Image analyzing device using adaptive criteria

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. 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.