Assessment of despeckle filtering algorithms for segmentation of breast tumours from ultrasound images

Publication date: Available online 12 October 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Kriti, Jitendra Virmani, Ravinder AgarwalAbstractIn the present work, the breast ultrasound images are pre-processed with various despeckle filtering algorithms to analyze the effect of despeckling on segmentation of benign and malignant breast tumours from ultrasound images. The despeckle filtering algorithms are broadly classified into eight categories namely local statistics based filters, fuzzy filters, Fourier filters, multiscale filters, non-linear iterative filters, total variation filters, non-local mean filters and hybrid filters. Total 100 breast ultrasound images (40 benign and 60 malignant) are processed using 42 despeckle filtering algorithms. A despeckling filter is considered to be appropriate if it preserves edges and features/structures of the image. Edge preservation capability of a despeckling filter is measured by beta metric (β) and feature/structure preservation capability is quantified using image quality index (IQI). It is observed that out of 42 filters, six filters namely Lee Sigma, FI, FB, HFB, BayesShrink and DPAD yield more clinically acceptable images in terms of edge and feature/structure preservation. The qualitative assessment of these images has been done on the basis of grades provided by the participating experienced radiologist. The pre-processed images are then fed to a segmentation module for segmenting the benign or malignant t...
Source: Biocybernetics and Biomedical Engineering - Category: Biomedical Engineering Source Type: research