Indian Journal of Science and Technology
DOI: 10.17485/ijst/2015/v8i7/62845
Year: 2015, Volume: 8, Issue: 7, Pages: 614–621
Original Article
Callins Christiyana Chelladurai 1*and Rajamani Vayanaperumal 2
1 Department of Computer Science and Engineering, Sethu Institute of Technology, Madurai, Tamilnadu, India; callinschristiyana_c@yahoo.co.in
2 Department of Electronics and Communication Engineering, Veltech Multitech Dr. Rangarajan Dr. Sakunthaladevi Engineering College, Chennai, Tamilnadu, India; rajavmani@gmail.com
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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