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3D printing has recently been used in various fields. Among various applications, 3D face data must be generated for 3D face printing. A laser scanner is used to acquire 3D face data, but there is a restriction that a person should not move during scanning. In this paper, we propose a 3D face modeling method based on a single image and a face transformation system to use the generated 3D face for virtual cosmetic surgery. We have defined facial feature points from the 3D face database for 3D face data generation. After extracting feature points from a single face image, 3D face of the input face image is generated corresponding to the 3D face feature points defined from the 3D face database. After 3D face modeling, 3D face modification part is applied for use such as virtual cosmetic surgery.

Due to the popularity of artificial intelligent, medical image processing using artificial neural network is increasingly attracting the attention of academic and industry researches. Deep learning with a convolutional neural network has been proved to very effective representation of images. However, the training process requires high performance H/W platform. Thus, the realtime learning of a large number of high dimensional samples within low-power devices is a challenging problem. In this paper, we attempt to establish this possibility by presenting a realtime neural network method on Raspberry pi using online sequential extreme learning machine. Our experiments on high-dimensional dataset show that the proposed method records an almost real-time execution.

This paper suggests techniques to enhance coding time which is a problem in traditional fractal compression and to improve fidelity of reconstructed images by determining fractal coefficient through adaptive selection of block approximation formula. First, to reduce coding time, we construct a linear list of domain blocks of which characteristics is given by their luminance and variance and then we control block searching time according to the first permissible threshold value. Next, when employing three-level block partition, if a range block of minimum partition level cannot find a domain block which has a satisfying approximation error, There applied to 24-bpp color image compression and image techniques. The result did not occur a loss in the image quality of the image when using the encoding method, such as almost to the color in the YIQ image compression rate and image quality, such as RGB images and showed good.

Ultrasound devices diagnose many disease, which is widely used, can not be standardized quantitative evaluated in order to evaluate sonography image of quality. Therefore, in this papers, aims to get correct image in order to accurate diagnosis by figuring out the appropriate parameter based on each target by measuring distortion which results in the analyzation of the sensitivity of SNR and the histogram of signal by manipulating parameter of 8 mm target in ATS-539 multipurpose phantom. Equipment using Acuson sequoia 512, convex probe and utilizes multi-objective phantom. experiment method is that first you put the phantom on the flat and acquire 85 sheets of image, changing frequency(2,3,4 MHz, harmonic 3, 4, 4.5 MHz), Focus(2, 4, 6 unit), and Dynamic Range(58, 68, 78, 88, 98) for a 8 mm structure. through the Image J program. The sensitivity angle of 8mm target through Image J program is gauged by each separate target SNR and the distorted angle subtract and measure Histogram of background from Histogram of signal and take top 40% from the given result value above. According to parameter variation we found out proper parameter by acquiring SNR of sensitivity and distortion data for aspect of transition. The more this findings have Focus, the lower distortion value and at 4 MHz frequency this result have high SNR and low distortion value. Dynamic Range got an appropriate image on 88 and 98. It is considered on the basis of the experimental data, the probability of disease diagnosis will get higher.

In this paper, a method was proposed for objective and scientific judgment on disease conditions that is currently relied on subjective judgment of ultrasound practitioners. In the proposed method, mean squared errors (MSE) in ultrasound images for follow-up of HCC patients were obtained and the Peak Signal to Noise Ratio(PSNR) of the ultrasound images was analyzed. According to the results of analysis, MSE and PSNR values changed over time. This is attributable to changes in ultrasound images resulting from increases in utrasonic echoes following the progression of HCC. The results of the present study can be used as a method for scientific and objective judgment in ultrasonic scan instead of current subjective judgment by practitioners. Key Words : HCC, Ultrasound, MSE, PSNR 요 약 본 연구에서는 술자의 주관적인 판단에 의존하고 있는 질환 여부의 판정을 객관적이고 과학적인 방법으로 판정하기 위한 방안을 제안하였다. 제안된 방법에서는 간세포암 환자를 추적관찰 한 초음파영상을 MSE(Mean Squared Error : 평균제곱오차)를 구한 후 PSNR(Peak Signal to Noise Ratio)분석을 하였다. 분석한 결과 시간이 지남에 따라 MSE값과 PSNR값이 변화함을 알 수 있었다. 이러한 이유는 간세포암이 진행됨에 따라 초음파 에코의 증가로 초음파영상의 변화에 기인하는 것이다. 본 논문의 결과는 술자의 주관적인 판단에 의하여 행해지고 있는 초음파 검사를 과학적이고 객관적인 판정을 할 수 있는 방법으로 활용될 수 있을 것이다.

In this paper, we present an algorithm to extract features about disease region in digital stomach images. For feature extraction, DCT coefficients of gastrointestinal imaging matrix was obtained. DCT coefficent matrix is concentrated energy in low frequency region, we were extracted 128 feature parameters in low frequency region. Extracted feature parameters can using for differential compression of PACS and, can using for input parameter in CAD.