Unsupervised medical image classification by combining case-based classifiers.
We introduce an automated pathology classification system for medical volumetric brain image slices. Existing work often relies on handcrafted features extracted from automatic image segmentation. This is not only a challenging and time-consuming process, but it may also limit the adaptability and robustness of the system. We propose a novel approach to combine sparse Gabor-feature based classifiers in an ensemble classification framework. The unsupervised nature of this non-parametric technique can significantly reduce the time and effort for system calibration. In particular, classification of medical images in this framework does not rely on segmentation, nor semantic-based or annotation-based feature selection. Our experiments show very promising results in classifying computer tomography image slices into pathological classes for traumatic brain injury patients.
School of Computing, National University of Singapore, Singapore.
Thien Anh
TA
Dinh
Tomi
T
Silander
Bolan
B
Su
Tianxia
T
Gong
Boon Chuan
BC
Pang
C C Tchoyoson
CC
Lim
Cheng Kiang
CK
Lee
Chew Lim
CL
Tan
Tze-Yun
TY
Leong
D000465:Algorithms; D001185:Artificial Intelligence; D001930:Brain Injuries / Q000000981:diagnostic imaging*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*