For accurate color restoration of underwater images, it is necessary to acquire the spectral scattering properties of seawater. In this study, we propose a method for acquiring spectral scattering properties from an RGB image. In the proposed method, we first obtain scattering properties (scattering and extinction coefficients) in RGB space. Then, we estimate spectral scattering properties from these RGB coefficients. We conduct simulation experiments using seawater datasets with various spectral scattering properties, and evaluate the accuracy of the proposed method.
KEYWORDS: Color, Color reproduction, Chemical vapor deposition, Color vision, Roentgenium, RGB color model, Metamerism, Cones, Matrices, Covariance matrices
The human eye has three types of cone cells called L-, M-, and S-cones. These cones have sensitivity peaks at long, medium, and short wavelengths, respectively. Color vision deficiency (CVD) is the inability to distinguish between certain shades of color due to the limited function of the cones. It is important to understand how CVD patients perceive color. Since color reproduction in most of the conventional methods is based on a conversion from RGB images, it is necessary to reproduce colors from spectra in order to represent colors more accurately.
In this paper, we propose a method for reproducing the color vision of CVD patients based on the stage theory, which combines trichromatic theory and opponent-color theory. The proposed method focuses on the diversity of CVD patients and can reproduce the color vision according to the severity of CVD. Furthermore, by taking into account the spectrum, the proposed method can investigate the problem of metamerism, which cannot be realized by conventional methods using RGB images.
Recently, the use of augmented reality in clinical environments has been increasing. Volume rendering is very useful in medical applications for visualizing volume data such as CT and MRI data. In this paper, we propose a real-time volume rendering that runs on AR glasses with limited computational power. In the proposed method, we introduce a new volume data structure based on the concept of level of detail (LOD).
In imaging diagnosis, radiologists refer to the CT images of the similar cases. However, it is a big burden for them to search such CT images from the huge numbers of CT images. To solve this problem, many retrieval methods of CT images have been developed. Most existing retrieval methods target cases in which lesions exist within a limited region of the lung. These methods retrieve similar cases by calculating the similarity to the region specified on a slice image of the query case, for example, solitary pulmonary nodules. However, radiologists diagnose not only such cases but also diffuse lung disease (DLD), where lesions exist throughout the lung. Radiologists diagnose DLDs by grasping the threedimensional (3D) distribution of lesion textures. However, the existing methods cannot retrieve similar DLDs. We propose a novel method that can retrieve morphologically similar cases based on the radiologist’s knowledge, how they diagnose DLDs. In the proposed method, we configure a 3D model for the central-peripheral region of a lung, represent the similarity for the 3D distribution of lesions as histograms, and then retrieve the cases of the similar histograms. We evaluate the average precision of the proposed method for DLD CT images. For the top 5 cases, the mean of the average precisions of the proposed method is 0.84 and is better than that of the method that only calculates the volume rate of the lesions in the lung (0.64). The proposed method retrieves similar DLDs based on 3D distribution of lesion textures and is expected to contribute to diagnosis support in clinical practice.
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