Abstract: A workstation-user interface for evaluating computer assisted diagnosis (CAD) methods for digital mammography is disclosed. Implementation of such an interface enables multiple, large-size images to be handled at high speeds. Furthermore, controls such as contrast, pan, and zoom, and tools such as reporting forms, case information, and analysis of results are included. The software and hardware used to develop such a workstation and interface were based on Sun platforms and the Unix operating system. The software is user friendly, and comparable to standard mammography film reading in terms of display layout and speed. The software, as designed, will work on entry-level workstations as well as high-end workstations with specialized hardware, thus being usable in an educational, training, or clinical environment for annotation purposes using CAD techniques as well as primary diagnosis.

Abstract: A workstation-user interface for evaluating computer assisted diagnosis (CAD) methods for digital mammography is disclosed. Implementation of such an interface enables multiple, large-size images to be handled at high speeds. Furthermore, controls such as contrast, pan, and zoom, and tools such as reporting forms, case information, and analysis of results are included. The software and hardware used to develop such a workstation and interface were based on Sun platforms and the Unix operating system. The software is user friendly, and comparable to standard mammography film reading in terms of display layout and speed. The software, as designed, will work on entry-level workstations as well as high-end workstations with specialized hardware, thus being usable in an educational, training, or clinical environment for annotation purposes using CAD techniques as well as primary diagnosis.

Abstract: A system and method for analyzing a medical image to determine whether an abnormality is present, for example, in digital mammograms, includes the application of a wavelet expansion to a raw image to obtain subspace images of varying resolution. At least one subspace image is selected that has a resolution commensurate with a desired predetermined detection resolution range. A functional form of a probability distribution function is determined for each selected subspace image, and an optimal statistical normal image region test is determined for each selected subspace image. A threshold level for the probability distribution function is established from the optimal statistical normal image region test for each selected subspace image. A region size comprising at least one sector is defined, and an output image is created that includes a combination of all regions for each selected subspace image.

Abstract: A computer-assisted diagnostic (CAD) method and apparatus are described for the enhancement, detection, and classification of suspicious regions in digital x-ray images, with particular emphasis on lung nodule detection using chest x-ray images. An objective is to improve the sensitivity and specificity of detection of suspicious areas such as nodules, while maintaining a low false positive detection rate. A modular CAD technique has been developed to be potentially automatic and to be used as a second-opinion method for lung nodule detection. The method consists of using a plurality of CAD modules to preprocess and enhance image features, including image preprocessing, selective enhancement, segmentation, and feature extraction using a multiresolution/multiorientation wavelet transform and a computationally efficient filter, a 1.5 D circular pattern filter.

Abstract: A computer-assisted diagnostic (CAD) method and apparatus are described for the enhancement and detection of suspicious regions in digital X-ray images, with particular emphasis on early cancer detection using digital mammography. An objective is to improve the sensitivity of detection of suspicious areas such as masses, while maintaining a low false positive detection rate, and to classify masses as benign or malignant. A modular CAD technique has been developed as a potentially automatic and/or second-opinion method for mass detection and classification in digital mammography that may in turn be readily modified for application with different digital X-ray detectors with varying gray-scale and resolution characteristics. The method consists of using a plurality of CAD modules to preprocess and enhance image features in the gray-level, the directional texture, and the morphological domains.

Abstract: A hybrid filter architecture and an artificial neural network is proposed for image enhancement and detection of suspicious areas in digital x-ray images that is operator, image, and digital x-ray sensor independent. The hybrid filter architecture includes an Adaptive Multistage Nonlinear Filter (AMNF) cascaded with an M-channel Tree Structured Wavelet Transform (TSWT). The AMNF shares the advantages of an array of linear and nonlinear filters and is adaptively supervised using either an order statistic or linear operator. The filter is used for noise suppression and image enhancement and adapts to the noise characteristics of the sensor. The TSWT is used for multiresolution image decomposition and reconstruction of subimages for further image enhancement of diagnostic features of interest.

Abstract: A computer-assisted diagnostic (CAD) method and apparatus are described for the enhancement and detection of suspicious regions in digital X-ray images, with particular emphasis on early cancer detection using digital mammography. An objective is to improve the sensitivity of detection of suspicious areas such as masses, while maintaining a low false positive detection rate, and to classify masses as benign or malignant. A modular CAD technique has been developed as a potentially automatic and/or second-opinion method for mass detection and classification in digital mammography that may in turn be readily modified for application with different digital X-ray detectors with varying gray-scale and resolution characteristics. The method consists of using a plurality of CAD modules to preprocess and enhance image features in the gray-level, the directional texture, and the morphological domains.

Abstract: A wavelet based neural network (WNN) is proposed for image restoration using a nuclear gamma camera. The WNN first segments the image noise from the detected signal using a wavelet or multi-resolution method and then restores the detector resolution using the neural network. An example of methods is described for detecting beta particles (bremsstrahlung radiation) but the proposed WNN can be applied to either the detection of positrons or gamma rays using the gamma camera.