KEYWORDS: Hyperspectral imaging, RGB color model, Elasticity, Education and training, Tissues, Image quality, Collagen, Digital imaging, Data modeling, Data conversion
SignificanceQuantification of elastic fiber in the tissue specimen is an important aspect of diagnosing different diseases. Though hematoxylin and eosin (H&E) staining is a routinely used and less expensive tissue staining technique, elastic and collagen fibers cannot be differentiated using it. So, in conventional pathology, special staining technique, such as Verhoeff’s van Gieson (EVG), is applied physically for this purpose. However, the procedure of EVG staining is very expensive and time-consuming.AimThe goal of our study is to propose a deep-learning-based computerized method for the generation of RGB EVG stained tissue from hyperspectral H&E stained one to save the time and cost of conventional EVG staining procedure.ApproachH&E stained hyperspectral image and EVG stained RGB whole slide image of human pancreatic tissue have been leveraged for this experiment. CycleGAN-based deep learning model has been proposed for digital stain conversion while images from source and target domains are of different modalities (hyperspectral and RGB) with different channel dimensions. A set of three basis functions have been introduced for calculating one of the losses of the proposed method, which retains the relevant features of EVG stained image within the reduced channel dimension of the H&E stained one.ResultsThe experimental results showed that a set of three basis functions including linear discriminant function and transmittance spectrum of eosin and hematoxylin better retained the essential properties of the elastic fiber to be discriminated from collagen fiber within the reduced dimension of the hyperspectral H&E stained image. Also, only a smaller number of paired training data with our proposed training method contributed significantly to the generation of more realistic EVG stained image with more precise identification of elastic fiber.ConclusionsRGB EVG stained image is generated from hyperspectral H&E stained image for which our model has performed two types of image conversion simultaneously: hyperspectral to RGB and H&E to EVG. The experimental results show that the intentionally designed set of three basis functions contains more relevant information and prove the effectiveness of our proposed method in generating realistic RGB EVG stained image from hyperspectral H&E stained one.
Quantifying elastic fiber in the tissue specimen is an important aspect of diagnosing different diseases. In conventional pathology, special staining technique such as EVG (Verhoeff’s Van Gieson) is applied physically for this purpose which is expensive and time-consuming procedure. Though H&E (Hematoxylin and Eosin) staining is routinely used, less expensive and most common tissue staining technique, elastic and collagen fibers cannot be differentiated using it. This study proposes a modified CycleGAN based unsupervised method for the computerized generation of RGB EVG stained tissue from hyperspectral H&E stained one to save the time and cost of conventional EVG staining procedure. Our proposed method is designed to utilize the sufficient spectral information provided by the H&E hyperspectral image (HSI) without reducing the spectral dimension. For doing so, we have faced challenges to calculate one of the training losses (identity loss) of CycleGAN that requires reducing the channel dimension of H&E HSI to be the same as RGB EVG stained image. We have addressed the issue by adopting intentionally designed three basis functions that can reduce the channel dimension of HSI into three without losing the essential color of elastic fibers. The set of this function includes Linear Discriminant Function (LDF) and the transmittance spectrum of Eosin and Hematoxylin which has proved to best preserve the underlying important features of EVG stained image while reducing the dimensionality of hyperspectral H&E. The experimental result proves the feasibility of our proposed method to generate realistic EVG stained image from its corresponding H&E stained one.
SignificanceMalignant skin tumors, which include melanoma and nonmelanoma skin cancers, are the most prevalent type of malignant tumor. Gross pathology of pigmented skin lesions (PSL) remains manual, time-consuming, and heavily dependent on the expertise of the medical personnel. Hyperspectral imaging (HSI) can assist in the detection of tumors and evaluate the status of tumor margins by their spectral signatures.AimTumor segmentation of medical HSI data is a research field. The goal of this study is to propose a framework for HSI-based tumor segmentation of PSL.ApproachAn HSI dataset of 28 PSL was prepared. Two frameworks for data preprocessing and tumor segmentation were proposed. Models based on machine learning and deep learning were used at the core of each framework.ResultsCross-validation performance showed that pixel-wise processing achieves higher segmentation performance, in terms of the Jaccard coefficient. Simultaneous use of spatio-spectral features produced more comprehensive tumor masks. A three-dimensional Xception-based network achieved performance similar to state-of-the-art networks while allowing for more detailed detection of the tumor border.ConclusionsGood performance was achieved for melanocytic lesions, but margins were difficult to detect in some cases of basal cell carcinoma. The frameworks proposed in this study could be further improved for robustness against different pathologies and detailed delineation of tissue margins to facilitate computer-assisted diagnosis during gross pathology.
