01530nas a2200265   4500000000100000000000100001008004100002260001200043653002500055653004100080653001300121653002600134100002100160700001700181700002000198700001600218700001600234700002600250245011800276856007900394300001000473490000600483520076100489022001401250       2021                            d  c09/202110aImage Classification10aLocal Mesh Peak Valley Edge Patterns10aPatterns10aInformation Retrieval1 aVarun Srivastava1 aShilpa Gupta1 aGopal Chaudhary1 aArun Balodi1 aManju Khari1 aVicente García-Díaz00aAn Enhanced Texture-Based Feature Extraction Approach for Classification of Biomedical Images of CT-Scan of Lungs  uhttps://www.ijimai.org/journal/sites/default/files/2021-08/ijimai6_7_2.pdf  a18-250 v63 aContent Based Image Retrieval (CBIR) techniques based on texture have gained a lot of popularity in recent times. In the proposed work, a feature vector is obtained by concatenation of features extracted from local mesh peak valley edge pattern (LMePVEP) technique; a dynamic threshold based local mesh ternary pattern technique and texture of the image in five different directions. The concatenated feature vector is then used to classify images of two datasets viz. Emphysema dataset and Early Lung Cancer Action Program (ELCAP) lung database. The proposed framework has improved the accuracy by 12.56%, 9.71% and 7.01% in average for data set 1 and 9.37%, 8.99% and 7.63% in average for dataset 2 over three popular algorithms used for image retrieval.  a1989-1660