Showing papers by "Nagaaki Ohyama published in 1999"
TL;DR: In this article, the authors presented a method to increase the color gamut of a liquid crystal display (LCD) using more than three prima- ries, i.e., multiprimaries generated by a diffraction grating and a liquid-crystal panel.
Abstract: To reproduce the natural color of an object through color im- aging systems, the range of the reproducible color, i.e., the color gamut, of the color display devices must be expanded, because the color gamut of current display devices such as cathode ray tubes (CRTs) and liquid crystal displays (LCDs) is insufficient. We present a new method to en- large the color gamut for the LCD device using more than three prima- ries, i.e., multiprimaries, generated by a diffraction grating and a liquid crystal panel. The optical system for the multiprimary color display is introduced, and its capability of increasing the gamut is discussed. The experimental result displayed by seven primary colors is also demon- strated. © 1999 Society of Photo-Optical Instrumentation Engineers. (S0091-3286(99)02011-5)
50 citations
24 Oct 1999
TL;DR: In this system, the color of a patient under observing illumination is calculated from multispectral image and the spectral reflectance is calculated using principal components of human skin in the process for accurate color reproduction.
Abstract: For a reliable diagnosis via visual telecommunication systems, it is very important to reproduce patient images with correct color. But it is not certain by conventional color imaging systems especially when the illumination of observing site is different from that of the image capturing site. We present a natural color reproduction system for telemedicine. In this system, the color of a patient under observing illumination is calculated from multispectral image. For accurate color reproduction, the spectral reflectance is calculated using principal components of human skin in the process. The color image is displayed on a calibrated display device. In the experiment, it is confirmed that the system realizes good accuracy in the color reproduction of human skin from 10 band multispectral image. And for simplification of the system we also adapted this process to commercial digital RGB camera.
7 citations
TL;DR: The results show that good accuracy in estimated scatter components, good uniformity of scatter compensation at the center and the side of an object, and good noise property can be acquired by this method.
Abstract: In this work we propose a method for scatter compensation in single photon emission computed tomography imaging, by which we can estimate the scatter components in projections in high speed with good accuracy. The method is that we first estimate the scatter components in projections based on scatter response kernels by one time of ordered subsets expectation maximization iterative reconstruction, and then subtract the estimated scatter components from the projections and complete reconstruction by filtered back-projection method. The principle is that the image corresponding to the scatter components in projections consists largely of low-frequency components of an activity distribution; these low-frequency components will converge faster than the high ones in iterative reconstruction. Therefore, we can estimate the low-frequency component image before the image converges with the high-frequency ones, and obtain the scatter components by re-projecting the low-frequency component image with scatter response kernels. The effects of the proposed method were compared with the dual- and triple-energy window methods using experimental measurements. The results show that good accuracy in estimated scatter components, good uniformity of scatter compensation at the center and the side of an object, and good noise property can be acquired by this method.
6 citations
TL;DR: The proposed method treats data in a multidimensional space without any pre-processing, and the data is classified into groups according to the criterion, to maximize likelihood calculated from the probability density, which is given by the Parzen estimation method.
Abstract: The introduction of information systems in the medical field has made it possible to accumulate a large amount of health care examination data. Analysis of such data could yield valuable new knowledge about health and disease. In this paper, we propose a method for the analysis of large amounts of medical and health care data, especially images or signals. The proposed method treats data in a multidimensional space without any pre-processing, and the data is classified into groups according to the criterion. The criterion used in this paper is to maximize likelihood calculated from the probability density, which is given by the Parzen estimation method. The result of classification is expressed by a binary tree structure as a hierarchy of clusters. We applied this method to computer-generated data and practical electrocardiogram data, and the results showed its validity.
4 citations
TL;DR: This work extends the blockcipher algorithm, based on the iterations of so called “toggle” cellular automata rules to two dimensions, and finds that it is very suitable for opto-electronic implementation.
Abstract: Parallel architectures and algorithms may offer a solution to the system bottleneck arising from the need to encrypt a very large amount of data without compromising security. In this respect the use of cellular automata with their parallel, simple, regular and modular structure is very promising. We extend the blockcipher algorithm, based on the iterations of so called “toggle” cellular automata rules to two dimensions.The advantages are higher complexity of the crypt-analytical attacks and substantial increase in the speed of the algorithm. Due to its massive parallelism and interconnectivity, the algorithm is very suitable for opto-electronic implementation.
3 citations
24 Oct 1999
TL;DR: In this paper, a fast image reconstruction method for positron emission tomography (PET) using an algebraic technique was proposed, where a reconstruction operator is given approximately using subsets of sensitivity functions.
Abstract: Filtered backprojection (FBP) method for positron emission tomography (PET) produces artifacts in the reconstructed images when the measurement system has the shift-variant characteristics. On the other hand, the conventional algebraic reconstruction methods, such as the generalized analytic reconstruction from discrete samples (CARDS), the natural pixel decomposition (NPD) and the algebraic reconstruction technique (ART), can correct these characteristics, while these methods have computational burden. Here, the authors propose a fast image reconstruction method for PET using an algebraic technique. In this method, a reconstruction operator is given approximately using subsets of sensitivity functions. The subsets are designed by selecting the sensitivity functions that have high sensitivity to each point to be reconstructed and by keeping an accuracy of the reconstructed images. The proposed method was applied to simulated data for the scanner, ECAT EXACT HR+ (Siemens/CTI) working in the 2D mode. This result shows that the proposed method produces images with almost the same quality as the conventional algebraic methods do and has a similar computation time to FBP method.
