Segmentation of medical images by using k-NN classifier on field programmable logic array (FPGA)

Çinar, SalimKurnaz, Mehmet Nadir2024-11-072024-11-072010978-142449588-7https://hdl.handle.net/11480/109752010 7th National Conference on Electrical, Electronics and Computer Engineering, ELECO 2010 -- 2 December 2010 through 5 December 2010 -- Bursa -- 83834Image segmentation is one of the mostly used procedures in the medical image processing applications. Due to the high resolution characteristic of the medical images and a large amount of computational load in mathematical methods, medical image segmentation process has an excessive computation complexity. Recently, Field Programmable Gate Array (FPGA) implementation capable of performing many complex computations in parallel has been applied in many areas needed for high computation time. In this study, it is proposed that neigbour-pixel-intensity based feature extraction methods for extraction of the textural features in medical images, and k-NN classifier for segmentation process.trinfo:eu-repo/semantics/closedAccessClassifiersElectrical engineeringFeature extractionField programmable gate arrays (FPGA)Medical imagingComplex computationComputation complexityComputation timeComputational loadsFeature extraction methodsField programmable logicField-programmable gate array implementationsHigh resolutionIntensity-basedk-NN classifierMathematical methodMedical Image ProcessingMedical image segmentationMedical imagesSegmentation processTextural featureImage segmentationSegmentation of medical images by using k-NN classifier on field programmable logic array (FPGA)Sahada programlanabilir kapi dizileri (FPGA) üzerinde k-NN siniflayici kullanilarak mr görüntülerin bölütlenmesiConference Object5165202-s2.0-79951665522N/A