Clinical diagnosis is often supported by a wide variety of medical images. Different images with their own specialized information complement each other. Biomedical image registration addresses the mapping of two or more images of same patient. In this paper, a Distance Transform (DT) is applied to the edge of the natural brain tissue in the CT image. Then the registration based on Chamfer matching algorithm is accomplished by using the edges of the natural brain tissue in MR-T1 and MR-T2 images as masks. When two images are precisely matched, the fusion of CT and MR is performed. The example used in this paper is the registration of CT and MR images of a brain tumor patient. After matching and fusion, the resultant image shows clear structure for all tissue and distinct contrast between gray matter and white matter. The tumor part in the final image pops out. Also, the details which show how the tumor presses the surrounding brain parenchyma are vivid. The algorithm can also be used as a reference for the matching of other medical images such as SPECT, PET, DSA, etc.
A ground simulator GSSS to be used at SAR factory, laboratory and imaging users is developed. When synchronization is set up between a real SAR and the GSSS, the system can be used to check, test the SAR system or make diagnosis for it. In this paper, we discuss the basic principle of GSS, and mainly focus on issues of SAR imaging, moving target detection and application of time-frequency transform algorithm. Several simulated examples of moving target detection and stationary scenery imaging are discussed. Simulation results are presented to validate the analysis and illumine new ideas.
The traditional method to extract target contour from aerial target image is changing the aerial image into a gray level image with multiple thresholds or binary image with single threshold. From the edge of target, contour can be extracted according to the changed value. The traditional method is useful only when contrast between target and background is in the proper degree. Snakes are curves defined within an image domain that can move under the influence of internal force coming from within the curve itself and external forces are defined so that the snake will conform to an object boundary or other desired features within an image. Snakes have been proved an effective method and widely used in image processing and computer vision. Snakes synthesize parametric curves within an image domain and allow them to move toward desired edges. Particular advantages of the GVF(Gradient Vector Flow) snakes over a traditional snakes are its insensitivity to initialization and its ability to move into boundary concavities. Its initializations can be inside, outside, or across the object’s boundary. The GVF snake does not need prior knowledge about whether to shrink or expand toward the boundary. This increased capture range is achieved through a diffusion process that does not blur the edges of themselves.
Affected by the light from different incident angle, the brightness of aerial target surface changed greatly in a complicate mode. So the GVF snakes is not fast, accurate and effective all the time for this kind of images. A new contour extracting method, GVF Snakes Combined with wavelet multi-resolution Analysis is proposed in this paper. In this algorithm, bubble wavelet is used iteratively to do the multi resolution analysis in the order of degressive scale before GVF Snakes is used every time to extract accurate contour of target. After accurate contour is extracted, polygon approximation is used to extract characteristics to realize the recognition of aerial target. The process is in the following: Step 1: use bubble wavelet filter to cut big part of the noises, weakening false edges. Step 2: initialize active contour and control the contour’s move according to GVF to get a new contour. Step 3: decrease the scale of filter, and use the new contour as the initial contour and control the contour’s move to get new contour again. Step 4: repeat step 3 till the set scale is reached. The last new contour is the final contour. Step 5: find the center determine an axis by calculate distance between every point on the final contour to the center. Step 6: adjust the distance threshold and combine the points until the contour is changed into a polygon with fixed angle number which is best fit the target recognition demand. Step 7: use the polygon to match the target plate to recognize target. Applied the new algorithm to aerial target images of a helicopter and a F22 battleplan, the contour extraction and polygon approximation results show that targets can be matched and recognized successfully. This paper mainly focuses on contour extraction and polygon approximation in the recognition area.
KEYWORDS: Data storage, Safety, Control systems, Signal to noise ratio, Optoelectronics, Data acquisition, Time-frequency analysis, Interference (communication), Data analysis
Locomotive safety controlling system is a system currently used in railway locomotives. The system detects and acquires useful parameters, analyzes the data, gives right decision according to the results and saves information for future use. As three of the important functions, compression, storage and analysis of voice data have shown to b e extremely useful. The analysis process shows that joint time-frequency extraction is effective when restoring voice from the unclear data. Not only does the SNR of the reconstructed signal improve greatly, most components of some random noise severely blurred voice signal can be removed as well. Description of system scheme and data analysis is included in the paper.
In this paper, we discuss the issue of radar imaging of multiple moving targets. When multiple moving targets are close to each other, the return signals from these targets are overlapped in time. Thus, by applying conventional motion compensation algorithms, when targets have rotational motion or maneuvering, multiple targets may not be resolved and each individual target cannot be clearly imaged. However, in cases where each individual target has its own velocity and moving direction different from others, then the different Doppler histories can be utilized to separate targets from each others. First we describe multiple target resolution. Then, we introduce the time-frequency based phase correction algorithm and discuss its limitation. Based on our previous work on time-frequency image formation, we propose a new method for radar imaging of multiple moving targets. With the new method, targets can be either point or extended targets, and target's motion can be either translational or rotational motion. Some examples are given for comparing the new method with the conventional Fourier method and time-frequency based phase correction methods.
When multiple radar targets are close to each other, the return signals form these targets are overlapped in time. Therefore, by applying conventional motion compensation algorithms designed for single target, the multiple targets cannot be resolved, and each individual target cannot be clearly imaged. However, each individual target may have its own velocity and direction different from others. These different Doppler histories can be utilized to separate target from each other. By taking time-frequency transforms, different Doppler changing rates can be estimated. Using each estimated Doppler changing rate and making phase correction, each individual target may be imaged. In this paper, we first review algorithms for radar imaging of multiple moving targets, then, analyze the performance of these algorithms, and, finally discuss the advantages and limitations of these algorithms by using simulated radar data.
Filter bank and multiresolution analysis have been widely used for wavelets. However, for multi-dimensional signals, the convolution algorithm needed for filter bank and multiresolution analysis is too complicated. In this paper, we propose a basic wavelet matrix, which can have either perfect reconstruction or desired result according to the chosen filter properties. The basic wavelet matrix method can be applied to pyramid wavelet decomposition, visual-based wavelet decomposition, tensor product, wavelet packets and adaptive tree-structured decomposition. Edge effects, the choice of filters are also discussed.
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