TY - JOUR
T1 - Medical image segmentation with transform and moment based features and incremental supervised neural network
AU - Iscan, Zafer
AU - Yüksel, Ayhan
AU - Dokur, Zümray
AU - Korürek, Mehmet
AU - Ölmez, Tamer
PY - 2009/9
Y1 - 2009/9
N2 - In this study, a novel incremental supervised neural network (ISNN) is proposed for the segmentation of medical images. Performance of the ISNN is investigated for tissue segmentation in medical images obtained from various imaging modalities. Two feature extraction methods based on transform and moments are comparatively investigated to segment the tissues in medical images. Two-dimensional (2D) continuous wavelet transform (CWT) and the moments of the gray-level histogram (MGH) are computed in order to form the feature vectors of ultrasound (US) bladder and phantom images, X-ray computerized tomography (CT) and magnetic resonance (MR) head images. In the 2D-CWT method, feature vectors are formed by the intensity of one pixel of each wavelet-plane of different energy bands. The MGH represents the tissues within the sub-windows by using the spatial variation of image intensities. In this study, the ISNN and Grow and Learn (GAL) network are employed for the segmentation task. It is observed that the ISNN has significantly eliminated the disadvantages of the GAL network in the segmentation of the medical images.
AB - In this study, a novel incremental supervised neural network (ISNN) is proposed for the segmentation of medical images. Performance of the ISNN is investigated for tissue segmentation in medical images obtained from various imaging modalities. Two feature extraction methods based on transform and moments are comparatively investigated to segment the tissues in medical images. Two-dimensional (2D) continuous wavelet transform (CWT) and the moments of the gray-level histogram (MGH) are computed in order to form the feature vectors of ultrasound (US) bladder and phantom images, X-ray computerized tomography (CT) and magnetic resonance (MR) head images. In the 2D-CWT method, feature vectors are formed by the intensity of one pixel of each wavelet-plane of different energy bands. The MGH represents the tissues within the sub-windows by using the spatial variation of image intensities. In this study, the ISNN and Grow and Learn (GAL) network are employed for the segmentation task. It is observed that the ISNN has significantly eliminated the disadvantages of the GAL network in the segmentation of the medical images.
KW - Continuous wavelet transform
KW - Incremental neural networks
KW - Medical image segmentation
KW - Statistical moments
KW - Supervised learning
UR - http://www.scopus.com/inward/record.url?scp=67349146823&partnerID=8YFLogxK
U2 - 10.1016/j.dsp.2009.03.001
DO - 10.1016/j.dsp.2009.03.001
M3 - Article
AN - SCOPUS:67349146823
SN - 1051-2004
VL - 19
SP - 890
EP - 901
JO - Digital Signal Processing: A Review Journal
JF - Digital Signal Processing: A Review Journal
IS - 5
ER -