The last two decades have witnessed unprecedented developments of imaging systems making use of 3D visualization. These new technologies have revolutionized diagnostic radiology, by providing information about the interior of the human body never before available. Ultrasound imaging is an important cost-effective technique used routinely in the management of a number of diseases. However, technical improvements are needed before its full potential is realized, particularly in applications involving minimally invasive therapy or surgery. 2D viewing of 3D anatomy, using conventional ultrasound, limits our ability to quantify and visualize the anatomy and guide therapy. This occurs because the use of 2D ultrasound requires that the diagnostician integrate multiple images in his mind. This practice is inefficient, and may lead to variability and incorrect diagnoses. Also, the 2D ultrasound image represents a thin plane at an arbitrary angle in the body. It is difficult to localize the image plane, and reproduce it at a later time. Over the past 2 decades, investigators have addressed these limitations by developing 3D ultrasound techniques. In this paper we describe our developments of 3D ultrasound techniques for imaging organs such as the prostate, breast, and kidney. To produce a 3D image, the ultrasound transducer is scanned mechanically or using a free-hand technique. The images are digitized and then reconstructed into a 3D image, which can be viewed and manipulated interactively. In addition, the user can segment the organ and measure its volume manually or using semi-automatic techniques. In this paper we describe the use of 3D ultrasound for diagnosis, image-guided therapy and quantifying organ volume. Examples will be given for imaging various organs, such as the prostate, carotid arteries, and breast, and for the use in 3D ultrasound-guided brachytherapy. In addition, we describe 3D segmentation methods that can be used for analysis of the volume of the prostate and carotid vessel lumen using 3D ultrasound images. The segmentation techniques applied to 3D ultrasound images has been shown to be less variable than manual segmentation techniques and of value in both 3D ultrasound-guided prostate brachytherapy and in the assessment of carotid plaque progression/regression.
In many application areas of imaging sciences, object information captured in multi-dimensional images needs to be extracted, visualized, manipulated, and analyzed. These four groups of operations have been (and are being) intensively investigated, developed, and applied in a variety of applications. In this paper, after giving a brief overview of the four groups of operations, we put forth two main arguments: (1) Computers are digital, and most image acquisition and communication efforts at present are toward digital approaches. In the same vein, there are considerable advantages to taking an inherently digital approach to the above four groups of operations rather than using concepts based on continuous approximations. (2) Considering the fact that images are inherently fuzzy, to handle uncertainties and heterogeneity of object properties realistically, approaches based on fuzzy sets should be taken to the above four groups of operations. We give two examples in support of these arguments.
This paper describes an original strategy for an automatic quantitative estimation of the regional myocardial wall thickening from segmentation of echocardiographic images. Our method is decomposed in two steps : first, we have developed a user-friendly computer software of segmentation to extract the myocardial wall in each image of the sequence. Secondly, the software automatically computes the regional wall thicknesses evolution for a cardiac sequence. The results are shown on regional diagrams. Furthermore, the percent systolic wall regional thickening is computed. This quantitation ofregional wall thickening by ultrasound imaging should be very helpful for physicians in the assessment ofthe cardiac contraction function
During the last few years, the development of the modern medicine has permitted the accurate diagnosis on more symptom of illness. But it is the imbalance of the medical treatment on different areas and decentralization of the medical resources that limited the widely applying on more people. However, as the important evidence on medical diagnosis, medical images need to be collaborative processed because of their large sizes, modality and processing complexity. Therefore one of the main aims on medical treatment now is to establish the distributed computer to support collaboration working environment based on web. The establishing of the environment is help to dissolve the problem about medical collaborative working on different areas, computer systems and network structures and to permit more people to receive the high quality medical care. In this paper, a distributed medical image collaborative framework was presented using the JAVA (a network computing language) and CORBA (Common Object Broker Request Architecture, a distributed computing standard). From the experimental result with the framework, it was clear that the framework made possible collaborative processing of the medical image by using many collaborative tools.
