International Journal of Mathematical Sciences and Computing (IJMSC)

IJMSC Vol. 1, No. 2, Aug. 2015

Cover page and Table of Contents: PDF (size: 175KB)

Table Of Contents


An Efficient Impulse Noise Removal Image Denoising Technique for MRI Brain Images

By Murugan Balasubramanian

DOI:, Pub. Date: 8 Aug. 2015

Image enhancement is an important challenge in medical field. There are various techniques for image enhancement during last two decades. The objective of this paper is to remove impulse noise for MRI brain image. This paper proposed an efficient filter for removing impulse noise. The shape of the filter is changed to diamond. Experiments are conducted for various noise levels. The proposed method is compared with the existing Denoising techniques. The experimental results proved that the proposed filter performed well than the other methods.

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Analysis of Signalling Time of Community Model

By Boudhayan Bhattacharya Banani Saha

DOI:, Pub. Date: 8 Aug. 2015

Data fusion is generally defined as the application of methods that combines data from multiple sources and collect information in order to get conclusions. This paper analyzes the signalling time of different data fusion filter models available in the literature with the new community model. The signalling time is calculated based on the data transmission time and processing delay. These parameters reduce the signalling burden on master fusion filter and improves throughput. A comparison of signalling time of the existing data fusion models along with the new community model has also been presented in this paper. The results show that our community model incurs improvement with respect to the existing models in terms of signalling time. 

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Clustering of Multi Scripts Isolated Characters Using k-Means Algorithm

By Neeru Garg Munish Kumar

DOI:, Pub. Date: 8 Aug. 2015

The aim of this paper is script identification problem of handwritten text which facilitates the clustering of data according to their type of script. In this paper, collection of different types of handwritten text document i.e. Devanagari, Gurumukhi and Roman is taken as input and then cluster of all these documents according to script type whether i.e. Devanagari, Gurumukhi, or Roman was prepared. Clustering of handwritten multi-script document scheme proposed in this paper is divided into two phases. First phase used to extract the features of given text images. In the second phase, features extracted in the previous phase were used for clustering with k-Means algorithm. In feature extraction phase, we have extracted four types of features, namely, circular curvature feature, horizontal stroke density feature, pixel density feature value and zoning based feature. In this study, we have considered 4,850 samples of isolated characters of Devanagari, Gurumukhi and Roman script.

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