Wyniki wyszukiwania - BazTech

1

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

Kriti, Virmani Jitendra, Agarwal Ravinder

Biocybernetics and Biomedical Engineering

|

2019

|

Vol. 39, no. 1

100--121

EN

In the present work, the performance assessment of despeckle filtering algorithms has been carried out for (α) noise reduction in breast ultrasound images and (b) segmentation of benign and malignant tumours from breast 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 experienced participating radiologist. The pre-processed images are then fed to a segmentation module for segmenting the benign or malignant tumours from ultrasound images. The performance assessment of segmentation algorithm has been done quantitatively using the Jaccard index. The results of both quantitative and qualitative assessment by the radiologist indicate that the DPAD despeckle filtering algorithm yields more clinically acceptable images and results in better segmentation of benign and malignant tumours from breast ultrasound images.

2

Image Quality Assessment Using Edge Correlation

Ruikar J., Sinha A., Chaudhury S.

International Journal of Electronics and Telecommunications

|

2017

|

Vol. 63, No. 1

99--107

EN

In literature, oriented filters are used for low-level vision tasks. In this paper, we propose use of steerable Gaussian filter in image quality assessment. Human visual system is more sensitive to multidirectional edges present in natural images. The most degradation in image quality is caused due to its edges. In this work, an edge based metric termed as steerable Gaussian filtering (SGF) quality index is proposed as objective measure for image quality assessment. The performance of the proposed technique is evaluated over multiple databases. The experimental result shows that proposed method is more reliable and outperform the conventional image quality assessment method.

3

Measurement of noise in the Monte Carlo point sampling method

Guzek K., Napieralski P.

Bulletin of the Polish Academy of Sciences. Technical Sciences

|

2011

|

Vol. 59, nr 1

15-19

EN

This paper gives a concise overview of the techniques we have used to find out the degree of measuring the quality of rendered images and a level of noise in particular. First part of the paper presents designed and conducted psychophysical experiment involving human subjective judgment. Then, two of the existing numerical image comparison methods are considered in the context of assessing the level of noise produced by global illuminations algorithms. The results of the participants’ subjective responses are correlated with the data obtained from objective mathematical metrics. The main goal of research was to determine the objective and perceptual measure of quality for images with fixed sampling strategy. The results will help to establish the measures of identifying the human perception in the assessment of images generated with global illumination algorithms.