Image Retrieval Based on Local Mesh Vector Co-occurrence Pattern for Medical Diagnosis from MRI Brain Images

In modern health-care, for evidence-based diagnosis, there is a requirement for an efficient image retrieval approach to retrieve the cases of interest that have similar characteristics from the large image databases. This paper presents a feature extraction approach that aims at extracting texture features present in the medical images using Local Pattern Descriptor (LPD) and Gray-level Co-occurrence Matrix (GLCM). As a main contribution, a novel local pattern named Local Mesh Vector Co-occurrence Pattern (LMVCoP) has been proposed by concatenating the Local Mesh Co-occurrence Pattern (LMCoP) and the Local Vector Co-occurrence Pattern (LVCoP). The fusion of GLCM with the Local Mesh Pattern (LMeP) and the Local Vector Pattern (LVP) produces LMCoP and LVCoP respectively. The LMVCoP method has been investigated on the Open Access Series of Imaging Studies (OASIS): a Magnetic Resonance Imaging (MRI) brain image database. LMVCoP descriptor achieves 87.57% of ARP and 53.21% of ARR which are higher than the existing methods of LTCoP, PVEP, LBDP, LMeP and LVP. The LMVCoP method enhances the retrieval results of LMeP/LVP from 81.36%/83.52% to 87.57% in terms of ARP on OASIS MRI brain database.

[1]  Kuo-Chin Fan,et al.  A Novel Local Pattern Descriptor—Local Vector Pattern in High-Order Derivative Space for Face Recognition , 2014, IEEE Transactions on Image Processing.

[2]  R. Shantha Selva Kumari,et al.  Spatial Fuzzy C Means and Expectation Maximization Algorithms with Bias Correction for Segmentation of MR Brain Images , 2016, Journal of Medical Systems.

[3]  Nassir Navab,et al.  AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images , 2016, IEEE Trans. Medical Imaging.

[4]  Nicholas Ayache,et al.  Medical Image Analysis: Progress over Two Decades and the Challenges Ahead , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Subrahmanyam Murala,et al.  Peak Valley Edge Patterns: A New Descriptor for Biomedical Image Indexing and Retrieval , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[6]  Degui Xiao,et al.  Medical Image Retrieval: A Multimodal Approach , 2014, Cancer informatics.

[7]  Ying Zhang,et al.  Notice of Violation of IEEE Publication PrinciplesBag-of-Features Based Medical Image Retrieval via Multiple Assignment and Visual Words Weighting , 2011, IEEE Transactions on Medical Imaging.

[8]  L. Rodney Long,et al.  Multi-modal Query Expansion Based on Local Analysis for Medical Image Retrieval , 2009, MCBR-CDS.

[9]  Hamid R. Tizhoosh,et al.  Medical Image Classification via SVM Using LBP Features from Saliency-Based Folded Data , 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).

[10]  Subrahmanyam Murala,et al.  Directional Binary Wavelet Patterns for Biomedical Image Indexing and Retrieval , 2012, Journal of Medical Systems.

[11]  Shadi Albarqouni,et al.  AggNet : Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images , 2016 .

[12]  Hamid R. Tizhoosh,et al.  Generating binary tags for fast medical image retrieval based on convolutional nets and Radon Transform , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[13]  Haralampos Karanikas,et al.  A Pattern Similarity Scheme for Medical Image Retrieval , 2009, IEEE Transactions on Information Technology in Biomedicine.

[14]  Subrahmanyam Murala,et al.  Local Mesh Patterns Versus Local Binary Patterns: Biomedical Image Indexing and Retrieval , 2014, IEEE Journal of Biomedical and Health Informatics.

[15]  R. Ravi,et al.  Local Mesh Co-Occurrence Pattern for Content Based Image Retrieval , 2015 .

[16]  S. Govindaraju,et al.  A Novel Content Based Medical Image Retrieval using SURF Features , 2016 .

[17]  John G. Csernansky,et al.  Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults , 2007, Journal of Cognitive Neuroscience.

[18]  Nilanjan Dey,et al.  Decision Making Based on Fuzzy Aggregation Operators for Medical Diagnosis from Dental X-ray images , 2016, Journal of Medical Systems.

[19]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[20]  Subrahmanyam Murala,et al.  Local Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval , 2012, IEEE Transactions on Image Processing.

[21]  Gwénolé Quellec,et al.  Case Retrieval in Medical Databases by Fusing Heterogeneous Information , 2015, IEEE Transactions on Medical Imaging.

[22]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.