Evidence Based Detection of Spiculated Masses and Architectural Distortions

摘要

Mass detection algorithms generally consist of two stages. The aim of the first stage is to detect all potential masses. Inthe second stage, the aim is to reduce the false-positives by classifying the detected objects as masses or normal tissue.In this paper, we present a new evidence based, stage-one algorithm for the detection of spiculated masses andarchitectural distortions. By evidence based, we mean that we use the statistics of the physical characteristics of theseabnormalities to determine the parameters of the detection algorithm. Our stage-one algorithm consists of two steps, anenhancement step followed by a filtering step. In the first step, we propose a new technique for the enhancement ofspiculations in which a linear filter is applied to the Radon transform of the image. In the second step, we filter theenhanced images with a new class of linear image filters called Radial Spiculation Filters. We have invented thesefilters specifically for detecting spiculated masses and architectural distortions that are marked by converging lines orspiculations. These filters are highly specific narrowband filters, which are designed to match the expected structures ofthese abnormalities and form a new class of wavelet-type filterbanks derived from optimal theories of filtering. A keyaspect of this work is that each parameter of the filter has been incorporated to capture the variation in physicalcharacteristics of spiculated masses and architectural distortions and that the parameters of the stage-one detectionalgorithm are determined by the physical measurements.