Significance: Skin cancer is one of the most prevalent cancers worldwide. In the advent of medical digitization and telepathology, hyper/multispectral imaging (HMSI) allows for noninvasive, nonionizing tissue evaluation at a macroscopic level.
Aim: We aim to summarize proposed frameworks and recent trends in HMSI-based classification and segmentation of gross-level skin tissue.
Approach: A systematic review was performed, targeting HMSI-based systems for the classification and segmentation of skin lesions during gross pathology, including melanoma, pigmented lesions, and bruises. The review adhered to the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. For eligible reports published from 2010 to 2020, trends in HMSI acquisition, preprocessing, and analysis were identified.
Results: HMSI-based frameworks for skin tissue classification and segmentation vary greatly. Most reports implemented simple image processing or machine learning, due to small training datasets. Methodologies were evaluated on heavily curated datasets, with the majority targeting melanoma detection. The choice of preprocessing scheme influenced the performance of the system. Some form of dimension reduction is commonly applied to avoid redundancies that are inherent in HMSI systems.
Conclusions: To use HMSI for tumor margin detection in practice, the focus of system evaluation should shift toward the explainability and robustness of the decision-making process.
In our previous study, we proposed a hand-waving finger vein authentication system, in which finger region extraction
from captured images was effective to verify the finger vein patterns with high accuracy. However, it is not easy to
extract the correct finger region from grayscale images that are taken with a near infra-red LED, because the background
condition of the captured image changes complicatedly according to the location of the waving hand. In order to
overcome this limitation, we propose an alternative finger region extraction method that takes color images with an RGB
camera and a white light source and identifies the finger region based on the skin color information.
Evaluation of tissue margins and hemodynamics is necessary during macropathology of skin lesions. This study aims to produce saliency maps of skin chromophores from ex-vivo specimens and observe the effect of formalin fixation on the maps. We used a multi-spectral imaging system with narrow-band illumination to capture various skin lesions. Saliency maps were produced with three different methods adapted from the literature by utilizing spectral absorption and absorption slope. Saliency maps derived from fixed and unfixed tissue were registered and subsequently compared in terms of correlation and histogram similarity. Preliminary results show high dissimilarity between maps of fixed and unfixed tissue, highlighting the influence of formalin fixing on hemodynamics, while relative distribution of melanin remained mostly unaffected.
A new simultaneous whole-body PET/CT imaging geometry based on the OpenPET imaging structure has been
proposed. In this geometry, multiple x-ray sources are adopted to implement the same field of view at the exactly same
time for the simultaneous PET and CT imaging process. In this paper, we conducted further quantitative analysis to the
new geometry by computer simulation. Then we examined and compared the iterative and analytical algorithms in terms
of image quality, regarding the features of the geometry. Results indicated better images were acquired with the iterative
algorithms under this geometry for the whole-body range rather than the analytical method. Improved reconstruction
images were still expected with generalization of modified algorithm for the proposed geometry. Technical
implementation of the geometry in clinical application should also be further considered.
Double Random Phase Encoding (DRPE), which is a typical optical encryption technique, has been reported to be
vulnerable to Known Plaintext-Attacks (KPAs) using a Phase Retrieval Algorithm (PRA). But the reported case in which
the encryption key is successfully estimated was that the plain image was rather simple such as the image of a character.
