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

被引:5
|
作者
Srivastava, Varun [1 ]
Purwar, Ravindra [1 ]
机构
[1] Guru Gobind Singh Indraprastha Univ, New Delhi, India
关键词
Image retrieval; Local Mesh Peak Valley Edge Pattern (LMePVEP); Local binary pattern (LBP); Directional local ternary quantized extrema pattern (DLTerQEP);
D O I
10.14201/ADCAIJ2018717789
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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 trema pattern using to 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 an improvement up to 19% in terms of precision and retrieval for the proposed enhanced LMePVEP technique over conventional LMePVEP approach in Wiki cancer data set.
引用
收藏
页码:77 / 89
页数:13
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