Multispectral co-occurrence with three random variables in dynamic contrast enhanced magnetic resonance imaging of breast cancer.
Presented is a new computer-aided multispectral image processing method which is used in three spatial dimensions and one spectral dimension where the dynamic, contrast enhanced magnetic resonance parameter maps derived from voxel-wise model-fitting represent the spectral dimension. The method is based on co-occurrence analysis using a 3-D 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 voxel-wise majority of a group of radiologist readings were used as the gold standard. The voxel-wise 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.
Kale
Mehmet C
MC
Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA.
Clymer
Bradley D
BD
Koch
Regina M
RM
Heverhagen
Johannes T
JT
Sammet
Steffen
S
Stevens
Robert
R
Knopp
Michael V
MV
D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000175:diagnosis*; D003287:Contrast Media; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D003287:Contrast Media