Contrast enhancement and tissues classification of breast MRI using Kalman filter-based linear mixing method.
Much attention is currently focused on one of the newest breast examination techniques, breast MRI. Contrast-enhanced breast MRIs acquired by contrast injection have been shown to be very sensitive in the detection of breast cancer, but are also time-consuming and cause waste of medical resources. This paper therefore proposes the use of spectral signature detection technology, the Kalman filter-based linear mixing method (KFLM), which can successfully present the results as high-contrast images and classify breast MRIs into major tissues from four bands of breast MRIs. A series of experiments using phantom and real MRIs was conducted and the results compared with those of the commonly used c-means (CM) method and dynamic contrast-enhanced (DCE) breast MRIs for performance evaluation. After comparison with the CM algorithm and DCE breast MRIs, the experimental results showed that the high-contrast images generated by the spectral signature detection technology, the KFLM, were of superior quality.
Yang
Sheng-Chih
SC
Department of Computer Science and Information Engineering, National Chin Yi University of Technology, Taichung 411, Taiwan, ROC.
Wang
Chuin-Mu
CM
Hsu
Hsian-He
HH
Chung
Pau-Choo
PC
Hsu
Giu-Cheng
GC
Juan
Chun-Jung
CJ
Lo
Chien-Shun
CS
D000465:Algorithms; D001943:Breast Neoplasms / Q000145:classification / Q000175:diagnosis*; D003287:Contrast Media; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods; D019047:Phantoms, Imaging; D012680:Sensitivity and Specificity
D003287:Contrast Media