University of South Florida

Maria Kallergi

Laurence P Clarke

Wei Qian

Michal Vossberg

Himanshu J Gohel

Computer aided diagnosis techniques in medical imaging are developed for the automated differentiation between benign and malignant lesions and go beyond computer aided detection by providing cancer likelihood for a detected lesion given image and/or patient characteristics. A computer aided detection and diagnosis algorithm for mammographic calcification clusters is developed and evaluated. The emphasis is on the diagnostic component although the algorithm includes automated detection, segmentation, and classification steps based on wavelet filters and artificial neural networks. Classification features are selected primarily from descriptors of the morphology of the individual calcifications and the distribution of the cluster as well as patient's demographics as input to the network. Te selected features are robust morphological and distributional descriptors, relatively insensitive to segmentation and detection errors such as false positive signals and variations among imaging sources or imaging equipment.

Computer aided diagnosis of mammographic microcalcification clusters

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.

Workstation interface for use in digital mammography and associated methods

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. A Multistage Artificial Neural Network (MANN) is proposed, together with Kalman Filtering for network training, for both improved detection or classification of suspicious areas and computational efficiency to allow the MANN to be applied to full digital images without operator input. The hybrid filter architecture and MANN may be applied to any gray scale image in medical imaging. The specific application of the proposed method includes: (a) improved enhancement or detection of suspicious areas as a 'second opinion' strategy using a computer workstation or (b) mass screening of large image databases such as that used for medical screening programs.

Image analyzing device using adaptive criteria

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.

Growing community of inventors

Tampa, FL, United States of America

Maria Kallergi