In this paper, a novel direction-based interpolation approach with discrete orthogonal polynomial decomposition is introduced. A 2D digital image is usually regarded as a sampling of an underlying 2D continuous function, which is called an image field. When the image field is considered as a scalar potential field, the interpolation problem is converted to that if the values at some points in a potential field are given, how to estimate the value of any point more accurately. Both the edges of the image and the content of the objects are well preserved if the image is interpolated along the equipotential lines instead of the coordinate axes. In this study, the equipotential direction at each pixel in the interpolated plane is calculated from the partial derivatives of the discrete orthogonal polynomial decomposition of the original image. For each point, the equipotential line through it is searched in a step-by-step way, guided by the equipotential directions. The value of a point is interpolated linearly from the values of points with known values along the equipotential line. Refinement scheme is applied to interpolate the images to the desired scale. Experiments on a set of CT images show that this method not only preserves the shape structure efficiently even for the objects with complicated structures but also has a low time complexity.
A new approach, a pyramid template matching as guided by a snake, is developed for the extraction of pelvic features in both portal and simulation images. Initially, the treatment field edge was extracted using a Canny edge detector. A template modeled using a polynomial function for the typical pelvic structures was used as the initial approximation for the anteroposterior (AP) pelvic brim. Several energies were defined to search the actual bony structures as guided by the snake in a larger range. In this study, a double-snake model coupled with a spring was developed to define the external constraint force. The image force was calculated from various processed images using different edge detection algorithms. The criterion used for search termination was to find the locations where the overall energy was at its minimum. The result of the initial search was fit using a polynomial function as the second approximation for the pelvic bony structure. Snake searching technique was repeated in a smaller range around the initial identified features for fine search of pelvic bony structure. This technique has shown to be very promising for extracting pelvic features.
A fully automated system is being developed for the portal verification of tangential breast fields in radiation therapy of breast cancer. The automated verification system involves image acquisition, image feature extraction, feature correlation between reference and portal images, and quantitative evaluation of patient setup. In this study, the portal images are acquired using a matrix liquid ion-chamber electronic portal imaging device (EPID), and have a matrix size of 256 X 256 pixels with 12-bit gray levels. A hierarchical region processing technique is developed to extract poor contrast features in the portal image generated by megavoltage photon beams at different levels sequentially. The treatment field is initially extracted from the portal image. The skin line is then extracted from the treatment field. Finally, the lung/soft tissue separation is extracted from the breast region. A Chamfer matching filter is used to correlate features in the portal image with those in the reference image. The resulting parameters for rotation, translation and scaling are used for the setup evaluation of the treatment field.
We are developing an 'intelligent' workstation to assist radiologists in diagnosing breast cancer from mammograms. The hardware for the workstation will consist of a film digitizer, a high speed computer, a large volume storage device, a film printer, and 4 high resolution CRT monitors. The software for the workstation is a comprehensive package of automated detection and classification schemes. Two rule-based detection schemes have been developed, one for breast masses and the other for clustered microcalcifications. The sensitivity of both schemes is 85% with a false-positive rate of approximately 3.0 and 1.5 false detections per image, for the mass and cluster detection schemes, respectively. Computerized classification is performed by an artificial neural network (ANN). The ANN has a sensitivity of 100% with a specificity of 60%. Currently, the ANN, which is a three-layer, feed-forward network, requires as input ratings of 14 different radiographic features of the mammogram that were determined subjectively by a radiologist. We are in the process of developing automated techniques to objectively determine these 14 features. The workstation will be placed in the clinical reading area of the radiology department in the near future, where controlled clinical tests will be performed to measure its efficacy.
We are developing various computer-vision schemes for the detection of masses and microcalcifications in digital mammograms. However, for the effective and efficient implementation of computer-aided diagnosis (CAD), appropriate man-machine interfaces must be developed. Thus, our plan is to incorporate our schemes into a dedicated workstation for use as a 'second opinion' in a mammographic screening program. Output from the computer would be displayed as an aid, leaving the final diagnostic decision with the radiologist.
The incidence of breast cancer in women continues to increase1 . Studies have shown that early detection of breast cancer through periodic mammographic screening of asymptomatic women could reduce breast cancer mortality by 3050. However screening yields a high volume of mammograms requiring interpretation. In addition accurate characterization of detected masses is an important task of radiologists in order to reduce the number of unnecessary biopsies. Although some general rules have been suggested for the differentiation of malignant and benign masses34 considerable misclassification of masses still occurs. In fact on average only 20-30 of masses referred for surgical breast biopsy are actually malignant''5. As a potential aid to radiologists in mammographic screening programs we are developing a computer-vision system for the detection and characterization of masses in digital mammograms6''7. This system includes a detection subsystem and a characterization subsystem. Motivated by the systematic methods of viewing mammograms used by radiologists the detection system is designed to analyze the deviation from the architectural symmetry of normal right and left breasts and employs gray-level histogram analysis a bilateral-subtraction technique and run-length linking of multiple subtraction images to locate potential masses. False-positive detections are further reduced by various feature-extraction techniques. The characterization system employs various image analysis techniques such as the measurement of margin spiculation of masses in order to estimate the likelihood of malignancy.
A prototype digital chest system, which uses storage phosphor technology and has the advantages over
existing computed radiography systems (CR) of compactness and immediate image display, is being evaluated in our
laboratory. We evaluated the imaging properties of the Konica Direct Digitizer (KDD) in order to assess its potential
usefulness for general clinical use, or as a front-end for a PACS. The prototype system consists of a new stimulable
phosphor (RbBr.Tl) detector read by a compact semiconductor laser scanning system, with images immediately
displayed on a CRT or transferred to a host computer. The imaging characteristics of resolution and noise were
evaluated, using display parameters matched to a Kodak Lanex Medium/OC system. Preliminary results using
sensitive composite test objects show an increase in noise and a slight decrease in resolution as compared to
conventional radiography. However, subjective comparison of a chest phantom and volunteer images indicates that
these differences may not be clinically significant. Further development is needed to provide increased absorption,
and thus improved image quality.
At present mammography is the most effective method for the early detection of breast cancer1 . Detection and classification of masses in mammograms are among the most important and difficult tasks performed by radiologists. Various studies have indicated that regular mammographic screening can reduce the mortality from breast cancer in women2. Thus mammography may become one of the largest volume x-ray procedures routinely interpreted by radiologists. The miss rate for the radiographic detection of malignant masses ranges from 12 to 30 percent. In addition although general rules exist for the visual differentiation of benign and malignant masses error does occur in the classification of masses with the current methods of radiologic characterization. Thus it is apparent that the efficiency and effectiveness of screening procedures could be increased by use of a computer system that successfully aids the radiologist in detecting and characterizing mammographic masses. We are developing computerized schemes for the automated detection and classification of masses in digital mammograms. The detection scheme utilizes the architectural symmeiry of the left and right breasts and digital bilateralsubtraction techniques in order to increase the conspicuity of the mammographic mass prior to the application of featureextraction techniques. The classification scheme involves the extraction of border information from the mammographic mass in order to quantify the degree of spiculation which is related to the likelihood of malignancy. METHODS Clinical screen/film mammograms were used in the
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