Evidence based detection of spiculated masses and architectural distortions

Mass detection algorithms generally consist of two stages. The aim of the first stage is to detect all potential masses. In the 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 and architectural distortions. By evidence based, we mean that we use the statistics of the physical characteristics of these abnormalities to determine the parameters of the detection algorithm. Our stage-one algorithm consists of two steps, an enhancement step followed by a filtering step. In the first step, we propose a new technique for the enhancement of spiculations in which a linear filter is applied to the Radon transform of the image. In the second step, we filter the enhanced images with a new class of linear image filters called Radial Spiculation Filters. We have invented these filters specifically for detecting spiculated masses and architectural distortions that are marked by converging lines or spiculations. These filters are highly specific narrowband filters, which are designed to match the expected structures of these abnormalities and form a new class of wavelet-type filterbanks derived from optimal theories of filtering. A key aspect of this work is that each parameter of the filter has been incorporated to capture the variation in physical characteristics of spiculated masses and architectural distortions and that the parameters of the stage-one detection algorithm are determined by the physical measurements.