Global Neighbour Preserving Local Ternary Co-Occurrence Pattern (GNPLTCoP) for Ultrasound Kidney Images Retrieval

Callins Christiyana Chelladurai, Rajamani Vayanaperumal
2015 Indian Journal of Science and Technology  
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:TheproposedGNPLTCoPfeaturefindsthelocalpatternbasedontheco-occurrenceoffirst 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
more » ... 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 comparedwiththeTernaryco-occurrencePattern(LTCoP)asitcomputestheco-occurrenceoffirstorderderivatives. 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 isdifferentfromtheLTCoPbymeansofconsideringthepixelcandidatesofradius2neighbourhoodandaddinginthe 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.
doi:10.17485/ijst/2015/v8i7/62845 fatcat:opsg3pquvzgdtpmuauofo5w42q