Proceedings of International Conference on Advances in Computing

This is the first International Conference on Advances in Computing (ICAdC-2012). The scope of the conference includes all the areas of Theoretical Computer Science, Systems and Software, and Intelligent Systems. Conference Proceedings is a culmination of research results, papers and the theory related to all the three major areas of computing, i.e., Theoretical Computer Science, Systems and Software, and Intelligent Systems. Helps budding researchers, graduates in the areas of Computer Science, Information science, Electronics, Telecommunication, Instrumentation, Networking to take forward their research work based on the reviewed results in the paper,  by mutual interaction through E-mail contacts in the proceedings

3D Reconstruction of Solid Breast Nodule in Ultra Sonographic Image

Breast cancer is one of the most frequent forms of cancer among women all over the world and the early detection of the cancer provides a better chance of proper treatment. A method for automatically reconstructing a 3-dimensional object (nodule) from serial cross sectional Ultra Sonographic data is presented in this paper. The segmentation of region of interest (ROI) is the most crucial step in 3dimensional nodule construction for which Fuzzy Stopping Force Level Sets equation is employed [11]. The method combines the Dynamic Elastic Contour Interpolation algorithm [22] to generate a series of intermediate missing contours between each pair of consecutive cross sections. These slices are connected in a definite order and rendered using Phong shading for smooth and complete surface of 3D nodule can thus be reconstructed

Color Image Restoration Using Neural Network Model

Abstract: Neural network learning approach for color image restoration has been discussed in this paper and one of the possible solutions for restoring images has been presented. Here neural network weights are considered as regularization parameter values instead of explicitly specifying them. The weights are modified during the training through the supply of training set data. The desired response of the network is in the form of estimated value of the current pixel. This estimated value is used to modify the network weights such that the restored value produced by the network for a pixel is as close as to this desired response. One of the advantages of the proposed approach is that, once the neural network is trained, images can be restored without having prior information about the model of noise/blurring with which the image is corrupted

Identification of brain regions responsible for Alzheimer’s disease using a self-adaptive resource allocation network

In this paper, we present a novel approach for the identification of brain regions responsible for Alzheimer’s disease using the Magnetic Resonance (MR) images. The approach incorporates the recently developed Self-adaptive Resource Allocation Network (SRAN) for Alzheimer’s disease classification using voxel-based morphometric features of MR images. SRAN classifier uses a sequential learning algorithm, employing self-adaptive thresholds to select the appropriate training samples and discard redundant samples to prevent over-training. These selected training samples are then used to evolve the network architecture efficiently. Since, the number of features extracted from the MR images is large, a feature selection scheme (to reduce the number of features needed) using an Integer-Coded Genetic Algorithm (ICGA) in conjunction with the SRAN classifier (referred to here as the ICGA–SRAN classifier) have been developed. In this study, different healthy/Alzheimer’s disease patient’s MR images from the Open Access Series of Imaging Studies data set have been used for the performance evaluation of the proposed ICGA–SRAN classifier. We have also compared the results of the ICGA–SRAN classifier with the well-known Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers. The study results clearly show that the ICGA–SRAN classifier produces a better generalization performance with a smaller number of features, lower misclassification rate and a compact network. The ICGA–SRAN selected features clearly indicate that the variations in the gray matter volume in the parahippocampal gyrus and amygdala brain regions may be good indicators of the onset of Alzheimer’s disease in normal persons