Various forms of cardiac pathology, such as myocardial ischemia and infarction, can be characterized with 13NH3-PET images. In clinical situation, polar map (bullseye image), which derived by combining images from multiple planes (designated by the circle around the myocardium in the above images), so that information of the entire myocardium can be displayed in a single image for diagnosis. However, image artifact problem always arises from body movement or breathing motion in image acquisition period and results in indefinite myocardium disorder region shown in bullseye image. In this study, a 3-D motion and movement correction method is developed to solve the image artifact problem to improve the accuracy of diagnostic bullseye image. The proposed method is based on 3-D optical flow estimation method (OFEM) and cooperates with the particular dynamic imaging protocol, which snaps serial PET images (5 frames) in later half imaging period. The 3-D OFEM assigns to each image point in the visual 3-D flow velocity field, which associates with the non-rigid motion of the time-varying brightness of a sequence of images. It presents vectors of corresponding images position between frames for motion correction. To validate the performance of proposed method, 10 normal and 20 abnormal whole-body dynamic PET imaging studies were applied, and the results show that the bullseye images, which generated by corrected images, present clear and definite tissue region for clinical diagnosis.
For practical clinical researches and applications, image segmentation is capable of extracting desired region information plays an important and essential role on a number of medical image preprocessing, including image visualization, malignant tissue recognition, multi-modalities image registration, and so forth. To classify the tissues by physiological characteristics is not satisfactory in high noise functional medical images. In this study, we incorporated both tissue time-activity curves (TACs) and derived "kinetic parametric curves (KPCs)" information to develop a novel image segmentation method for liver tissues classification in dynamic FDG-PET studies. Validation of proposed method, four commmon clustering techniques, include K-mean (KM), Fuzzy C-mean (FCM), Isodata (ISO), Markov Random Fields (MRF), and proposed method were compared to evaluate its precision of segmentation performance. As results, 35.6% and 6.7% less mean errors in mean difference for KPCs and TACs are performed, respectively, than other methods. With combined KPCs and TACs based clustering method can provide the ability to diagnose ill liver tissues exactly.
In clinical research, non-invasive MR perfusion imaging is capable of investigating brain perfusion phenomenon via various hemodynamic measurements, such as cerebral blood volume (CBV), cerebral blood flow (CBF), and mean trasnit time (MTT). These hemodynamic parameters are useful in diagnosing brain disorders such as stroke, infarction and periinfarct ischemia by further semi-quantitative analysis. However, the accuracy of quantitative analysis is usually affected by poor signal-to-noise ratio image quality. In this paper, we propose a hemodynamic measurement method based upon sub-band denoising and spline curve fitting processes to improve image quality for better hemodynamic quantitative analysis results. Ten sets of perfusion MRI data and corresponding PET images were used to validate the performance. For quantitative comparison, we evaluate gray/white matter CBF ratio. As a result, the hemodynamic semi-quantitative analysis result of mean gray to white matter CBF ratio is 2.10 ± 0.34. The evaluated ratio of brain tissues in perfusion MRI is comparable to PET technique is less than 1-% difference in average. Furthermore, the method features excellent noise reduction and boundary preserving in image processing, and short hemodynamic measurement time.
KEYWORDS: 3D modeling, Brain, Single photon emission computed tomography, Neuroimaging, Image registration, Quantitative analysis, Medical imaging, 3D image processing, 3D imaging standards, Positron emission tomography
Functional medical imaging, such as PET or SPECT, is capable of revealing physiological functions of the brain, and has been broadly used in diagnosing brain disorders by clinically quantitative analysis for many years. In routine procedures, physicians manually select desired ROIs from structural MR images and then obtain physiological information from correspondent functional PET or SPECT images. The accuracy of quantitative analysis thus relies on that of the subjectively selected ROIs. Therefore, standardizing the analysis procedure is fundamental and important in improving the analysis outcome. In this paper, we propose and evaluate a normalization procedure with a standard 3D-brain model to achieve precise quantitative analysis. In the normalization process, the mutual information registration technique was applied for realigning functional medical images to standard structural medical images. Then, the standard 3D-brain model that shows well-defined brain regions was used, replacing the manual ROIs in the objective clinical analysis. To validate the performance, twenty cases of I-123 IBZM SPECT images were used in practical clinical evaluation. The results show that the quantitative analysis outcomes obtained from this automated method are in agreement with the clinical diagnosis evaluation score with less than 3% error in average. To sum up, the method takes advantage of obtaining precise VOIs, information automatically by well-defined standard 3-D brain model, sparing manually drawn ROIs slice by slice from structural medical images in traditional procedure. That is, the method not only can provide precise analysis results, but also improve the process rate for mass medical images in clinical.
