Segmentation of MR images by using grow and learn network on FPGAs
Küçük Resim Yok
Tarih
2013
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Image 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.
Açıklama
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 -- 3 July 2013 through 7 July 2013 -- Osaka -- 100170
Anahtar Kelimeler
Algorithms, Computer Simulation, Electronics, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Software, Feature extraction, Image processing, Magnetic resonance imaging, Medical image processing, Computational loads, FPGA implementations, Fpga platforms, High resolution, Image processing applications, Mathematical method, Parallel processing, Segmentation process, algorithm, computer program, computer simulation, electronics, human, image processing, nuclear magnetic resonance imaging, procedures, Field programmable gate arrays (FPGA)
Kaynak
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
WoS Q Değeri
Scopus Q Değeri
N/A
