RT Journal Article
SR Electronic
T1 Multiple Kernel Learning approach for Medical Image Analysis
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 121509
DO 10.1101/121509
A1 Nisar Wani
A1 Khalid Raza
YR 2017
UL http://biorxiv.org/content/early/2017/03/29/121509.abstract
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