A digital image display apparatus for converting a pixel value of medical digital image data such as MRI image data or CT image data into brightness in accordance with a display window including a window level and a window width of a display unit, determines the optimum window level and width for each image as follows. The apparatus obtains a histogram of pixel values from the digital image data and calculates brightness data of a pixel value having a highest frequency, brightness data of a pixel value at a boundary between a background and an image, area data of a portion having middle brightness within a display brightness range, area data of a portion having maximum brightness, and data indicating a ratio between an area of a portion having higher brightness than the middle brightness and an area of a portion having lower brightness than that obtained, when the digital image is to be displayed by a given display window on the basis of the histogram. The apparatus obtains image quality indicating clarity of the image displayed by the given window on the basis of the above data by using arithmetic operations or by using a neural network, thereby determining the optimum display window which provides a maximum image quality.
A method and apparatus is presented in which a medical imagery workstation provides an end-user interface which when activated, windows and levels a whole image or a region of interest within the image utilizing the pixel values within a selection area. The method and apparatus customizes the pixel values when the entire image is selected before calculating the window and levels to produce a higher contrast, then redraws the entire image utilizing the newly calculated window and level values. The present invention provides a method and apparatus to enable an operator to define a region of interest, that when activated, the image is redrawn utilizing only the pixel values from the region of interest, maximizing the brightness and contrast of the selected.
An adaptive hierarchical neural network based system with online adaptation capabilities has been developed to automatically adjust the display window width and center for MR images. Our windowing system possesses the online training capabilities that make the adaptation of the optimal display parameters to personal preference as well as different viewing conditions possible. The online adaptation capabilities are primarily due to the use of the hierarchical neural networks and the development of a new width/center mapping system. The large training image set is hierarchically organized for efficient user interaction and effective re-mapping of the width/center settings in the training data set. The width/center values are modified in the training data through a width/center mapping function, which is estimated from the new width/center values of some representative images adjusted by the user. The width/center mapping process consists of a global spline mapping for the entire training images as well as a first-order polynomial sequence mapping for the image sequences selected in the user's new adjustment procedure.
A system for deriving final display parameters for a wide range of MR images consists of a feature generator utilizing both histogram and spatial information computed from an input MR image, a wavelet transform within the feature generator for compressing the size of the feature vector, a competitive layer based neural network for clustering MR images into different subclasses, a bi-modal linear estimation network and a radial bases function network based non-linear estimator for each subclass, as well as a data fusion system using estimates from both estimators to compute the final display parameters.
A method and apparatus are provided for acquiring and reconstructing an image. The method includes the steps of obtaining prior knowledge of the image, possibly by coarse sampling of the image and using the obtained prior knowledge of the image to identify relative locations of structures having relatively high contrast edges. The method further includes the steps of prescribing a set of k-space locations based upon the relative locations of the structures in order to achieve comparable eigenvalues of a reconstruction matrix and sampling the k-space at the prescribed k-space locations to obtain k-space sample data. The k-space sample data are decomposed into background data and edge data. The background data are Fourier transformed to reconstruct a background image component. Similarly, subsets of the edge data are Fourier transformed and the reconstruction matrix is used to form a linear combination of these Fourier transformations in order to reconstruct an edge image component. Finally, the background image component and the edge image component are combined to generate a final image.
Image data of a target pixel and peripheral pixels are stored in a memory. Using a most significant bit extractor circuit, 4 most significant bits of data are extracted from each image data. A histogram circuit generates a histogram of the extracted 4-bit data. Referring to the histogram, a data processor circuit (17) replaces the image data of the target pixel with a maximum value of the numbers of pixels having the same level and outputs the processed data. Then, a digit-complementing circuit converts the data output from the data processor circuit to 8-bit data and outputs the converted data. In this manner, a regular image is converted into an image similar to a draft-design image.