In this paper, an important class of nonlinear adaptive speckle filter, called#segmentation-based filter, has been used to suppress speckles with few detail lost and edge fuzziness. The initial image is first segmented into regions of different tissue and lesion characteristics using a self-creating and organizing neural network (SCONN) based on fractal features. Then each of the segmental regions is processed by a different filter parameter. SCONN is a modified self-organizing neural network (SONN), which can search for an optimal number of output nodes automatically and has no dead center nodes and boundary effect. Experimental results of several sectional ultrasonic images show that our method can filter the medical ultrasonic images efficiently and proved to be superior to traditional filters.
This paper discusses such a binary three-dimensional (3D) vessel model whose voxel will be 1 if it belongs to vessel volume otherwise it will be 0. The model can be considered as a stack piled up by a series of slices, and each slice is a 2D binary image. A digital subtraction angiogram (DSA) image is a projection of the 3D vessel model in certain angle, while the projection of a slice is a line in the DSA image. The paper is thus to discuss the representation of the slice projection and the reconstruction model. Based on an assumption of parallel ray geometry, a binary projection model of the slice is introcded, which can be describe by Boolean Radon Transform (BRT). Then a nonlinear quantitative model of DSA image is presented. Simultaneous a slice reconstruction algorithm is developed, which is called optimal square wave decomposition (OSWD). In the end, some simulation results are given, which prove validity of the models.
Base on the analysis of the characteristics of bronchoscopic fluorescence image, four kinds of digital image processing (DIP) techniques are presented to enhance the contrast of fluorescence image between early lung cancer and surrounding normal tissues. Firstly, nearest neighbor averaging method is used to smooth noise that comes from different sources, which mainly including the photoelectric and electronic noise in the diagnostic system. Next, a method of enhancing contrast in fluorescence image is devised based on real-time digital subtraction of a background video image from a signal-plus-background video image. Then, segmental linearity image enhancement technique is applied after background subtraction to project the lesions. Finally, A processing technique for pseudocolor display of monochrome image is performed to enhance color contrast for the fluorescence image of early lung cancer, which highly enhanced the discrimination of lung cancer image in color. The validity of these approaches have been verified and primary clinical results show that a sufficient fluorescence contrast of suspicions versus normal tissue is obtained, which can effectively eliminate the false positive and negative diagnosis occurring in the clinical application.
One of the goals of the National Cancer Institute (NCI) cancer control program to reach more than 80% of the eligible women in mammography screening by the year 2000 was not fully realized and yet remains as a challenge. In fact, breast cancer is the only type of cancer with a positive growth rate over the last few years (+1 .2%). That is primarily due to 1)the fact that examination process is a complex and lengthy one and 2) it is not available to the majority of women who live in remote and urban sites. This problem can be solved using advanced networking technologies and signal processing algorithms. On one hand, software modules can help detect, with high precision, true negatives (TN), while marking true positives (TP) for further investigation. Since TNs are the majority of examinations on a randomly selected population, this first step reduces the load on radiologists by a tremendous amount. On the other hand, high-speed networking equipment can accelerate the required clinic-lab connection and make detection, segmentation and image enhancement algorithms readily available to the radiologists. This research describes the Asynchronous Transfer Mode (ATM) Telemammography Network (ATMTN) architecture for real-time, on-line screening, detection and diagnosis of breast cancer. ATMTN is a high-speed network integrated with associated automatic robust Computer Assisted Diagnostic (CAD) methods for mass detection
In this work, we developed a snake algorithm to approach for the detection of edge of microcirculation blood vessel images from microscope. The images we process are the image sequences obtained from a microscope-television system. The difficulty of the approach is the bad image quality. The algorithm was based on dual active contour model to overcome some problems of the conventional snakes. Two contours extended from the initial contour are used as the inner and outer contour. We then apply dynamic programming to search the minimization within the search domain. The results show that the method can detect the object contours in complex background more effective. This work is helpful for the research on tracking the motion of the blood vessel and the segmentation of blood vessel net in microcirculation.
