• Journal of Biomedical Engineering Research
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• v.28 no.3
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• pp.332-337
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• 2007

In the early twenty-first century, minimally invasive surgery is the mainstay of various kinds of surgical fields. Surgeons gave percutaneously surgical treatment of the screw directly using a fluoroscopic view in the past. The latest date, they began to operate the fractured carpal bone surgery using Computerized Tomography (CT). Carpal bones composed of wrist joint consist of eight small bones which have hexahedron and sponge shape. Because of these shape, it is difficult to grasp the shape of carpal bones using only CT image data. Although several image segmentation studies have been conducted with carpal bone CT image data, more studies about carpal bone using CT data are still required. Especially, to apply the software implemented from the studies to clinical fIeld, the outcomes should be user friendly and very accurate. To satisfy those conditions, we propose modified multi-seed region growing segmentation method which uses simple threshold and the canny edge detector for finding edge information more accurately. This method is able to use very easily and gives us high accuracy and high speed for extracting the edge information of carpal bones. Especially, using multi-seed points, multi-bone objects of the carpal bone are extracted simultaneously.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.6
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• pp.114-121
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• 2013

BTC image compression is a simple and easy hardware implementation, is widely used in a video compression techniques required for LCD overdrive. In this paper, methods for reducing compression loss, a multi-level quantization BTC (MLQ-BTC) algorithm is proposed. The process of the MLQ-BTC algorithm is, a input image is compressed and decompressed by Quasi 8-level method and Advanced 2-level BTC method, and select the algorithm with the smallest compression loss. Simulation results show that the proposed algorithm is efficient as compared with PSNR and compression ratio of the existing BTC methods.

• Journal of Information Display
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• v.3 no.3
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• pp.1-11
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• 2002

Multi-view function is important to three-dimensional displays without dedicated glasses. It is the reason that the observers earnestly desire to change their positions freely. Multi-viewing is also principal to the reality of three-dimensional (3D) image displayed on the screen. The display of projection type has the advantage that the number of viewing points can be easily increased according to the number of projectors. The authors research on multi-view projection display with hologram screen. Powerful directionality of the diffracted beam from hologram screen is required unlike two-dimensional (2D) display. We developed a new method that all diffracted beams satisfied the same Bragg condition and became sufficiently bright to observe the 3D image under usual indoor light. The principle is based on the essential Bragg diffraction in the three-dimensional space. Owing to such three-dimensional Bragg diffraction we achieved an excellent hologram screen that could be multiple reconstructed in spite of single recording. This hologram screen is able to answer arbitrary numbers of viewing points within wide viewing zone. The distortion of 3D image becomes also sufficiently small with the method of dividing the cross angle between illumination and diffraction beam.

• Journal of Multimedia Information System
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• v.8 no.2
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• pp.101-110
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• 2021

In computer vision, single-image super resolution has been an area of research for a significant period. Traditional techniques involve interpolation-based methods such as Nearest-neighbor, Bilinear, and Bicubic for image restoration. Although implementations of convolutional neural networks have provided outstanding results in recent years, efficiency and single model multi-scalability have been its challenges. Furthermore, previous works haven't placed enough emphasis on real-number scalability. Interpolation-based techniques, however, have no limit in terms of scalability as they are able to upscale images to any desired size. In this paper, we propose a convolutional neural network possessing the advantages of the interpolation-based techniques, which is also efficient, deeming it suitable in practical implementations. It consists of convolutional layers applied on the low-resolution space, post-up-sampling along the end hidden layers, and additional layers on high-resolution space. Up-sampling is applied on a multiple channeled feature map via bicubic interpolation using a single model. Experiments on architectural structure, layer reduction, and real-number scale training are executed with results proving efficient amongst multi-scale learning (including scale multi-path-learning) based models.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3844-3853
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• 2023

Although radiation and chemotherapy methods for cancer therapy have advanced significantly, surgical resection is still recommended for most cancers. Therefore, intraoperative imaging studies have emerged as a surgical tool for identifying tumor margins. Intraoperative imaging has been examined using conventional imaging devices, such as optical near-infrared probes, gamma probes, and ultrasound devices. However, each modality has its limitations, such as depth penetration and spatial resolution. To overcome these limitations, hybrid imaging modalities and tracer studies are being developed. In a previous study, a multi-modal laparoscope with silicon photo-multiplier (SiPM)-based gamma detection acquired a 1 s interval gamma image. However, improvements in the near-infrared fluorophore (NIRF) signal intensity and gamma image central defects are needed to further evaluate the usefulness of multi-modal systems. In this study, an attempt was made to change the NIRF image acquisition method and the SiPM-based gamma detector to improve the source detection ability and reduce the image acquisition time. The performance of the multi-modal system using a complementary metal oxide semiconductor and modified SiPM gamma detector was evaluated in a phantom test. In future studies, a multi-modal system will be further optimized for pilot preclinical studies.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.113-120
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• 2020

The multi-view video is a collection of multiple videos capturing the same scene at different viewpoints. Thus, there is an advantage of providing for user oriented view pointed video. This paper is suggested that the compression performance of layered depth image structure expression has improved by using more improved method. We confirm the data size of layer depth image by encoding H.264 technology and the each performances of reconstructed images. The H.264/AVC technology has easily extended for H.264 technology of video contents. In this paper, we suggested that layered depth structure can be applied for an efficient new image contents. We show that the huge data size of multi-view video image is decreased, and the higher performance of image is provided, and there is an advantage of for stressing error restoring.