In addition, although Phase Only DRPE (PO-DRPE) was proposed to achieve more resistance to the KPA than Complex
DRPE (C-DRPE) in which both amplitude and phase components are used as an encrypted image, no quantitative results
about the relationship between the vulnerability and the plaintext image. In this paper, we show the result of quantitative
analysis on KPA by PRA to C-DRPE and PO-DRPE, for the plaintext images of different characteristics. As a result of
experiment, KPA to C-DRPE succeeded to estimate the correct key while the probability of success became lower when
the number of non-zero pixel increases in the plaintext image. However, KPA to PO-DRPE enabled to estimate only
"singular" keys, which are effective for no more than given plaintext/ciphertext image pair and far different from the
correct encryption key. We also conducted KPA using two plaintext-ciphertext image pairs for KPA. In the case when
two plaintext-ciphertext image pairs were given to KPA, the cryptanalysis succeeded with higher probability than the
case of one. Moreover, the probability of success in the KPA was high even in PO-DRPE.
The simultaneous imaging system of PET and Fluorescent CT is being developed in the National Institute of
Radiological Science in Japan. For the simultaneous system, we are considering using the DOI-PET detectors as
simultaneous detectors of the gamma ray for PET and NIR light for fluorescence by changing the upper reflectors to the
dichroic mirrors. Here, DOI-PET detector has very low spatial resolution to the NIR light compared to basically used
CCD cameras. However, since the NIR light is scattered by biological tissues, it can be possible to reconstruct valuable
image from the data which acquired from low resolution devices. In this study, we show feasibility for the Fluorescent
CT imaging using the DOI-PET detector by computer simulations. In the simulations, we used a cubic phantom and
square shaped detector geometry and a diffusion equation to approximate the light propagation. The system matrices of
the Fluorescent CT geometries having different detector resolutions are calculated and we evaluated the singular values
of the matrices. Using the system matrices, we simulated the image reconstruction from observed data which is
generated by simulation and noise added. As a result, it is confirmed the reconstructed image from low resolution
detectors is as same level as one from higher resolution detectors.
The projection X-ray microscope utilized a very small X-ray source emitted from a thin (0.1-3 μm) target metal film
excited by the focused electron beam of a scanning electron microscope (SEM). When an object is placed just below the
target metal film, the diverging X-rays enlarge the shadow of the object. Because no X-ray optics such as a zone-plate is
used, the focal depth is, in principle, infinitely large. We exploited this to apply projection X-ray microscopy to
three-dimensional (3-D) structure analysis by means of cone-beam computed tomography (CT).
A small arthropod (Pseudocneorhinus bifasciatus, 5 mm in length) was examined for CT study. The projection images
were recorded at 3-degree increments over the whole range (360°) of a stepping-motor-controlled sample rotator. The
3-D reconstructed image was calculated to be 256 x 256 x 256 (5 μm) voxel data. The reconstructed 3-D image showed
in detail the internal structure of an opaque object.
Trial for element mapping using projection X-ray microscope is also performed by developing a new target exchanger.
This apparatus enables exchange of metal targets without leaking vacuum of SEM. By taking images using Kα line from
nickel and cobalt targets, distribution of iron, which has absorption edge between two Kα lines, can be shown.
Distribution of less than 10 μm iron particles is distinguished from cobalt particles. This system would be applicable for
3-D element analysis.
A novel four-layered depth-of-interaction (DOI) positron emission tomography (PET) scanner is being developed at National Institute of Radiological Sciences, Japan. It aims to improve the image resolution, particularly at the edge of field of view while maintaining high sensitivity. However, inter-crystal scatter (ICS) occurs in the detector blocks of the jPET-D4. It is a phenomenon where there are multiple scintillations for a single irradiation of gamma photon due to Compton scatter in detecting crystals. Because of the Anger-type logic calculation, only one approximated position is detected by the jPET-D4 in the case of ICS. This causes error in position detection and ICS worsens the image contrast, particularly for smaller hotspot. In this paper, we propose to model an ICS probability by utilizing a Monte-Carlo simulator. It is a statistical relationship between gamma-ray first interaction crystal pair and the detected crystal pair. The ICS probability is then used to improve the system matrix of statistical image reconstruction algorithm ML-EM in order to correct the error of ICS. We have shown in computer simulation that image contrast is recovered successfully by applying the proposed method.
