Open AccessArtificial Bee Colony Optimization Based Despeckling Framework for Ultrasound Images
Author(s) -
Pradeep Kumar Gupta,
Shyam Lal,
Farooq Husain
Publication year - 2020
Publication title -
journal of engineering science and technology review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.19
H-Index - 28
eISSN - 1791-9320
pISSN - 1791-2377
DOI - 10.25103/jestr.135.04
Subject(s) - mean squared error , artificial intelligence , speckle noise , speckle pattern , ultrasound , filter (signal processing) , computer science , peak signal to noise ratio , benchmark (surveying) , pattern recognition (psychology) , computer vision , mathematics , image (mathematics) , radiology , medicine , statistics , geodesy , geography
This paper proposed an artificial bee colony optimization (ABC) algorithm based despeckling framework to overcome the effect of speckle noise present in real ultrasound images. A low pass filter and fast non-local mean filter along with Artificial Bee Colony (ABC) optimization algorithm are used for the quality enhancement of ultrasound images. The output results obtained for the real ultrasound images filtered with the proposed approach and the other most studied approaches discussed in the literature. The outperformance of the proposed method is verified by calculation of peak signal to noise ratio (PSNR), mean square error (MSE), mean absolute error (MAE), and structure similarity index (SSIM) quality measures. The proposed filtering approach is tested on eight real clinical ultrasound images of adrenal gland, appendicitis, bladder, pancreas, parathyroid gland, scrotal gland, thoracic wall, and uterus. The experimental results yield that the quantitative and qualitative results of the proposed framework are better than benchmark despeckling methods compared to real ultrasound images. Further, the proposed framework also preserves the fine details in real ultrasound images.