Segmentation of MR images by using grow and learn network on FPGAs

Author

Cinar, Salim ; Kurnaz, Mehmet Nadir

Author_Institution

Dept. of Electr. & Electron. Eng., Nigde Univ., Nigde, Turkey

fYear

2013

fDate

3-7 July 2013

Firstpage

4070

Lastpage

4073

Abstract

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.

Keywords

biomedical MRI; biomedical electronics; feature extraction; field programmable gate arrays; image segmentation; learning (artificial intelligence); medical image processing; FPGA platform; GAL network; MR image segmentation; PC platform; computational complexity; feature extraction; field programmable gate array; grow and learn network; magnetic resonance imaging; mathematical method; medical image characteristics; medical image processing application; medical image segmentation process; neighbor-pixel-intensity based method; parallel processing capability; Feature extraction; Field programmable gate arrays; Hardware; Image segmentation; Software; Training; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE

Conference_Location

Osaka

ISSN

1557-170X

Type

conf

DOI

10.1109/EMBC.2013.6610439

Filename

6610439