Multispectral co-occurrence analysis for medical image processing

Presented is a new computer aided multispectral image processing method which is used in 3 spatial dimensions and 1 spectral dimension where the parametric dynamic contrast enhanced magnetic resonance breast maps derived from voxelwise model-fitting represent the spectral dimension. The method is based on co-occurrence analysis using a 3-dimensional window of observation which introduces an automated identification of suspicious lesions. The co-occurrence analysis defines 21 different statistical features, a subset of which were inputted to a neural network classifier where the assessments of voxelwise majority of a group of radiologist readings were used as the gold standard. The voxelwise true positive fraction (TPF) and false positive fraction ( FPF) results of the computer classifier were statistically indistinguishable from the TPF and FPF results of the readers using a one sample paired t-test. In order to observe the generality of the method, two different groups of studies were used with widely different image acquisition specifications.