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 dynamic, contrast enhanced magnetic resonance parameter 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 input to a neural network classifier where the assessments of the 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.

Autorentext

Dr. Mehmet Cemil Kale received the B.S. degree in electrical and electronics engineering from the Anadolu University in 2002 and the M.S. and Ph. D. degrees in electrical engineering from The Ohio State University in 2004 and 2008, respectively. From 2004 to 2007, he worked in the Department of Radiology at The Ohio State University Medical Center.