SEGMENTATION USING NEURAL NETWORKS
SEGMENTATION WITH NEURAL NETWORK
B.Prasanna Rahul Radhakrishnan
Valliammai Engineering College Valliammai Engineering College prakrish_2001@yahoo.com krish_rahul_1812@yahoo.com
Abstract: Our paper work is on Segmentation by Neural networks. Neural networks computation offers a wide range of different algorithms for both unsupervised clustering (UC) and supervised classification (SC). In this paper we approached an algorithmic method that aims to combine UC and SC, where the information obtained during UC is not discarded, but is used as an initial step toward subsequent SC. Thus, the power of both image analysis strategies can be combined in an integrative computational procedure. This is achieved by applying “Hyper-BF network”. Here we worked a different procedures for the training, preprocessing and vector quantization in the application to medical image segmentation and also present the segmentation results for multispectral 3D MRI data sets of the human brain with respect to the tissue classes “ Gray matter”, “ White matter” and “ Cerebrospinal fluid”. We correlate manual and semi automatic methods with the results.
Keywords: Image analysis, Hebbian learning rule, Euclidean metric, multi spectral image segmentation, contour tracing.
Introduction:
Segmentation can be defined as the identification of meaningful image components. It is a fundamental task in image processing providing the basis for any kind of further highlevel image analysis. In medical image processing, a wide range of application is based on segmentation. A possible realization of high-level image analysis principle is the acquisition and processing of multisprectral image data sets, which forms the basis of the segmentation approach. A good survey is provided by the list of citations published in [1] that may serve as a good starting point for further reference. Different segmentation methods range from simple histogram-based
B.Prasanna Rahul Radhakrishnan
Valliammai Engineering College Valliammai Engineering College prakrish_2001@yahoo.com krish_rahul_1812@yahoo.com
Abstract: Our paper work is on Segmentation by Neural networks. Neural networks computation offers a wide range of different algorithms for both unsupervised clustering (UC) and supervised classification (SC). In this paper we approached an algorithmic method that aims to combine UC and SC, where the information obtained during UC is not discarded, but is used as an initial step toward subsequent SC. Thus, the power of both image analysis strategies can be combined in an integrative computational procedure. This is achieved by applying “Hyper-BF network”. Here we worked a different procedures for the training, preprocessing and vector quantization in the application to medical image segmentation and also present the segmentation results for multispectral 3D MRI data sets of the human brain with respect to the tissue classes “ Gray matter”, “ White matter” and “ Cerebrospinal fluid”. We correlate manual and semi automatic methods with the results.
Keywords: Image analysis, Hebbian learning rule, Euclidean metric, multi spectral image segmentation, contour tracing.
Introduction:
Segmentation can be defined as the identification of meaningful image components. It is a fundamental task in image processing providing the basis for any kind of further highlevel image analysis. In medical image processing, a wide range of application is based on segmentation. A possible realization of high-level image analysis principle is the acquisition and processing of multisprectral image data sets, which forms the basis of the segmentation approach. A good survey is provided by the list of citations published in [1] that may serve as a good starting point for further reference. Different segmentation methods range from simple histogram-based
References: 1.A.J.Worth . http://demonmac.mgh.harvard.edu/cma/seg_f/seg_refs.html. 2.F.Girosi and T.Poggio, Networks and the best approximation property. A.I.Memo 1164,Massachusetts Institute of Technology, 10,1989. 3.J.Moody and C.Darken. Fast learning in networks of locally tuned processing units. 4. F.Rosenblatt.Principles of neurometrics. Spartans books,1962. 5.Y.Linde,A.Buzo,and R.M.Gray . An algorithm for vector quantizer design. 6.D.O.Hebb.The organization of behaviour.