• Journal of Broadcast Engineering
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• v.23 no.4
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• pp.474-483
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• 2018

In this paper, we propose a multi-scale high dynamic range (HDR) tone mapping algorithm using guided image filter (GIF). The GIF is used to divide an image into a base layer and a detail layer, then the range of the detail layer is reduced with a compression function to enhance the detail information of the image. However, in most cases, an image includes the detail and edge information in different scales. That is to say, it is difficult to represent all detail features under a certain scale, and a single-scale image decomposition method is not free from artifacts around edges. To solve the problems, the multi-scale image decomposition method is proposed. It utilizes the detail layers of several scale to determine how much edge is preserved. Experiment results show that the proposed algorithm has better image performance in preserving edge compared to conventional algorithm.

• Journal of Broadcast Engineering
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• v.20 no.4
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• pp.521-532
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• 2015

This paper presents a piecewise image denoising with multi-scale block region detector based on quadtree structure for effective image restoration. Proposed piecewise image denoising method suggests multi-scale block region detector (MBRD) by dividing whole pixels of a noisy image into three parts, with regional characteristics: strong variation region, weak variation region, and flat region. These regions are classified according to total pixels variation between multi-scale blocks and are applied principal component analysis with local pixel grouping, bilateral filtering, and structure-preserving image decomposition operator called relative total variation. The performance of proposed method is evaluated by Experimental results. we can observe that region detection results generated by the detector seems to be well classified along the characteristics of regions. In addition, the piecewise image denoising provides the positive gain with regard to PSNR performance. In the visual evaluation, details and edges are preserved efficiently over the each region; therefore, the proposed method effectively reduces the noise and it proves that it improves the performance of denoising by the restoration process according to the region characteristics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2698-2717
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• 2019

This paper proposes a multi-stage encryption technique to enhance the level of secrecy of image to facilitate its secured transmission through the public network. A great number of researches have been done on image secrecy. The existing image encryption techniques like visual cryptography (VC), steganography, watermarking etc. while are applied individually, usually they cannot provide unbreakable secrecy. In this paper, through combining several separate techniques, a hybrid multi-stage encryption technique is proposed which provides nearly unbreakable image secrecy, while the encryption/decryption time remains almost the same of the exiting techniques. The technique consecutively exploits VC, steganography and one time pad (OTP). At first it encrypts the input image using VC, i.e., splits the pixels of the input image into multiple shares to make it unpredictable. Then after the pixel to binary conversion within each share, the exploitation of steganography detects the least significant bits (LSBs) from each chunk within each share. At last, OTP encryption technique is applied on LSBs along with randomly generated OTP secret key to generate the ultimate cipher image. Besides, prior to sending the OTP key to the receiver, first it is converted from binary to integer and then an asymmetric cryptosystem is applied to encrypt it and thereby the key is delivered securely. Finally, the outcome, the time requirement of encryption and decryption, the security and statistical analyses of the proposed technique are evaluated and compared with existing techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1706-1725
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• 2024

The unique U-shaped structure of U-Net network makes it achieve good performance in image segmentation. This network is a lightweight network with a small number of parameters for small image segmentation datasets. However, when the medical image to be segmented contains a lot of detailed information, the segmentation results cannot fully meet the actual requirements. In order to achieve higher accuracy of medical image segmentation, a novel improved U-Net network architecture called multi-scale encoder-decoder U-Net+ (MEDU-Net+) is proposed in this paper. We design the GoogLeNet for achieving more information at the encoder of the proposed MEDU-Net+, and present the multi-scale feature extraction for fusing semantic information of different scales in the encoder and decoder. Meanwhile, we also introduce the layer-by-layer skip connection to connect the information of each layer, so that there is no need to encode the last layer and return the information. The proposed MEDU-Net+ divides the unknown depth network into each part of deconvolution layer to replace the direct connection of the encoder and decoder in U-Net. In addition, a new combined loss function is proposed to extract more edge information by combining the advantages of the generalized dice and the focal loss functions. Finally, we validate our proposed MEDU-Net+ MEDU-Net+ and other classic medical image segmentation networks on three medical image datasets. The experimental results show that our proposed MEDU-Net+ has prominent superior performance compared with other medical image segmentation networks.