As an extremely important part of vessel's three-dimensional reconstruction, the vessel cross-section's reconstruction has been incisively researched around the world. Up to now, most of the reconstruction methods use circle or ellipse vessel cross-section as prior information model and search optimal cross-section contour with complex arithmetic. Most researchers have paid little attention to exploiting the vessel's elasticity qualities, which virtually can bring many advantages to the whole reconstruction process. Based on this viewpoint, a novel parametrical model of vessel cross-section is presented using vessel's elasticity qualities in this paper. Sufficient experiments have been made to test the effects of the elasticity model. These results indicate that the parametrical model can excellently describe the various real vessel's cross-section contour by controlling these limited parameters. For instance, when ten parameters are adopted, the model's reconstruction error can be reduced to 2.5%. The study of the elasticity model is quite promising in many aspects of 3D reconstruction of vessel, and the model's further application potential is apparent.
We propose a new method for reduction of the patient dose by using scattered X-rays in order to achieve the same density without scattered rays. The minimum perceptible thickness difference (delta) Xmin of the object was calculated using psychophysical analysis for various radiographic densities, scatter fractions and luminous exitances of the viewer. The mAs values to obtain many densities were measured using four kinds of anti-scattered X-ray grid with their scatter fractions. These measured values were applied to above calculated psychophysical results. The smallest value of (delta) Xmin for acrylate of thickness 20cm was 0.18mm, if the scattered X-rays were negligible. The value of (delta) Xmin increases with increasing scatter fraction. The perceptibility of human eye is influenced by luminous exitance of the viewer. By increasing the luminous exitance from 1500 lm(DOT)m-2 to 8000 lm(DOT)m-2, the patient dose can be reduced 33 percent in maximum under the same perceptibility of (delta) Xmin. The method of changing grid will be considered.
The technique of support vector machines (SVM's) has been used as a new method for solving classification, regression, time series prediction and function estimation problems with many successful applications. In this paper, we use SVM to solve the problem of classification for ultrasonic medicine image. We use statistical characteristics of the interesting part in an ultrasonic image to aid a doctor to give a correct diagnosis. The procedure is: first, to extract a number of small sampling regions in the interesting part; second, to calculate a series of moments about those sampling regions; third, to decide whether the interesting part of the organ is normal or abnormal according to the analyses of the series of moments based on SVM. SVM's structural risk minimization principle is the guarantee that the diagnosis has the minimum mistake probability. The diagnosis based on SVM is optimal from the viewpoint of structural risk minimization principle. It is hoped that the results presented here will be helpful to the diagnosis based on ultrasonic medicine image.
The three-dimensional reconstruction of human brain vessels has been the research focus in recent year. Reconstructing the axis and the cross section of vessels respectively is one effective method in 3D vessel reconstruction. However the conventional reconstruction methods usually take the complicated space relations of different projections into counts to match a vessel segment in different angles. Obviously, this kind of method is very convoluted and can not be easily used in general situations. Considering that the underlying cause of the difficulty in reconstruction is the complexity of brain vesselsí» intricate directions and bifurcation styles, this paper proposes a novel method of vessel axis reconstruction based on vessel bifurcation model. The experiments are satisfying.
The spatial distributions of bone and soft tissue in human body are separated by independent component analysis (ICA) of dual-energy x-ray images. It is because of the dual energy imaging model¡-s conformity to the ICA model that we can apply this method: (1) the absorption in body is mainly caused by photoelectric absorption and Compton scattering; (2) they take place simultaneously but are mutually independent; and (3) for monochromatic x-ray sources the total attenuation is achieved by linear combination of these two absorption. Compared with the conventional method, the proposed one needs no priori information about the accurate x-ray energy magnitude for imaging, while the results of the separation agree well with the conventional one.
