SEGMENTATION USING NEURAL NETWORKS

Topics: Neural network, Brain, Unsupervised learning Pages: 7 (1953 words) Published: August 4, 2014
SEGMENTATION WITH NEURAL NETWORK

B.PrasannaRahul 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 thresholding or region growing algorithms, to more sophisticated techniques such as active contours or watershed transformation. Appropriate preprocessing steps comprise the anatomically correct registration of the data sets and masking a region of interest in which the segmentation should be performed. Data analysis may be performed by two different strategies. The first one tries to identify characteristic properties of the multidimensional data distribution of unlabeled feature vectors. This approach is called as unsupervised clustering (UC). The second strategy involves labeled data, i.e., the learning algorithm requires both the feature vector itself and a target function defining its interpretation with regard to segmentation class membership. We call it supervised classification (SC).

Structure and function of the Hyber-BF network:
The general architecture of a
Hyper-BF network is shown in the figure It consists of three layers of neurons: Input layer, hidden layer, and outer layer

n” input neurons with activations xi , i €{1,……,n}, the activation pattern of the input layer is represented by an n-dimensional vector x in the feature space R. This activation is propagated to the N neurons of the hidden layer by directed connections with synaptic weights Wji. The synaptic weights Wj € R, j € {1,……, N}, Are computed as a set of prototypical vectors that represent the data set in the feature space. The activation aj of the hidden layer neuron j is chosen as a function of distance d=||x-wj|| of the data vector x with respect to the virtual position wj of the hidden layer neuron j. d hereby defines an arbitrary metric in the feature space, e.g., the Euclidean metric. The term virtual position is based on the idea that the activation aj of the hidden layer neuron should take its maximum value xmax, which can be looked at as a specification of the neuron j with respect to the position xmax. It is obviously reasonable to choose aj as a...

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.
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