• Journal of Korean Neurosurgical Society
• /
• v.59 no.5
• /
• pp.466-470
• /
• 2016

Objective : To determine imaging features that may separate adamantinomatous and papillary variants of craniopharyngiomas given that tumors with adamantinomatous signature features are associated with higher recurrence rates, morbidity, and mortality. We specifically reviewed calcification on CT, T1 bright signal intensity, and cystic change on T2 weighted images for differentiating these two types. Methods : We retrospectively reviewed the MRI and CT studies in 38 consecutive patients with pathologically proven craniopharyngiomas between January 2004 and February 2014 for the presence of calcification on CT scans, bright signal intensity on T1 weighted images, and cystic change on T2 weighted images. Results : Of the 38 craniopharyngiomas, 30 were adamantinomatous type and 8 were papillary type. On CT scans, calcification was present in 25 of 38 tumors. All calcified tumors were adamantinomatous type. Twenty four of 38 tumors had bright signal intensity on T1 weighted images. Of these 24 tumors, 22 (91.7%) were adamantinomatous and 2 were papillary type. Cystic change on T2 weighted images was noted in 37 of 38 tumors; only 1 tumor with papillary type did not show cystic change. Conclusion : T1 bright signal intensity and calcification on CT scans uniformly favor the adamantinomatous type over papillary type of craniopharyngioma in children. However, these findings are more variable in adults where calcification and T1 bright signal intensity occur in 70.6% and 58.8% respectively of adult adamantinomatous types of craniopharyngiomas.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2006.05a
• /
• pp.163-166
• /
• 2006

The field of medical images has been digitalized as the development of computer and the digitalization of the medical instruments. As a result it causes a lot of problems such as an illegal copy related to medical images and property right of the medical images. Therefore, digital watermarking is used for discrimination whether the data are modified or not. It is also used to protect both the property right of medical images and the private life of many patients. The proposed theories, the Non-blind and the Blind method, have two problems. One is needed an original image and the other is using a gaussian watermarking. This paper proposes the new Blind Watermarking using binary images in order to easily recognize the results of watermark. This algorithm is described that an watermark of a binary image is wavelet-transformed, and then a transformed watermark is inserted in medium-band of frequency domains of original image by the Circular Input method. The propose method presented the good performance of over 0.97 in NC.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2004.05b
• /
• pp.241-246
• /
• 2004

Medical images are being managed more by PACS in general medical institutions. It is important to protect patients from being invaded their privacy related to the images. It is also necessary to confirm the ownership, the right of properity of the medical images and notice whether the data are modified. In this paper, we propose a robust watermarking against RST attacks in medical images on the PACS. The proposed scheme modifies and improves Log-Polar Mapping and Fourier Mellin Transform in order to realize and recover serious image degradation and watermark data loss caused by the conversion between cartesian coordinate and log-polar coordinate. We used the radius and theta Look Up Table to solve the realization of the Fourier Mellin Transform, and inserted a watermark into 2D-DFT magnitudes using Spread Spectrum. Experimental results shows that this method are robust to several attack.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.18 no.6
• /
• pp.267-275
• /
• 2023

This paper presents the development of specialized software for annotating volume-of-interest on ¹⁸F-FDG PET/CT images with the goal of facilitating the studies and diagnosis of head and neck cancer (HNC). To achieve an efficient annotation process, we employed the SE-Norm-Residual Layer-based U-Net model. This model exhibited outstanding proficiency to segment cancerous regions within ¹⁸F-FDG PET/CT scans of HNC cases. Manual annotation function was also integrated, allowing researchers and clinicians to validate and refine annotations based on dataset characteristics. Workspace has a display with fusion of both PET and CT images, providing enhance user convenience through simultaneous visualization. The performance of deeplearning model was validated using a Hecktor 2021 dataset, and subsequently developed semi-automatic annotation functionalities. We began by performing image preprocessing including resampling, normalization, and co-registration, followed by an evaluation of the deep learning model performance. This model was integrated into the software, serving as an initial automatic segmentation step. Users can manually refine pre-segmented regions to correct false positives and false negatives. Annotation images are subsequently saved along with their corresponding ¹⁸F-FDG PET/CT fusion images, enabling their application across various domains. In this study, we developed a semi-automatic annotation software designed for efficiently generating annotated lesion images, with applications in HNC research and diagnosis. The findings indicated that this software surpasses conventional tools, particularly in the context of HNC-specific annotation with ¹⁸F-FDG PET/CT data. Consequently, developed software offers a robust solution for producing annotated datasets, driving advances in the studies and diagnosis of HNC.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.34 no.8C
• /
• pp.772-781
• /
• 2009