Tumour hypoxia is an important biological feature that is very close related to vasculature, and it has been proved to play a crucial role in the radiation response of solid tumours. In this paper we present a novel image analysis technique for simultaneous tumour hypoxia grading and blood vessel detection in dual-stained tissue sections, originated from the bladder region of patients treated by radiotherapy. The K-Nearest Neighbour classification scheme is employed initially in order to label the image colour pixels. Classification is based on a training set selected from manually drawn regions corresponding to the biological patterns being segmented. For tissue section images presenting a low quality staining, some further processing is required to reject misclassified pixels. A series of specific task-oriented routines have been developed (texture analysis, fuzzy c-means clustering and edge detection), in order to improve the final segmentation result. Validation experiments indicate that the algorithm can robustly detect these biological features, even in tissue sections with very inhomogeneous staining. This approach has also been combined with other image analysis procedures to objectively obtain quantitative measurements of potential clinical interest.
Detecting early symptoms of breast cancer is very important to enhance the possibility of cure. There have been active researches to develop computer-aided diagnosis(CAD) systems detecting early symptoms of breast cancer in digital mammograms. An expert or a CAD system can recognize the early symptoms based on microcalcifications appeared in digital mammographic images. Microcalcifications have higher gray value than surrounding regions, so these can be detected by expanding a region from a local maximum. However the resultant image contains unnecessary elements such as noise, holes and valleys. Mathematical morphology is a good solution to delete regions that are affected by the unnecessary elements. In this paper, we present a method that effectively detects microcalcifications in digital mammograms using a combination of local maximum operation and the region-growing operation.
An effective method based on mutual information to solve the problem of medical image retrieval is presented in this paper. This method has three features, which are scale invariance, position invariance and rotation invariance. The complexity of algorithm is significantly reduced, and image segmentation is avoided. The results show that the performance of retrieval by this method is better than that by others.
In the analysis of nuclear medicine images, it is generally necessary to recognize organ's boundary and determine some special regions of interest (ROl). This paper will introduce a method of auto-edge detection of the ROl. Due to some spatial properties of nuclear medicine images, traditional method, such as using edge detection operators, sometimes can not give us the correct ROl. The region-growing method is frequently used in image segmentation. Though this method needs a large calculation, it can get exact edge of ROl by utilize several properties ofthe image directly. So it is adaptive to nuclear medicine image that has small size and a lot of local spatial properties. The procedure of region-growing method is: At first, the image is segmented into several cells. Secondly pick up one typical pixel, examine the value of the pixel with the clinical statistical criterion to decide whether the pixel is a part of the target organ. If it meets the criterion, merge it into the target organ. If not, reject this pixel. Then go on checking all neighbors of the pixel until all image have been examined. By means of the spatial relationship of the pixels, we can easily find the edge ofthe target organ. Using this method in edge detection of some clinical images such as kidney, heart, liver, we find that the result is better than some other methods.
In this paper, a model for correction of distortion in endoscope image is proposed. The definition of optical distortion is described briefly and the theory of correction of distortion using grid consisting of solid circles is presented. Then, we address the three steps of correction, including pre-processing of grid image, correction of spatial distortion and reconstruction of gray level. Finally, the corrected results are given to demonstrate the performance and validity of correction algorithm with standard calibration grid.
During the ultrasonic cardio-image 3-D reconstruction, one of the difficult problems is that there are some speckles that show strongly variance of image gray level, the speckles reduce the space difference and remain the detailed structure under cover. The filtering technology can be classified into space domain filtering and frequency domain filtering, and spatial filtering methods have been widely used in ultrasonic medical image processing, according to the statistical characteristic of the speckle, the speckle filtering method based on multidirectional morphological structure is presented. The experiment shows that the speckle noise has been filtered and image details have been preserved by the method based on local statistical property of the image, certainly the speckle filtering is not the final objective and would like to be proved and perfected in the further research of ultrasonic image segmentation and analyzing.
This paper explores a novel dynamic programming (DP) based optimal technique in ultrasound image (USI) edge detection, which is less constrained now than previous. Dynamic programming is an optimal approach in multistage decision-making. In an image segmentation system, we want to find a global optimal contour with connectedness and closeness. The DP algorithms process the object image to get the minimum cumulative cost matrix to tracing a global optimal edge. Combined with LUM nonlinear enhancement filter and Gaussian preprocessor, this method shows robustness on noisy image edge detection.
