Classification of Wireless Endoscopy Image based on the LBP & CLEOP Texture Pattern

  • E. Niranjan, Dr. P. Dayananda


Automation in medical image processing has enhanced the prediction of affected layer from the source of image. In that, image analysis in endoscopic medical field was focused for high level prediction and to estimate proper and better treatment for patients at earlier stage with less amount of stress. There are several methods of image analysis to extract the features of the image and predict its category. For better prediction model, the features of that image should be in rotational invariant to find the best match training set. To improve the feature extraction in image processing application, texture patterns performed with better efficiency to analyze an image. In this proposed work, a novel method of texture pattern analysis method to classify the endoscopic image. This was achieved by using the Convoluted Local Energy Oriented Pattern (CLEOP) combined with Local Binary Pattern (LBP) based feature extraction method. For validating the performance of proposed work, the CVC-ClinicDB image database was used in the testing process. In that, the database was separated into two major types based on the level of affected tissue region and its size. The efficiency of CLEOP texture pattern extraction method is compared with other state-of-the-art methods of feature extraction and classification model. This can be justified by estimating the statistical parameters estimated by validating the ground-truth of image database.

How to Cite
E. Niranjan, Dr. P. Dayananda. (2021). Classification of Wireless Endoscopy Image based on the LBP & CLEOP Texture Pattern. Design Engineering, 555-563. Retrieved from