• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.1
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• pp.36-44
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• 2022

Reversible data hiding techniques have been developed to hide confidential data in the image by shifting the histogram of the image. These techniques have a weakness in which the security of hidden confidential data is weak. In this paper, to solve this drawback, we propose a technique of triple encrypting confidential data using pixel value information and hiding it in the cover image. When confidential data is triple encrypted using the proposed technique and hidden in the cover image to generate a stego-image, since encryption based on pixel information is performed three times, the security of confidential data hidden by triple encryption is greatly improved. In the experiment to measure the performance of the proposed technique, even if the triple-encrypted confidential data was extracted from the stego-image, the original confidential data could not be extracted without the encryption keys. And since the image quality of the stego-image is 48.39dB or higher, it was not possible to recognize whether confidential data was hidden in the stego-image, and more than 30,487 bits of confidential data were hidden in the stego-image. The proposed technique can extract the original confidential data from the triple-encrypted confidential data hidden in the stego-image without loss, and can restore the original cover image from the stego-image without distortion. Therefore, the proposed technique can be effectively used in applications such as military, medical, digital library, where security is important and it is necessary to completely restore the original cover image.

• International journal of advanced smart convergence
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• v.8 no.1
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• pp.24-34
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• 2019

In this research, a practical deep learning framework to differentiate the lesions and nodules in breast acquired with ultrasound imaging has been proposed. 7408 ultrasound breast images of 5151 patient cases were collected. All cases were biopsy proven and lesions were semi-automatically segmented. To compensate for the shift caused in the segmentation, the boundaries of each lesion were drawn using Fully Convolutional Networks(FCN) segmentation method based on the radiologist's specified point. The data set consists of 4254 benign and 3154 malignant lesions. In 7408 ultrasound breast images, the number of training images is 6579, and the number of test images is 829. The margin between the boundary of each lesion and the boundary of the image itself varied for training image augmentation. The training images were augmented by varying the margin between the boundary of each lesion and the boundary of the image itself. The images were processed through histogram equalization, image cropping, and margin augmentation. The networks trained on the data with augmentation and the data without augmentation all had AUC over 0.95. The network exhibited about 90% accuracy, 0.86 sensitivity and 0.95 specificity. Although the proposed framework still requires to point to the location of the target ROI with the help of radiologists, the result of the suggested framework showed promising results. It supports human radiologist to give successful performance and helps to create a fluent diagnostic workflow that meets the fundamental purpose of CADx.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.1
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• pp.83-91
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• 2019

In this paper, we have effectively used the surface characteristics and local similarity existing in image, solved the problem that there is no CZP(Closest Zero point) that occurs in a very few images to hide secrete data into cover image by using histogram shift. By applying the proposed technique, it is possible to hide secrete data invisibly into the cover image, extract secrete data from the stego-image with no data loss, and completely restore the original cover image. It is impossible to know whether the secrete data is hidden in the stego-image because the stego-image constructed by applying the proposed technique has a good visual quality that can not distinguish the difference from the cover image. The proposed method is able to hide secrete data at various levels compared to conventional APD(Adjacent Pixel Difference) technique, and hide secrete data up to 25.1% more than APD in cover image.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.6 s.312
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• pp.65-84
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• 2006

In this paper, we present an improved image formation model and propose a color image enhancement based on the model. In the presented image formation model, an input image is represented as a product of global illumination, local illumination, and reflectance. In the proposed color image enhancement, an input RGB color image is converted into an HSV color image. Under the assumption of white-light illumination, the H and S component images are remained as they are and the V component image only is enhanced based on the image formation model. The global illumination is estimated by applying a linear LPF with wide support region to the input V component image and the local illumination by applying a JND (just noticeable difference)-based nonlinear LPF with narrow support region to the processed image, where the estimated global illumination is eliminated from the input V component image. The reflectance is estimated by dividing the input V component image by the estimated global and local illuminations. After performing the gamma correction on the three estimated components, the output V component image is obtained from their product. Histogram modeling is next executed such that the final output V component image is obtained. Finally an output RGB color image is obtained from the H and S component images of the input color image and the final output V component image. Experimental results for the test image DB built with color images downloaded from NASA homepage and MPEG-7 CCD color images show that the proposed method gives output color images of very well-increased global and local contrast without halo effect and color shift.

• Journal of Korea Multimedia Society
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• v.7 no.11
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• pp.1597-1609
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• 2004

A lot of lipreading system has been proposed to compensate the rate of speech recognition dropped in a noisy environment. Previous lipreading systems work on some specific conditions such as artificial lighting and predefined background color. In this paper, we propose a real-time lipreading system which allows the motion of a speaker and relaxes the restriction on the condition for color and lighting. The proposed system extracts face and lip region from input video sequence captured with a common PC camera and essential visual information in real-time. It recognizes utterance words by using the visual information in real-time. It uses the hue histogram model to extract face and lip region. It uses mean shift algorithm to track the face of a moving speaker. It uses PCA(Principal Component Analysis) to extract the visual information for learning and testing. Also, it uses HMM(Hidden Markov Model) as a recognition algorithm. The experimental results show that our system could get the recognition rate of 90% in case of speaker dependent lipreading and increase the rate of speech recognition up to 40～85% according to the noise level when it is combined with audio speech recognition.

• Radiation Oncology Journal
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• v.23 no.4
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• pp.236-242
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• 2005

Purpose: To evaluate the role of surgical clips and scars in determining electron boost field for early stage breast cancer undergoing conserving surgery and postoperative radiotherapy and to provide an optimal method in drawing the boost field. Materials and Methods: Twenty patients who had $4{\sim}7$ surgical clips in the excision cavity were selected for this study. The depth informations were obtained to determine electron energy by measuring the distance from the skin to chest wall (SCD) and to the clip implanted in the most posterior area of tumor bed. Three different electron fields were outlined on a simulation film. The radiological tumor bed was determined by connecting all the clips implanted during surgery Clinical field (CF) was drawn by adding 3 cm margin around surgical scar. Surgical field (SF) was drawn by adding 2 cm margin around surgical clips and an Ideal field (IF) was outlined by adding 2 cm margin around both scar and clips. These fields were digitized into our planning system to measure the area of each separate field. The areas of the three different electron boost fields were compared. Finally, surgical clips were contoured on axial CT images and dose volume histogram was plotted to investigate 3-dimensional coverage of the clips. Results : The average depth difference between SCD and the maximal clip location was $0.7{\pm}0.55cm$. Greater difference of 5 mm or more was seen in 12 patients. The average shift between the borders of scar and clips were 1.7 1.2, 1.2, and 0.9 cm in superior, inferior, medial, and lateral directions, respectively. The area of the CF was larger than SF and IF in 6y20 patients. In 15/20 patients, the area difference between SF and if was less than 5%. One to three clips were seen outside the CF in 15/20 patients. In addition, dosimetrically inadequate coverage of clips (less than 80% of prescribed dose) were observed in 17/20 patients when CF was used as the boost field. Conclusion: The electron field determined from clinical scar underestimates the tumor bed in superior-inferior direction significantly and thereby underdosing the tissue at risk. The electron field obtained from surgical clips alone dose not cover the entire scar properly As a consequence, our technique, which combines the surgical clips and clinical scars in determining electron boost field, was proved to be effective in minimizing the geographical miss as well as normal tissue complications.