Local ternary co-occurrence patterns: A new feature descriptor for MRI and CT image retrieval

Authors:
Subrahmanyam Murala;Q. M. Jonathan Wu
Affiliations:
-;-
Venue:
Neurocomputing
Year:
2013

Citing 22
Cited 0

Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults

Journal of Cognitive Neuroscience
Linear Models of Cumulative Distribution Function for Content-based Medical Image Retrieval

Journal of Medical Systems
A comprehensive review of current local features for computer vision

Neurocomputing
Using relevance feedback with short-term memory for content-based spine X-ray image retrieval

Neurocomputing
Rotation invariant texture classification using LBP variance (LBPV) with global matching

Pattern Recognition
A Boosting Framework for Visuality-Preserving Distance Metric Learning and Its Application to Medical Image Retrieval

IEEE Transactions on Pattern Analysis and Machine Intelligence
Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor

IEEE Transactions on Image Processing
Enhanced local texture feature sets for face recognition under difficult lighting conditions

IEEE Transactions on Image Processing
A completed modeling of local binary pattern operator for texture classification

IEEE Transactions on Image Processing
Pyramid binary pattern features for real-time pedestrian detection from infrared videos

Neurocomputing
MultiWaveMed: a system for medical image retrieval through wavelets transformations

CBMS'03 Proceedings of the 16th IEEE conference on Computer-based medical systems
Retrieval by content of medical images using texture for tissue identification

CBMS'03 Proceedings of the 16th IEEE conference on Computer-based medical systems
Neighborhood preserving regression for image retrieval

Neurocomputing
PLBP: An effective local binary patterns texture descriptor with pyramid representation

Pattern Recognition
Evaluation of a Content-Based Retrieval System for Blood Cell Images with Automated Methods

Journal of Medical Systems
Local maximum edge binary patterns: A new descriptor for image retrieval and object tracking

Signal Processing
Discriminative features for texture description

Pattern Recognition
A simple method for improving local binary patterns by considering non-uniform patterns

Pattern Recognition
Shift and gray scale invariant features for palmprint identification using complex directional wavelet and local binary pattern

Neurocomputing
Classification of Benign and Malignant Breast Masses Based on Shape and Texture Features in Sonography Images

Journal of Medical Systems
Directional Binary Wavelet Patterns for Biomedical Image Indexing and Retrieval

Journal of Medical Systems
Moment-based local binary patterns: A novel descriptor for invariant pattern recognition applications

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper presents a novel feature extraction algorithm called local ternary co-occurrence patterns (LTCoP) for biomedical image retrieval. The LTCoP encodes the co-occurrence of similar ternary edges which are calculated based on the gray values of center pixel and its surrounding neighbors. Whereas the standard local derivative pattern (LDP) encodes the co-occurrence between the first-order derivatives in a specific direction. The existing LDP is a specific direction rotational variant feature where as our method is rotational invariant. In addition, the effectiveness of our algorithm is confirmed by combining it with the Gabor transform. To prove the effectiveness of our algorithm, three experiments have been carried out on three different biomedical image databases. Out of which two are meant for computer tomography (CT) and one for magnetic resonance (MR) image retrieval. It is further mentioned that the database considered for three experiments are OASIS-MRI database, NEMA-CT database and VIA/I-ELCAP database which includes region of interest CT images. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LBP, LTP, local tetra patterns (LTrP) and LDP with and without Gabor transform.