Cinar, SalimKurnaz, Mehmet Nadir2024-11-072024-11-072013978-145770216-71557-170Xhttps://doi.org/10.1109/EMBC.2013.6610439https://hdl.handle.net/11480/109842013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 -- 3 July 2013 through 7 July 2013 -- Osaka -- 100170Image segmentation is one of the mostly used procedures in the medical image processing applications. Due to the high resolution characteristics of the medical images and a large amount of computational load in mathematical methods, medical image segmentation process has an excessive computational complexity. Recently, FPGA implementation has been applied in many areas due to its parallel processing capability. In this study, neighbor-pixel-intensity based method for feature extraction and Grow and Learn (GAL) network for segmentation process are proposed. The proposed method is comparatively examined on both PC and FPGA platforms. © 2013 IEEE.eninfo:eu-repo/semantics/closedAccessAlgorithmsComputer SimulationElectronicsHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingSoftwareFeature extractionImage processingMagnetic resonance imagingMedical image processingComputational loadsFPGA implementationsFpga platformsHigh resolutionImage processing applicationsMathematical methodParallel processingSegmentation processalgorithmcomputer programcomputer simulationelectronicshumanimage processingnuclear magnetic resonance imagingproceduresField programmable gate arrays (FPGA)Conference Object4070407310.1109/EMBC.2013.6610439241106262-s2.0-84886457836N/A