Automated method and system for digital image processing of radiologic images utilizing artificial neural networks

Abstract

An automated method and system for digital imaging processing of radiologic images, wherein digital image data is acquired and subjected to multiple phases of digital imaging processing. During the Pre-Processing stage, simultaneous box-rim filtering and k-nearest neighbor processing and subsequent global thresholding are performed on the image data to enhance object-to-background contrast, merge subclusters and determine gray scale thresholds for further processing. Next, during the Preliminary Selection phase, body part segmentation, morphological erosion processing, connected component analysis and image block segmentation occurs to subtract unwanted image data preliminarily identify potentials areas including abnormalities. During the Pattern Classification phase, feature patterns are developed for each area of interest, a supervised, back propagation neural network is trained, a feed forward neural network is developed and employed to detect true and several false positive categories, and two types of pruned neural networks are utilized in connection with a heuristic decision tree to finally determine whether the regions of interest are abnormalities or false positives.

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