In clinically, structural image based brain tissue segmentation as a preprocess plays an important and essential role on a number of image preprocessing, such as image visualization, object recognition, image registration, and so forth. However, when we need to classify the tissues according to their physiological functions, those strategies are not satisfactory. In this study, we incorporated both tissue time-activity curves (TACs) and derived kinetic parametric curves (KPCs) information to segment brain tissues, such as striatum, gray and white matters, in dynamic FDOPA-PET studies. Four common clustering techniques, K-mean (KM), Fuzzy C-mean (FCM), Isodata (ISO), Markov Random Fields (MRF), and our method were compared to evaluate its precision. The results show 41% and 48% less mean errors in mean difference for KPCs and TACs, respectively, than other methods. Combined KPCs and TACs based clustering method provide the ability to define brain structure effectively.
In the context of functional positron emission tomographic (PET) images analysis, the segmentation method can not only entails the separation of the image into regions of similar attribute but also presents clearer understanding about the features embedded in the original image to improve the quantitative analysis. However, for completely recording, clinical instruments often collect subject signal as well as signals from background environment, which are regarded as noises of various levels. High noise often makes the original PET image unrecognizable and difficult to analyze. Thus, manual or semiautomatic methods have been utilized to overcome the difficulty of high noise image segmentation. Furthermore, the success of image segmentation is one of the important key factors in the accompanying automated system, and there has been no general segmentation method that can be applied to the high noise PET images of different feature characteristics. However, the PET image is high noisy causing by the imaging procedure, and the image quality of PET image is affected inherently. To improve this issue, a novel nonlinear anisotropic diffusion technique based on the diffusion theorem with multi-scale and edge detection scheme to inhibit the noise level and hold the boundary characteristics of the high noise PET image was provided in this paper.
Since the positron emission tomographic (PET) image shows the abnormal activity of brain by the different parts of the brain respond to different stimuli and the patient's response to noise, illumination, change in mental concentration, and other activity, the functional PET image data is much helpful for clinical diagnosis. However, the PET image is also a high noise image that the quality of the PET image and the diagnosis accuracy is affected by the noise. To improve the quality problem of PET image, a novel subband denoising technique is provided in this paper. The method is based on the subband transformation and the statistical features in each subbands of the PET image.
Since the breast cancer is one of the major mortality increasing to middle-aged women, the digital mammograms are used to diagnose the breast cancer broadly. However, the digital mammography requires high spatial resolution and high gray-level resolution. These requirements result in very large image file sizes. Thus, the image transmission and image quality are very important problems in clinical diagnose. Several lossy image compression techniques have been developed to handle this issue. To improve these obstacles, we develop a statistical-based sub-band filtering technique for digital mammogram to increase the compression ratio and apply on the digital mammogram to enhance the disease part, and inhibit the noise in the image. By the way, the digital mammogram data can be compressed effectively and the image quality can also be improved conspicuously.
KEYWORDS: Prostate, Image segmentation, Magnetic resonance imaging, Image processing, 3D image reconstruction, 3D image processing, Medical imaging, 3D modeling, Image analysis, Data processing
The purpose of this paper is to develop a method for prostate gland segmentation in MR image by combining the two scan planes and Fourier descriptor technique. Because not all of the prostate gland in MR image slices have clear boundary that between a prostate gland and its surrounding soft tissues. In this study, the paper improves the problems by using the two scan planes method. And, reconstructing the prostate gland by the image deformable model that integrates the Fourier descriptor method and energy continuity concept. The two scan planes method uses two MR prostate image sets; one is axial and the other is coronal. The coronal image set is for supplementing the base and the apex regions of the prostate gland in the axial image set. By this method, the prostate gland segmentation in MR images will be obtained more correctly. Then the known boundary images of the axial and coronal images can be reconstructed to 3-D image by the Fourier descriptor technique. The technique of this study integrates the spatial coordinate, energy continuity concept and Fourier descriptor to describe the objects for time sequence or spatial related images. Therefore, the model of this work can estimates the interpolation or exterpolational images that from the known images and reconstructs them to three-dimensional object efficiently and accurately.
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