An extension of local mesh peak valley edge based feature descriptor for image retrieval in bio-medical images

• Published in: Advances in distributed computing and artificial intelligence journal Vol. 7; no. 1; pp. 77 - 89
• Main Authors: Srivastava, Varun, Purwar, Ravindra
• Format: Journal Article
• Language: English
• Published: Salamanca Ediciones Universidad de Salamanca 01.01.2018
• Subjects: Brain; Cancer; directional local ter-nary quantized ex-trema pattern (dlterqep); Image management; Image processing; Image retrieval; Indexing; lo-cal mesh peak valley edge pattern (lmepvep); local binary pattern (lbp); Lungs; Medical imaging; Pixels
• ISSN: 2255-2863; 2255-2863
• DOI: 10.14201/ADCAIJ2018717789

Various texture based approaches have been proposed for image indexing in bio-medical image processing and a precise description of image for indexing in bio-medical image database has always been a challenging task. In this paper, an extension of local mesh peak valley edge pattern (LMePVEP) has been proposed and its effectiveness is experimentally justified. The proposed algorithm explores the relationship of center pixel with the surrounding ones along with the relationship of pixels amongst each other in five different directions. It is then compared with the original LMePVEP as well as a directional local ternary quantized extrema pattern (DLTerQEP) based approach using two bench mark databases viz. ELCAP database for lungs and Wiki cancer data set for thyroid cancer. Further a live dataset for brain tumor is also used for experimental evaluation. The experimental results show that an average improvement of 11.16% in terms of average retrieval rate (ARR) and 5.37% in terms of average retrieval precision (ARP) is observed for proposed enhanced LMePVEP over conventional LMePVEP.