ABSTRACT
Computer aided diagnosis is gradually making its way into the domain of medical research and clinical diagnosis. With field of radiology and diagnostic imaging producing petabytes of image data. Machine learning tools, particularly kernel based algorithms seem to be an obvious choice to process and analyze this high dimensional and heterogeneous data. In this chapter, after presenting a breif description about nature of medical images, image features and basics in machine learning and kernel methods, we present the application of multiple kernel learning algorithms for medical image analysis.
ABBREVIATIONS
- BSD
- Berkely software distribution
- CAD
- Computer aided diagnosis
- CT
- Computed Tomography
- DWT
- Discrete wavelet transform
- GFB
- Gabor lter bank
- GLCM
- Grey level co-occurenece matrix
- GLRLM
- Grey level run length matrix
- HOG
- Histogram of oriented gradients
- KNN
- K nearest neighbor
- LBP
- Local binary patterns
- LP
- Linear programming
- MIAS
- Mammographic Imaging Analysis Society
- MKL
- Multiple kernel learning
- MRI
- Magnetic Resonance Imaging
- PACS
- Picture archive communication system
- PEIPA
- Pilot European Image Processing Archive
- PET
- Positron Emission Tomography
- ROI
- Region of interest
- SDP
- Semi-denite programming
- SEM
- Scanning electronic microscope
- SIFT
- Scale invariant transform
- SPECT
- Single photon Emission Computed Tomography
- SVM
- Support vector machine
- TEM
- Transmission electronic microscope
Copyright
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