3 citations
21 May 1999
TL;DR: A new method is proposed in order to detect interval changes accurately without using an image registration technique based on construction of so-called pattern histogram and comparison procedure and found that the method can be used as an alternative way of temporal change detection, particularly when the precise image registration is not available.
Abstract: An accurate and reliable detection of temporal changes from a pair of images has considerable interest in the medical science. Traditional registration and subtraction techniques can be applied to extract temporal differences when,the object is rigid or corresponding points are obvious. However, in radiological imaging, loss of the depth information, the elasticity of object, the absence of clearly defined landmarks and three-dimensional positioning differences constraint the performance of conventional registration techniques. In this paper, we propose a new method in order to detect interval changes accurately without using an image registration technique. The method is based on construction of so-called pattern histogram and comparison procedure. The pattern histogram is a graphic representation of the frequency counts of all allowable patterns in the multi-dimensional pattern vector space. K-means algorithm is employed to partition pattern vector space successively. Any differences in the pattern histograms imply that different patterns are involved in the scenes. In our experiment, a pair of chest radiographs of pneumoconiosis is employed and the changing histogram bins are visualized on both of the images. We found that the method can be used as an alternative way of temporal change detection, particularly when the precise image registration is not available.
2 citations
TL;DR: In this paper, the authors investigated a method of modeling scatter in multiple energy windows in cases of a few projection views and analyzed the system performance using singular value decomposition and resolution kernels.
Abstract: Using scattered radiation as useful information to improve radioisotope image quality is a topic attracting many researchers Some reports showed that incorporating scattered components offers a possibility to improve image quality The general method is modeling scatter in multiple energy windows and incorporating that information into the reconstruction process However, what the performance will be and how noise will behave when using scattered radiation in reconstruction are not yet well answered In this paper, we investigate a method of modeling scatter in multiple energy windows in cases of a few projection views The system performance is analyzed using singular value decomposition and resolution kernels For noise behavior investigation, reconstructions are accomplished by estimating the variance of reconstructed voxel values and the effectiveness of using scatter is evaluated by resolution kernel analysis The results show there are improvements in normalized mean-square error of the images and the resolution kernels When photon counts fall below about one million, it is still effective to use scatter for some cases of a few projections
2 citations
28 May 1999
TL;DR: Wang et al. as mentioned in this paper proposed a method for scatter compensation in SPECT imaging, by which they can estimate the scatter components in projections in high speed with a good accuracy, and they showed that a good uniformity of subtraction at both the center and side spheres and a good noise property can be acquired by proposed method compared with the dual-and triple-energy window methods.
Abstract: In this work, we propose a method for scatter compensation in SPECT imaging, by which we can estimate the scatter components in projections in high speed with a good accuracy. The method is that, at first, we estimate the scatter components in projections based on scatter response kernels by one time of OS-EM iteration, and then, subtract the estimated scatter components from the projections and complete the reconstruction by FBP method. The principle is that, the image corresponding to the scatter components in projections consist of almost low-frequency components of the activity distribution and the low-frequency components will converge faster than the high ones during iterative reconstruction. Therefore, we can estimate the low-frequency component image before the image converges with high-frequency ones and estimate the scatter components by re-projecting the low- frequency component image with scatter response kernels. The effects of the method were compared with dual- and triple- energy window methods using experimental measurements. The results show a good accuracy in estimated scatter components, a good uniformity of subtraction at both the center and side spheres and a good noise property can be acquired by proposed method compared with the dual- and triple-energy window methods.
2 citations
24 Oct 1999
TL;DR: An effective method for an automated detection of long-term interval changes from a sequence of time-varying medical images is presented and is advantageous to conventional image comparison techniques particularly when the precise image registration is not available prior to subtraction.
Abstract: An effective method for an automated detection of long-term interval changes from a sequence of time-varying medical images is presented. We combine the pattern histogram method with an autoregressive model and novel classification algorithm to distinguish real abnormality variations from chaotic behaviors of background patterns without using an image registration technique. We conducted an experiment using 16 personal chest radiographs of pneumoconiosis acquired at the time span of 33 years. It is confirmed that the proposed method is able to detect suspicious regions and it is advantageous to conventional image comparison techniques particularly when the precise image registration is not available prior to subtraction.
2 citations
TL;DR: A new method to detect interval changes from a pair of images is presented, especially when the precise image registration is not available, and this approach is preferred as an alternative image comparison technique.
Abstract: A new method to detect interval changes from a pair of images is presented, especially when the precise image registration is not available. We extend a well-known image histogram to multi-dimensions with some modifications and make use of it to elucidate differences between the images. The framework herein introduced is based on a comparison of the images in their multi-dimensional histogram space rather than in a spatial domain. Due to its flexibility, this approach is preferred as an alternative image comparison technique. The performance of the method is confirmed on simulated and real medical images and compared with the results from related conventional counterparts.
24 Oct 1999
TL;DR: The method is based on the hierarchical clustering method in multi-dimensional pattern vector space and considers to change the size of pattern vectors adaptively to explore useful image features which can be used in medical diagnosis.
Abstract: In this paper we propose a method for unsupervised image segmentation, which is suitable for finding the features contained in medical images. The method is based on the hierarchical clustering method in multi-dimensional pattern vector space. We consider to change the size of pattern vectors adaptively to explore useful image features which can be used in medical diagnosis. We have tested our method on the simulation image, which is generated by the Markov Random Field (MRF) model, and the real medical images, photomicrographs of colon tumor, and its effectiveness is confirmed.