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

  1. Varun Srivastava 1
  2. Shilpa Gupta 1
  3. Gopal Chaudhary 1
  4. Arun Balodi 2
  5. Manju Khari 3
  6. Vicente García-Díaz 4
  1. 1 Bharati Vidyapeeth’s College of Engineering, Paschim Vihar, New Delhi
  2. 2 Atria Institute of Technology, Bengaluru, Karnataka
  3. 3 Netaji Subhas University of Technology, East Campus, Delhi
  4. 4 Universidad de Oviedo
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    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

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Revista:
IJIMAI

ISSN: 1989-1660

Año de publicación: 2021

Volumen: 6

Número: 7

Páginas: 18-25

Tipo: Artículo

DOI: 10.9781/IJIMAI.2020.11.003 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: IJIMAI

Resumen

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.

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Fuente de los datos: Dialnet