PT  - JOURNAL ARTICLE
AU  - Nisar Wani
AU  - Khalid Raza
TI  - Multiple Kernel Learning approach for Medical Image Analysis
AID  - 10.1101/121509
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 121509
4099  - http://biorxiv.org/content/early/2017/03/29/121509.short
4100  - http://biorxiv.org/content/early/2017/03/29/121509.full
AB  - 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.BSDBerkely software distributionCADComputer aided diagnosisCTComputed TomographyDWTDiscrete wavelet transformGFBGabor lter bankGLCMGrey level co-occurenece matrixGLRLMGrey level run length matrixHOGHistogram of oriented gradientsKNNK nearest neighborLBPLocal binary patternsLPLinear programmingMIASMammographic Imaging Analysis SocietyMKLMultiple kernel learningMRIMagnetic Resonance ImagingPACSPicture archive communication systemPEIPAPilot European Image Processing ArchivePETPositron Emission TomographyROIRegion of interestSDPSemi-denite programmingSEMScanning electronic microscopeSIFTScale invariant transformSPECTSingle photon Emission Computed TomographySVMSupport vector machineTEMTransmission electronic microscope