KEYWORDS: Color reproduction, Visualization, Light emitting diodes, Switching, LED displays, Control systems, Telecommunications, Optimization (mathematics), Integrating spheres, Standards development
In the conventional color reproduction based on the colorimetric match for a standard observer, color mismatch can be perceived if the color matching functions of the observer deviate from those of the standard observer; this phenomenon is known as observer metamerism. Recently, multi-primary display, using more than three-primary colors, has attracted attention as a color reproduction media because of its expanded gamut and its possibility to reduce the color mismatch caused by observer metamerism. In this paper, a new color conversion method for multi-primary display that reduces the observer metamerism is proposed. The proposed method gives the multi-dimensional control value of a display device to minimize the spectral approximation error under the constraints of tristimulus match. Reproduced spectrum by a seven-primary display is simulated and evaluated by the color matching functions of Stiles's 20 observers. The results confirmed that the proposed method reduces the color reproduction error caused by observer variability compared to the other seven-primary reproduction and conventional three-primary reproduction. The preliminary visual evaluation results with a seven-primary display using light-emitting diodes are also introduced.
Here, we propose a new method to enhance the sensitivity of the reflectance spectrum to the scattering feature of the superficial tissue layer. This method is based on multiple discriminant analysis (MDA) in the eigen subspace of the spectrum. Considering the application of scattering imaging, we evaluated this method by performing multispectral imaging of two-layered tissue phantoms. A color map converted from the spectral reflectance well corresponds to variations in the size of the scatter in the first layer. In order to confirm our proposed method works well under more realistic conditions, we performed the computational simulations of the light propagation in the tissue. We used the simulation model combined with the Monte Carlo and the Mie scattering. Its conditions like the slab geometry and the particle distribution of the cell nucleus were estimated by the image measuring of pathological slices. Results on simulations show the possibility of enhancing the sensitivity of the reflectance spectrum to the scattering feature of the superficial tissue layer.
This study was performed to examine the usefulness of medical endoscopic imaging utilizing narrow-band illumination. The contrast between the vascular pattern and the adjacent mucosa of the underside of the human tongue was measured using five narrow-band illuminations and three broadband illuminations. The results demonstrate that the pathological features of a vascular pattern are dependent on the center wavelength and the bandwidth of illumination. By utilizing narrow-band illumination of 415±30 nm, the contrast of the capillary pattern in the superficial layer was markedly improved. This is an important benefit that is difficult to obtain with ordinary broadband illumination. The appearances of capillary patterns on color images were evaluated for three sets of filters. The narrow, band imaging (NBI) filter set (415±30 nm, 445±30 nm, 500±30 nm) was selected to achieve the preferred appearance of the vascular patterns for clinical tests. The results of clinical tests in colonoscopy and esophagoscopy indicated that NBI will be useful as a supporting method for observation of the endoscopic findings of early cancer.
Color reproduction systems using multispectral imaging techniques make it possible to accurately reproduce the color of the original object under various viewing illuminants. In this paper, a multispectral image compression method for high fidelity color reproduction is proposed in consideration of color degradation. In the proposed method, a spectral transform and a nonlinear quantization designed to reduce colorimetric error are combined with the discrete wavelet transform in JPEG2000. Through the experiments using some 16-band multispectral images, it is confirmed that the
proposed method reduces the average and the maximum color differences in L*a*b* color space in comparison with the conventional methods.
Fingerprint verification for smart card holders is one of the methods which are able to identify smart card holders with a high level of security. However, an ingenious implementation is needed to execute it in the embedded processor quickly and safely, because of its computational burden and the limitation of the smart card performance. For this purpose, we propose a hybrid method which is a combination of personal identification number (PIN) verification with a smart card and an optical fingerprint verification method. The result of a preliminary computer simulation to evaluate the proposed system shows that false acceptance rate is completely zero, though false rejection rate is a little inferior to the conventional figerprint verification system.
We present the method which can calculate the spectral reflectance from physical parameters corresponding to the pathological features, e.g. average size of cell nuclei and standard deviation of cell nuclear size distribution, in consideration of multiple scattering in biological tissue. In this paper, the method combined the Monte Carlo method which simulates multiple scattering effects and the Mie theory which provides phase function (angular properties of light scattering) and scattering coefficient was employed. In order to investigate the validity of this method, the calculated spectra by the method and Monte Carlo method with Henyey-Greenstein phase functions were compared with measurement spectra derived from the tissue phantom whose size distribution has double peaks. From the results, it is shown that the method can better predict the spectral reflectance of tissue phantom rather than Monte Carlo method with Henyey-Greenstein phase function.