Extraction of edge feature and accurate measurement of vascular diameter in cardiovascular image are the bases for labeling the coronary hierarchy, 3D refined reconstruction of the coronary arterial tree and accurate fusion between the calculated 3D vascular trees and other views. In order to extract vessels from the image, the grayscale minimization of the circle template and differential edge detection are put forward. Edge pixels of the coronary artery are set according to maximization of the differential value. The edge lines are determined after the edge pixels are smoothed by B-Spline function. The assessment of feature extraction is demonstrated by the excellent performance in computer simulation and actual application.
The motion of coronary arteries has attracted more and more attention because of its possible effects on the development of atherosclerosis and potential clinical application for diagnosis of cardiovascular disease. Angiography is the best clinical modality so far to extract this information spatially and temporally. In this paper, a new method, which combines 'snakes' and a template matching technique, is proposed to track vessel segments in angiographic image sequences. By considering both global and local motion, the vessel can be tracked well in a cardiac cycle.
We have developed a method for analyzing motion at skeletal joints based on the 3D reconstruction of magnetic resonance (MR) image data. Since the information about each voxel in MR images includes its location in the scanner, it follows that information is available for each organ whose 3D surface is computed from a series of MR slices. In addition, there is information on the shape and orientation of each organ, and the contact areas of adjacent bones. By collecting image data in different positions we can calculate the motion of the individual bones. We have used this method to study human foot bones, in order to understand normal and abnormal foot function. It has been used to evaluate patients with tarsal coalitions, various forms of pes planus, ankle sprains, and several other conditions. A newly described feature of this system is the ability to visualize the contact area at a joint, as determined by the region of minimum distance. The display of contact area helps understand abnormal joint function. Also, the use of 3D imaging reveals motions in joints which cannot otherwise be visualized, such as the subtalar joint, for more accurate diagnosis of joint injury.
In the paper, we analyze comprehensively the mathematics of the Algebraic Reconstruction Techniques (ART) in the Computerized Tomography (CT), obtain some illumining conclusions, and then we design the procedure of ART simulation. The experiment result is also presented in the paper.
The present paper studies the method of 3D reconstruction of multiplane transesophageal rotational scanning echocardiography. According to the characteristic of rotational scanning echocardiography, a direct matching interpolation method is exploited to reconstruct regular volume data from distributed ultrasound scanning points. The whole system is developed and clinical ultrasound data is tested for this method. The volume rendering results show that the proposed method is valid and effective. At last, the possibility of functional reconstruction based on tissue Doppler imaging is explored.
The heart is the most important organ to our life. The coronary artery is the only path through which blood is provided to the heart. In the vessels of the heart especially the coronary artery, fluency of blood is mainly determined by vascular diameter and blood pressure. If blood is obstructed thus flows not freely due to vascular straitness, coronary heart disease would have a high occurrence among the aged people. Therefore, measurement of vessel diameter of coronary artery and the vascular parameters are of great importance of reflecting the function of the heart and disease diagnosis. In this paper, one novel and simple method to measure the coronary arterial diameter is presented and the relative difference of vessel (Dpi) is defined as one clinic diagnosis criterion for the degree of vascular straitness.
Sinoatrial node plays an important role in cardiac conduction system because it initiates the cardiac electrical activation, and sets the rate and rhythm of the heart. A method based on ultrasound strain rate imaging is proposed in this paper to reflect the local compression and expansion rates not affected by overall heart translation, or regional deformation during the cardiac cycle under real physiological conditions. In this method, tissue Doppler motion information was first derived from Doppler tissue velocity images, and then the quantitative velocity values was mapped according to the color bar in the images. Strain rate was estimated from the velocity value according to the relation between velocity gradient and strain rate. Preliminary result of color-coded strain rate images of the sinoatrial node of a dog is given. Result shows a variable pattern in different cardiac phase within a cardiac cycle. Further study of the method may provide a new non-invasive way to observe and characterize sinoatrial node.