An incremental neural network for tissue segmentation in ultrasound images.
This paper presents an incremental neural network (INeN) for the segmentation of tissues in ultrasound images. The performances of the INeN and the Kohonen network are investigated for ultrasound image segmentation. The elements of the feature vectors are individually formed by using discrete Fourier transform (DFT) and discrete cosine transform (DCT). The training set formed from blocks of 4x4 pixels (regions of interest, ROIs) on five different tissues designated by an expert is used for the training of the Kohonen network. The training set of the INeN is formed from randomly selected ROIs of 4x4 pixels in the image. Performances of both 2D-DFT and 2D-DCT are comparatively examined for the segmentation of ultrasound images.
Kurnaz
Mehmet Nadir
MN
Istanbul Technical University, Department of Electronics and Communication Engineering, 34469 Maslak, Istanbul, Turkey. mnkurnaz@itu.edu.tr
Dokur
Zümray
Z
Olmez
Tamer
T
D003238:Connective Tissue / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D016571:Neural Networks, Computer; D014421:Turkey; D014463:Ultrasonography