This paper proposes a novel algorithm for the efficient classification and retrieval of medical images. After color and edge features are extracted from medical images, these two feature vectors are then applied to a multi-class Support Vector Machine, to give membership vectors. Thereafter, the two membership vectors are combined into an ensemble feature vector. Also, to reduce the search time, Correlated Categories Vector is proposed for similarity matching. The experimental results show that the proposed system improves the retrieval performance when compared to other methods.

• Journal of International Society for Simulation Surgery
• /
• v.1 no.2
• /
• pp.62-66
• /
• 2014

Image registration is the process for finding the correct geometrical transformation that brings one image in precise spatial correspondence with another image. There are limitations on the visualization of simple overlay between two different modality images because two different modality images have different anatomical information, resolution, and viewpoint. In this paper, various image registration methods and their applications are introduced. With the recent advance of medical imaging device, image registration is used actively in diagnosis support, treatment planning, surgery guidance and monitoring the disease progression.

• Proceedings of the KIEE Conference
• /
• 1996.07b
• /
• pp.1362-1364
• /
• 1996

In this paper a Dear lossless compression of medical images with vector quantizer is proposed. In order to apply the vector quantizer to medical images, the peak error in the reconstructed image is reduced down to 1. Simulation results show that the proposed coding scheme provides better performance with a PSNR improvement compared to the conventional JPEG standard.

• Imaging Science in Dentistry
• /
• v.43 no.3
• /
• pp.185-190
• /
• 2013

Purpose: The aim of the present study was to evaluate the measurement accuracy of endodontic file length on periapical digital radiography after application of noise reduction digital enhancement. Materials and Methods: Thirty-five human single-rooted permanent teeth with canals measuring 20-24 mm in length were selected. ISO #08 endodontic files were placed in the root canals of the teeth. The file lengths were measured with a digital caliper as the standard value. Standard periapical digital images were obtained using the Digora digital radiographic system and a dental X-ray unit. In order to produce the enhanced images, the noise reduction option was applied. Two blinded radiologists measured the file lengths on the original and enhanced images. The measurements were compared by repeated measures ANOVA and the Bonferroni test (${\alpha}=0.05$). Results: Both the original and enhanced digital images provided significantly longer measurements compared with the standard value (P<0.05). There were no significant differences between the measurement accuracy of the original and enhanced images (P>0.05). Conclusion: Noise reduction digital enhancement did not influence the measurement accuracy of the length of the thin endodontic files on the digital periapical radiographs despite the fact that noise reduction could result in the elimination of fine details of the images.

• Journal of the Korean Society of Radiology
• /
• v.2 no.2
• /
• pp.11-16
• /
• 2008

The most difficult of implementation PACS is large amount of data. Therefore, PACS needs mass storage, as well as rapid transmission time. Consequently, medical images needs compression when stored in PACS. WT(wavelet transform) was announced by Ingrid Daubechies and Stephane Mallat, WT was methods of signal analysis by a base functions set same as Fourie transform. This paper estimated an efficiency, that experimental medical images compressed by DWT. The result of estimated, we are knows effectiveness that display to remained signal in low frequency region after 4-level DWT form $512{\times}512{\times}2^8$ input images. Compression ratio of images by 4-level DWT was 1:16. It is a high compression ratio, the other side has a problem appears on staircase phenomenon.

• International Journal of Computer Science & Network Security
• /
• v.24 no.3
• /
• pp.59-70
• /
• 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