There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources. We have developed a functional connectivity toolbox Conn (www.nitrc.org/projects/conn) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data. Compared to methods that rely on global signal regression, the CompCor noise reduction method all...

Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

Fuzzy c-means clustering with spatial information for image segmentation.

Group independent component analysis reveals consistent resting-state networks across multiple sessions.

The authors propose two new methods, namely iso-transmission line and subregion direct approximation methods, for dual energy image decomposition calculations. These two methods are compared with two other conventional techniques, i.e. non-linear equations and direct approximation. The computation accuracy, sensitivity to quantum noise and efficiency are used as indices for comparison. Both the subregion direct approximation method and the iso-transmission line method give better accuracy than the two conventional methods. The iso-transmission line, direct approximation and subregion methods are computationally more efficient than the non-linear equations method. The image calculated by the iso-transmission line and the subregion methods are more sensitive to noise.

https://iopscience.iop.org/article/10.1088/0031-9155/33/4/005/pdf

Comparison of four dual energy image decomposition methods

Micro positron emission tomography (PET) and micro single-photon emission computed tomography (SPECT), used for imaging small animals, have become essential tools in developing new pharmaceuticals and can be used, among other things, to test new therapeutic approaches in animal models of human disease, as well as to image gene expression. These imaging techniques can be used noninvasively in both detection and quantification. However, functional images provide little information on the structure of tissues and organs, which makes the localization of lesions difficult. Image fusion techniques can be exploited to map the functional images to structural images, such as X-ray computed tomography (CT), to support target identification and to facilitate the interpretation of PET or SPECT studies. Furthermore, the mapping of two functional images of SPECT and PET on a structural CT image can be beneficial for those in vivo studies that require two biological processes to be monitored simultaneously. This paper proposes an automated method for registering PET, CT, and SPECT images for small animals. A calibration phantom and a holder were used to determine the relationship among three-dimensional fields of view of various modalities. The holder was arranged in fixed positions on the couches of the scanners, and the spatial transformation matrix between the modalities was held unchanged. As long as objects were scanned together with the holder, the predetermined matrix could register the acquired tomograms from different modalities, independently of the imaged objects. In this work, the PET scan was performed by Concorde's microPET R4 scanner, and the SPECT and CT data were obtained using the Gamma Medica's X-SPECT/CT system. Fusion studies on phantoms and animals have been successfully performed using this method. For microPET-CT fusion, the maximum registration errors were 0.21 mm /spl plusmn/ 0.14 mm, 0.26 mm /spl plusmn/ 0.14 mm, and 0.45 mm /spl plusmn/ 0.34 mm in the X (right-left), Y (upper lower), and Z (rostral-caudal) directions, respectively; for the microPET-SPECT fusion, they were 0.24 mm /spl plusmn/ 0.14 mm, 0.28 mm /spl plusmn/ 0.15 mm, and 0.54 mm /spl plusmn/ 0.35 mm in the X, Y, and Z directions, respectively. The results indicate that this simple method can be used in routine fusion studies.

A three-dimensional registration method for automated fusion of micro PET-CT-SPECT whole-body images

An efficient method has been developed to speed up the computational time required for tissue separation after the dual‐energy scan in computerized tomography and digital radiography. This method consists of two steps. First, an isotransmission line method is used to replace the iterative algorithm for solving the two nonlinear equations, which is computationally noisy and sensitive. Second, two tables, one for the aluminum component and the other for the plastic component, are derived from calibration data in order to replace calculation by a table lookup. Results from this method are compared with the conventional method.

Keh-Shih Chuang

Papers

Fuzzy c-means clustering with spatial information for image segmentation.

Group independent component analysis reveals consistent resting-state networks across multiple sessions.

Comparison of four dual energy image decomposition methods

A three-dimensional registration method for automated fusion of micro PET-CT-SPECT whole-body images

A fast dual-energy computational method using isotransmission lines and table lookup.