Abstract: A method and system for the computerized automatic analysis of lesions in magnetic resonance (MR) images, a computer programmed to implement the method, and a data structure for storing required parameters is described. Specifically the system includes the computerized analysis of lesions in the breast using spatial, temporal and/or hybrid measures. Techniques include novel developments and implementations of two-dimensional and three-dimensional features to assess the characteristics of the lesions and in some cases give an estimate of the likelihood of malignancy or of prognosis. The system can also allow for the enhanced visualization of the breast and its pathological states. The system also includes an option to merge the extracted features with those from x-ray and/or ultrasound images in order to further characterize the lesion and/or make a diagnosis and/or a prognosis.

Abstract: A method and system for the automated detection of lesions in medical images. Medical images, such as mammograms are segmented and optionally processing with peripheral enhancement and/or modified median filtering. A modified morphological open operation and filtering with a modified mass filter are performed for the initial detection of circumscribed lesions. Then, the lesions are matched using a deformable shape template with Fourier descriptors. Characterization of the match is done using simulated annealing, and measuring the circularity and density characteristics of the suspected lesion. The procedure is performed iteratively at different spatial resolution in which at each resolution step a specific lesion size is detected. The detection of the lesion leads to a localization of a suspicious region and thus the likelihood of cancer.

Abstract: A method, computer program product, and system for assessing tumor extent in medical temporally acquired images. The method, on which the computer program product and system is based, includes obtaining image data corresponding to temporally acquired images including a tumor and surrounding anatomy and performing variance processing on the obtained image data to derive variance image data defining a variance image indicative of variation of voxels in the temporally acquired medical images. Techniques include novel developments and implementations of breast volume segmentation, breast border removal, lesion enhancement, determination of the bounding sphere, computation of a 3-D search volume, suppression of surrounding structures, and volume growing. Output from the methods yields an estimate of the extent of the tumor (lesion) in the breast.

Abstract: A method and apparatus for the computerized automatic analysis of lesions in ultrasound images, including the computerized analysis of lesions in the breast, using gradient, gray-level, and texture based measures. Echogenicity features are developed to assess the characteristics of the lesions and in some cases give an estimate of the likelihood of malignancy or of prognosis. The output from the computerized analysis is used in making a diagnosis and/or prognosis. For example, with the analysis of the ultrasound images of the breast, the features can be used to either distinguish between malignant and benign lesions, or distinguish between (i.e., diagnosis) the types of benign lesions such as benign solid lesions (e.g., fibroadenoma), simple cysts, complex cysts, and benign cysts. The ultrasound image features can be merged with those from mammographic and/or magnetic resonance images of the same lesion for classification by means of a common artificial neural network.

Abstract: A method for the automated segmentation of medical images, including generating image data from radiographic images of the breast. The method is applicable to breast mammograms including the extraction of the skinline as well as correction for non-uniform exposure conditions, hand radiographs, and chest radiographs. Techniques for the segmentation include noise filtering, local gray value range determination, modified global histogram analysis, region growing and determination of object contour. The method also is applicable to skin detection and analysis of skin thickening in medical images, where image segmentation, local optimization of external skinline, creation of a gradient image, identification of the internal skinline and then skin thickness determination are carried out.