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Callins Christiyana Chelladurai ¹, Rajamani Vayanaperumal ²

Affiliations
1 Department of Computer Science and Engineering, Sethu Institute of Technology, Madurai, Tamilnadu, IN
2 Department of Electronics and Communication Engineering, Veltech Multitech Dr. Rangarajan Dr. Sakunthaladevi Engineering College, Chennai, Tamilnadu

Abstract

Objectives: This work proposes a feature extraction procedure named as Global Neighbour Preserving Local Ternary Co-occurrence Pattern (GNPLTCoP) in the Content Based Image Retrieval (CBIR) Task for ultrasound kidney images retrieval. Methods/Analysis: The proposed GNPLTCoP feature finds the local pattern based on the co-occurrence of first order derivatives in ternary fashion from radius 1 and radius 2 neighbourhoods of the center pixel in a small 3X3 square region. Then the pattern is classified into two in order to integrate the global information. The classification is based on the correlation between the global and local region mean intensities. Findings: The Performance of GNPLTCoP is compared with the Ternary co-occurrence Pattern (LTCoP) as it computes the co-occurrence of first order derivatives. The LTCoP considers the co-occurrence of 8 pixels in the radius 1 neighbourhood with the 8 pixel candidates in the radius 2 neighbourhood. Among the 16 pixels in the radius 2 neighbourhood, 8 pixels are used and 8 pixels information are left in the LTCoP computation. This problem is addressed in the computation of GNPLTCoP. The proposed GNPLTCoP feature is different from the LTCoP by means of considering the pixel candidates of radius 2 neighbourhood and adding in the global information. The pixel candidates of radius 2 neighbourhood are replaced by the mean value of itself and its two neighbours to preserve the neighbour pixels information. This work employs GNPLTCoP feature as a feature extraction procedure in image retrieval system consists of database of ultrasound kidney images. The performance of GNPLTCoP is compared with LTP and LTCoP. The discriminative power of GNPLTCoP is substantiated through Precision and Recall measures. Conclusion/Application: The proposed GNPLTCoP can be applied as the feature extraction procedure for other types of medical images and pattern recognition applications.

Keywords

CBIR, GNPLTCoP, Local Patterns, LTCoP, Texture, Ultrasound Kidney Images.

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D. Udaya Suriya Raj Kumar ¹, Rajamani Vayanaperumal ²

Affiliations
1 Department of Computer Science and Engineering, Sathyabama University, Chennai - 600119, Tamil Nadu, IN
2 Department of Electronic and Communication Engineering, Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College, Avadi, Chennai 600062, Tamil Nadu, IN

Abstract

Objectives: The main aim of this paper is security by applying Intelligent Intrusion detection System to detect one of the critical attack know as sinkhole attack in Wireless Sensor network. Methods/Analysis: The major problem occurs while transmitting the data is security. The malicious node attracts the packets from the other normal sensor nodes and drops the sensitive packets which may leads to selective forwarding attack and black hole attack. Findings: In the existing system, Leader based intrusion detection system is used to monitor the surrounding node. Here the leader can monitor the nodes only region wise and does not have any clustering technique. By using the clustering technique we can monitor the surrounding nodes in efficient manner and better solution. In the proposed system, an Intelligent Intrusion Detection System (IIDS) mechanism to detect the intruder in the network, which uses a Hybrid Approach to detect and prevent sinkhole attack, with two approaches such as Zone-Based Leader Election Method and Leader Based Monitoring. In Zone-Based Leader Election method, a set of nodes is located in different regions and a Cluster Based Zone Leader (CBZL) is allocated to every region in the network. In the Leader Based Monitoring Approach, when a node gets detected as a compromised node, it communicates the status of that node to the other leader within the WSN. Application/Improvement: This technique can identify the intruder node and improve the efficiency of the network. As a simulation results, we are implementing in Network Simulator. In environment monitoring and military applications the proposed approach can be deployed.

Keywords

Cluster Zone-based Leader, Leader based Monitoring Approach, Sinkhole Node, Wireless Sensor Network

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