Sectional anatomy of human brain is useful to examine the diseased brain as well as normal brain. However, intracerebral reference points for the axial, sagittal, and coronal planes of brain have not been standardized in anatomical sections or radiological images. We made 2,343 serially-sectioned images of a cadaver head with 0.1 mm intervals, 0.1 mm pixel size, and 48 bit color and obtained axial, sagittal, and coronal images based on the proposed reference system. This reference system consists of one principal reference point and two ancillary reference points. The two ancillary reference points are the anterior commissure and the posterior commissure. And the principal reference point is the midpoint of two ancillary reference points. It resides in the center of whole brain. From the principal reference point, Cartesian coordinate of x, y, z could be made to be the standard axial, sagittal, and coronal planes.

The sectioned images (SIs) of the pelvis from a female cadaver enable the creation of realistic three-dimensional (3D) images and various educational tools of female uro

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Because of the rapidly increasing use of digital composite images, recent studies have identified digital forgery and filtering regions. This research has shown that interpolation, which is used to edit digital images, is an effective way to analyze digital images for composite regions. Interpolation is widely used to adjust the size of the image of a composite target, making the composite image seem natural by rotating or deforming. As a result, many algorithms have been developed to identify composite regions by detecting a trace of interpolation. However, many limitations have been found in detection maps developed to identify composite regions. In this study, we analyze the pixel patterns of noninterpolation and interpolation regions. We propose a detection map algorithm to separate the two regions. To identify composite regions, we have developed an improved algorithm using minimum filer, Laplacian operation and maximum filters. Finally, filtering regions that used the interpolation operation are analyzed using the proposed algorithm.

Raw file records luminance value responding to each pixel of a digital camera sensor. In digital imaging, it has a characteristic that assigns the half of the whole levels for the first highlight stop and the half of the other for the second highlight stop, and etc., so that the darkest shadow stop is assigned the smallest level number of them. Therefore, when we exposure a digital camera, we should obtain the first highlight stop because it has the largest number of levels. In other words, controlling exposure to capture the first highlight stop is important in this kind of linear-distribution of raw file characteristic. Throughout the experiment, we verify the optimized exposure value and ISOs to maintain the first highlight stop which has the largest number of levels. In order to make it, we overexposure a scene with a raw file and convert it to underexposure in a raw file converting software. That is Exposure To The Right (ETTR) which can improve the image quality reproduction. Our research verifies the efficiency of ETTR with controlling the exposure range and ISOs. The result shows that the optimized exposure value is around + 1⅔ stop over compared to the normal exposure and simultaneously with the high ISOs. Throughout this practical research, we can provide the effective ETTR information to consumers and manufacturers. This method will contribute the optimum image performance to maximize dynamic range and to minimize noise in a digital imaging.