this paper presented convolutional neural network for classification breast cancer from images to benign or malignant. our network contains 11 layers and ends with softmax for the output, the experiments result using public BreakHis dataset, and the proposed methods outperformed the state-of-the-art methods.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.273-275
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• 2022

Deep Learning is used nowadays in Nuclei segmentation. While recent developments in theory and open-source software have made these tools easier to implement, expert knowledge is still required to choose the exemplary model architecture and training setup. We compare two popular segmentation frameworks, U-Net and Mask-RCNN, in the nuclei segmentation task and find that they have different strengths and failures. we compared both models aiming for the best nuclei segmentation performance. Experimental Results of Nuclei Medical Images Segmentation using U-NET algorithm Outperform Mask R-CNN Algorithm.

• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.252-259
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• 2022

Artificial intelligence is used in fusion with image processing techniques using cameras. Image processing technology is a technology that processes objects in an image received from a camera in real time, and is used in various fields such as security monitoring and medical image analysis. If such image processing reduces the accuracy of recognition, providing incorrect information to medical image analysis, security monitoring, etc. may cause serious problems. Therefore, this paper uses a mixture of YOLOv4-tiny model and image processing algorithm and uses the COCO dataset for learning. The image processing algorithm performs five image processing methods such as normalization, Gaussian distribution, Otsu algorithm, equalization, and gradient operation. For RGB images, three image processing methods are performed: equalization, Gaussian blur, and gamma correction proceed. Among the nine algorithms applied in this paper, the Equalization and Gaussian Blur model showed the highest object detection accuracy of 96%, and the gamma correction (RGB environment) model showed the highest object detection rate of 89% outdoors (daytime). The image binarization model showed the highest object detection rate at 89% outdoors (night).

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.53-64
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• 2023

Early detection of endometrial carcinoma in uterus is essential for effective treatment. Endometrial carcinoma is the worst kind of endometrium cancer among the others since it is considerably more likely to affect the additional parts of the body if not detected and treated early. Non-invasive medical computer vision, also known as medical image processing, is becoming increasingly essential in the clinical diagnosis of various diseases. Such techniques provide a tool for automatic image processing, allowing for an accurate and timely assessment of the lesion. One of the most difficult aspects of developing an effective automatic categorization system is the absence of huge datasets. Using image processing and deep learning, this article presented an artificial endometrium cancer diagnosis system. The processes in this study include gathering a dermoscopy images from the database, preprocessing, segmentation using hybrid Fuzzy C-Means (FCM) and optimizing the weights using the Whale Optimization Algorithm (WOA). The characteristics of the damaged endometrium cells are retrieved using the feature extraction approach after the Magnetic Resonance pictures have been segmented. The collected characteristics are classified using a deep learning-based methodology called Long Short-Term Memory (LSTM) and Bi-directional LSTM classifiers. After using the publicly accessible data set, suggested classifiers obtain an accuracy of 97% and segmentation accuracy of 93%.

• Korean Journal of Digital Imaging in Medicine
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• v.7 no.1
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• pp.33-38
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• 2005

Modern hospitals have been greatly facilitated with information technology (IT) such as hospital information system (HIS). One of the most prominent achievements is medical imaging and image data management so-called Picture Archiving and Communication Systems (PACS). Due to inevitable use of diagnostic images (such as X-ray, CT, MRI), PACS made tremendous impact not only on radiology department but also nearly all clinical departments for exchange and sharing image related clinical information. There is no doubt that better use of PACS leads to highly efficient clinical administration and hospital management. However, due to rapid and widespread acceptance of PACS storage and management of digitized image data in hospital introduces overhead and bottleneck when transferring images among clinical departments within and/or across hospitals. Despite numerous technical difficulties, financing for installing PACS is a major hindrance to overcome. In addition, a mirroring or a clustering backup can be used to maximize security and efficiency, which may not be considered as cost-effective approach because of extra hardware expenses. In this study therefore we have developed a new based on grid of distributed PACS in order to balance between the cost and network performance among multiple hospitals.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.11
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• pp.1980-1986
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• 2008

A digital watermarking technique used as a protection and certifying mechanism of copyrighted creations including music, still images, and videos in terms of lading any loss in data, reproduction and pursuit. This study suggests using a selected geometric invariant point through the whole processing procedure based on the invariant point so that it will be robust in a geometric transformation attack. The introduced algorithm here is based on a watershed splitting method in order to make medical images strong against RST transformation and other processing. This algorithm also proved that is has robustness against not only RST attack, but also JPEG compression attack and filtering attack.

• Journal of the Korean Society of Radiology
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• v.83 no.4
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• pp.759-770
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• 2022

The importance of ethics in research and the use of artificial intelligence (AI) is increasingly recognized not only in the field of healthcare but throughout society. This article intends to provide domestic readers with practical points regarding the ethical issues of using radiological images for AI research, focusing on data security and privacy protection and the right to data. Therefore, this article refers to related domestic laws and government policies. Data security and privacy protection is a key ethical principle for AI, in which proper de-identification of data is crucial. Sharing healthcare data to develop AI in a way that minimizes business interests is another ethical point to be highlighted. The need for data sharing makes the data security and privacy protection even more important as data sharing increases the risk of data breach.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.3
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• pp.371-377
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• 2020

Currently used iris cameras are expensive and have many limitations in their use. Existing iris cameras are inconvenient in interworking with newly developed software, and light reflections generated during iris photography are inadequate for medical use. Therefore, it is impossible to utilize the existing camera to take an image by yourself. In this paper, the iris camera is newly constructed so that the iris can be photographed by ourselves and the area of interest can be seen well. Anyone can easily wear glasses-type iris cameras to acquire images using IoT devices, and the acquired images are linked to the iris analysis program and used to read genetic weak parts. The proposed iris camera module automatically provides light reflection, shake, and accurate focus when capturing images, increasing the accuracy of image analysis to 91.49%. In addition, we have proved through experiments that one image processing time is fast as 0.007ms due to accurate image input.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.7
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• pp.487-494
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• 2013

Medical image watermarking has received extensive attention as wide security services in the healthcare information system. This paper proposes a blind medical image watermarking approach on the segmented gray-matter (GM) images by utilizing discrete wavelet transform (DWT) and discrete cosine transform (DCT) along with enhanced suppressed fuzzy C-means (EnSFCM) for the optimal selection of sub-blocks position to insert a watermark. Experimental results show that the proposed approach outperforms other methods in terms of peak signal to noise ratio (PSNR) and M-SVD. In addition, the proposed approach shows better robustness than other methods in normalized correlation (NC) values against several attacks, such as noise addition, filtering, JPEG compression, blurring, histogram equalization, and cropping.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.12
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• pp.2586-2594
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• 2009

A digital watermarking technique used as a protection and certifying mechanism of copyrighted creations including music, still images, and videos in terms of finding any loss in data, reproduction and pursuit. This study suggests using a selected geometric invariant point through the whole processing procedure of an image and inserting and extracting based on the invariant point so that it will be robust in a geometric transformation attack. The introduced algorithm here is based on a watershed splitting method in order to make medical images strong against RST(Rotation Scale, Translation) transformation and other processing. It also helps to maintain the watermark in images that are compressed and stored for a period of time. This algorithm also proved that is has robustness against not only JPEG compression attack, but also RST attack and filtering attack.