An Enhanced Texture-Based Feature Extraction Approach for Classification of Biomedical Images of CT-Scan of Lungs

Authors

Varun Srivastava
Shilpa Gupta
Gopal Chaudhary
Arun Balodi
Manju Khari
Vicente García-Díaz

DOI

10.9781/ijimai.2020.11.003

Abstract

Content 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.

Source Publication

International Journal of Interactive Multimedia and Artificial Intelligence

Recommended Citation

Srivastava, Varun; Gupta, Shilpa; Chaudhary, Gopal; Balodi, Arun; Khari, Manju; and García-Díaz, Vicente (2021) "An Enhanced Texture-Based Feature Extraction Approach for Classification of Biomedical Images of CT-Scan of Lungs," International Journal of Interactive Multimedia and Artificial Intelligence: Vol. 6: Iss. 7, Article 11.
DOI: 10.9781/ijimai.2020.11.003
Available at: https://ijimai.researchcommons.org/ijimai/vol6/iss7/11