Multispectral imaging is receiving attention in medical color imaging, as high-fidelity color information can be acquired by the multispectral image capturing. On the other hand, as color enhancement in medical color image is effective for distinguishing lesion from normal part, we apply a new technique for color enhancement using multispectral image to enhance the features contained in a certain spectral band, without changing the average color distribution of original image. In this method, to keep the average color distribution, KL transform is applied to spectral
data, and only high-order KL coefficients are amplified in the enhancement.
Multispectral images of human skin of bruised arm are captured by 16-band multispectral camera, and the proposed color enhancement is applied. The resultant images are compared with the color images reproduced assuming CIE D65 illuminant (obtained by natural color reproduction technique). As a result, the proposed technique successfully visualizes unclear bruised lesions, which are almost invisible in natural color images. The proposed technique will
provide support tool for the diagnosis in dermatology, visual examination in internal medicine, nursing care for preventing bedsore, and so on.
We have applied projection X-ray microscope for three- dimensional (3D) structure analysis by means of cone-beam computed tomography. The projection images of small insect, Omiscus Porcellio, were recorded in every 1 degree for whole direction (360 degree) with a stepping motor controlled sample rotator. The images were recorded with cooled CCD camera (HAMAMATSU C4880) which detect X-ray directly. The 3D image was reconstructed from cone-beam projections using back-projection algorithm. The resolution of the reconstructed 3D image (256 x 256 x 256 pixels) was about 20 micrometers . The digestive organs were clearly visualized in 3D.
A method to reproduce images on an object under various observation geometries is presented. In this method, a multispectral image sequence is captured with rotating the object under a point light source, where spectral distribution and the position are measured. The diffuse and specular reflection images are decomposed from the captured images based on the dichromatic reflection and the Lambertian models. The angular distribution of reflected light is obtained by the decomposed images, and the images under new observation geometry are synthesized by using light-ray rearrangement technique. In the experiments using two types of 2D objects, leather and fabric, it is confirmed that synthesized images under new illumination geometry are almost the same as the images actually captured under the new geometry.
The range of the reproducible color, i.e., color gamut, of the conventional display devices such as CRTs (cathode ray tubes) and LCDs (liquid crystal displays) is sometimes insufficient for reproducing the natural color of an object through color imaging systems. In this paper, six-primary color display is presented to reproduce the expanded color gamut, by using two conventional RGB projectors and six interference filters. The design method of the filters is also introduced to maximize the volume of the color gamut in CIE-LUV uniform color space. Using the experimental system, the gamut of the six-primary projection display is evaluated comparing with that of conventional CRTs and projectors.
KEYWORDS: Expectation maximization algorithms, Optical spheres, Reconstruction algorithms, Image processing, Monte Carlo methods, Image analysis, Signal attenuation, Single photon emission computed tomography, Signal to noise ratio, Image quality
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.
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.
For a reliable diagnosis through visual telecommunication systems, it is very important to reproduce patient images with correct color. Natural color television system previously proposed is capable of reproducing the original color of the objects using multispectral images, even when the illumination environment for observing images is different from the illumination used for recording. In this paper, to render this system practical for telemedicine system, we reduce the number of color bands of multispectral camera by utilizing the principal components of spectral reflectances in human skin class. In addition, we investigate a simple method, using color charts, to obtain the recording information of both illumination and camera. As color chart selection criterion, it becomes clear that principal components for human skin class need to be described by the linear combination of spectral reflectances of color charts. Principal components for human skin class are obtained through measurements and analysis of 484 human skin spectral reflectances. In the experiment, natural color reproduction for human skin is realized from limited number of color bands using color charts for acquiring the recording information instead of spectral measurement devices.
Analyzing medical images, which have been stored in digital information system day by day, is expected to make it possible to formulate knowledge useful for image diagnosis. In this paper, we propose a method for unsupervised medical image segmentation as the pre-processing of the analysis aiming to clear the relation between the image features and the possible outcome of a medical condition. In the proposed method, a square region around the every pixel is considered as a pattern vector, and a set of pattern vectors acquired from whole image are classified by using the technique of hierarchical clustering. In the hierarchical clustering, the set of pattern vectors is divided into two clusters at each node, according to the statistical criterion based on the entropy in thermodynamics. Results on the test image generated by the Markov Random Field model and the real medical images, photomicrographs of intestine, are shown.
