HESCHL'S GYRUS AUDITORY CORTEXSLICE REGISTRATION USINGECHO STATE NEURAL NETWORK (ESNN)
Research Scholar,Department of Computer ScienceMother Theresa Women’s University,Kodaikanal, India.Email:email@example.com
Dean, Information Technology,Arupadai Veedu Institute of TechnologyPaiyanoor-603104, India.E-mail: <firstname.lastname@example.org>,
This paper presents Herschel’s gyrusauditory cortex slice registration using Echo stateneural network (ESNN). Training the network is donewith translation and rotational values of the selectivepoints (feature points) from two images at a time(source and target images). The input layer is given withcoordinates of the selective points of the source imageand in the output layer; the labeling is the translationand rotational values of the selective points of the targetimage. ESNN is an estimation network which estimatesthe required registration information from the selectivepoints of target and source image. The output of ESNNis compared with radial basis function (RBF).
Keywords-Echo state neural network, functional magnetic resonance imaging (fMRI), Heschl's gyrus,auditory cortex
The image registration  aims to find atransformation that aligns images of the same scenetaken at different times, from different viewpoints. Ithas been studied in various contexts due to itssignificance in a wide range of areas, includingmedical image fusion, remote sensing, and computervision. Medical image acquisition systems generatedigital images that can be processed by a computerand transferred over computer networks. Digitalimaging allows extracting objective, quantitativeparameters from the images by image analysis.Medical image analysis exploits the numericalrepresentation of digital images to develop imageprocessing techniques that facilitate computer-aidedinterpretation of medical images. The continuingadvancement of image acquisition technology and theresulting improvement of radiological image qualityhave led to an increasing clinical need andphysician’s demand for quantitative imageinterpretation in routine practice, imposing new andmore challenging requirements for medical imageanalysis. A fundamental problem in medicalimage analysis is the integration of information frommultiple images of the same subject, acquired usingthe same or different imaging modalities and possiblyat different time points. One essential aspect thereof is image registration, i.e., recovering the geometricbetween corresponding points in multiple images of the same scene. While various more or lessautomated approaches for image registration havebeen proposed in the field of medical imaging andimage analysis, one strategy in particular, namelymaximization of mutual information , has beenextremely successful at automatically computing theregistration of 3-D multimodal medical images of various organs from the image content itself.
MATERIALS AND METHODS
Neural Network Structures
The Echo state neural network is used forlearning the images. The number of neurons in theinput layer is 4, and the number of neurons in theoutput layer is 6.
Input layer description
Node 1 = x coordinate of point in image 2(targetimage)Node 2 = y coordinate of point in image 2(targetimage)Node 3 = x coordinate of point in image 1(imageto be registered with target image)Node 4 = y coordinate of point in image 1(imageto be registered with target image)
Output layer description
Node 1= vertical shift
(IJCSIS) International Journal of Computer Science and Information Security,Vol. 9, No. 2, February 2011204 http://sites.google.com/site/ijcsis/ISSN 1947-5500