Parallel architectures and algorithms will offer a solution to the system bottleneck arising from the need to encrypt very large amount of data without compromising security. In this respect the use of cellular automata (CA) with their parallel, simple, regular and modular structure is very promising. So far proposed cryptosystems based on CA use iterations of binary, 1D CA. We extend the block-cipher algorithm, based on the backward iteration and forward iteration of so-called 'toggle' CA rules to two-dimensions. Higher dimensional CA have more complex behavior and in general their inversion is a NP problem, therefore they are potentially resistant against cryptanalytical attacks. Other advantages are substantial increase in the speed of the algorithm, parallel with the increase in the block size and key length. The algorithm allows customized block and key size. It uses two independent keys, each of them sufficient for secure encryption. This allows one of the keys to be replaced by the time stamp, user identification information or other relevant information. Hardware implementations of the algorithm are considered.
KEYWORDS: Cameras, Color reproduction, Imaging systems, Multispectral imaging, Televisions, Telecommunications, Telemedicine, CRTs, Human vision and color perception, Reflectivity
In the telemedicine application through visual communication systems, the reproduction of color is quite important. The purpose of this work is to develop a method for the reproduction of the natural color of the object in the TV system for telemedicine. When the illumination of observation environment is different form the object illumination, the change in color perception is caused by the color adaptation of human vision. In the method presented in this paper, the difference of illumination condition is corrected by using multispectral information captured by a multispectral camera. This paper shows the methods and the basic results for the reproduction of two types of color; the reproduction of color image as if the object is directly observed, and the reproduction of color appeared when the object is placed under the observation condition.
In order to give medical doctors objective information of the internal surface of the organs, which will lead to an improvement of the quality of the endoscopic diagnosis, it is necessary to develop a method to quantitatively measure colors from the digital images of CCD endoscope. However, when the internal surface of the human digestive organs is illuminated by the light of the endoscope, images captured by the endoscope become reddish compared to the original object due to the reflections by the surrounding red mucosal surface. This phenomenon makes it difficult to measure the object color correctly. We propose a method to compensate the color shift of illumination and to estimate the color of the mucous internal surface from CCD endoscopic images. In this method, the spectral reflectance of the internal surface and the spectral distribution of the illumination are represented by a weighted sum of some functions given by the statistical analysis of the surface reflectances. Each weighting factor is estimated from sequential images captured by the CCD endoscope. The effectiveness of the proposed method is confirmed by a basic experiment using a CCD endoscope and color charts.
SPECT imaging system has shift-variant characteristics due to nonuniform attenuation of gamma-ray, collimator design, scattered photons, etc. In order to provide quantitatively accurate SPECT images, these shift-variant characteristics should be compensated in reconstruction. This paper presents a method to correct the shift-variant characteristics based on a continuous-discrete mapping model. In the proposed method, the projection data are modified using sensitivity functions so that filtered backprojection (FBP) method can be applied. Since the projection data are assumed to be acquired by narrow ray sum beams in the FBP method, narrow ray sum beams are approximated by a weighted sum of sensitivity functions of the measurement system, then the actual projection data are corrected by the weighting factors. Finally, FBP method is applied to the corrected projection data and a SPECT image is reconstructed. Since the proposed method requires the inversion of smaller matrices than the conventional algebraic methods, the amounts of calculation and memory space become smaller, and the stability of the calculation is greatly improved as well. The results of the numerical simulations are also demonstrated.
Due to the development of digital information system in medical field, a large amount of image or signal data obtained from health examination has been stored. Analyzing these data is expected to make it possible to formulate new diagnostic knowledge for health care. In this paper, we propose a classification method suitable for the analysis of a large amount of medical data, for the purpose of assisting medical doctors to analyze the data. Int he proposed method, image or signal data are treated as vectors and mapped into multi-dimensional space, then hierarchical clustering method is applied. To obtain optimal division of cluster, a statistical criterion is introduced, and a binary tree of clusters is constructed base don the criterion. From the results of experiment using generated data and ECG signal, it is confirmed that the data sets can be correctly classified by our proposed method.
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