A review on medical image denoising algorithms

The objective of this study is to assess the combined performance of textural and morphological features for the detection and diagnosis of breast masses in ultrasound images We have extracted a total of forty four features using textural and morphological techniques Support vector machine (SVM) classifier is used to discriminate the tumors into benign or malignant The performance of individual as well as combined features are assessed using accuracy(Ac), sensitivity(Se), specificity(Sp), Matthews correlation coefficient(MCC) and area AZ under receiver operating characteristics curve The individual features produced classification accuracy in the range of 6166% and 9083% and when features from each category are combined, the accuracy is improved in the range of 7916% and 9583% Moreover, the combination of gray level co-occurrence matrix (GLCM) and ratio of perimeters (P
 ratio
 ) presented highest performance among all feature combinations (Ac 9585%, Se 96%, Sp 9146%, MCC 09146 and AZ 09444)The results indicated that the discrimination performance of a computer aided breast cancer diagnosis system increases when textural and morphological features are combined

https://link.springer.com/content/pdf/10.1186%2Fs13673-015-0029-y.pdf

Assessment of combined textural and morphological features for diagnosis of breast masses in ultrasound

A hybrid algorithm for speckle noise reduction of ultrasound images

Ultrasound image despeckling using low rank matrix approximation approach

Breast ultrasound is an important tool used in the medical treatment and diagnosis of breast tumor. However, noise defined as speckles are generated inevitably. Although the existence of speckle may be beneficial to diagnosis if used by a well-trained observer, it often causes disturbance which negatively affects clinical diagnosis, not only by reducing resolution and contrast of ultrasound images, but also by adding difficulties to recognize tumor region accurately. In this paper, we investigate a number of popular de-speckling algorithms, including filters based on frequency domain, filters based on local statistical properties, filters based on minimum mean square error (MMSE), and filters based on Partial Differential Equation (PDE). Two visual measurement evaluation criteria, i.e., Mean to Variance Ratio (VMR) and Laplace Response of Domain (LRD), are chosen for the performance comparison of those filters in the application of ultrasound breast image filtering. Moreover, the filtering effect is further evaluated with respect to the segmentation accuracy of tumor regions. According to the evaluation results, we conclude that Bilateral Filter (BF) achieves the best visual effect. Although Weickert J Diffusion (WJD) and Total Variation (TV) can also obtain good visual effect and segmentation accuracy, they are very time-consuming.

Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images

Wavelet and fast bilateral filter based de-speckling method for medical ultrasound images

It is well known that the quality of ultrasound image is significantly degraded by the speckle noise, which has restricted the development of automatic diagnostic techniques for ultrasound images, especially for the breast ultrasound images. This necessitates the need to choose an optimal speckle filtering algorithm for the specific clinical application with different required criteria. In this paper, the study focuses on the comparison of despeckle filters for the breast ultrasound images. Firstly, the models of speckle noise for medical ultrasound images are discussed. After that, eleven despeckle filters which are classified into five categories (local adaptive filter, anisotropic diffusion filter, multi-scale filter, nonlocal means filter, and hybrid filter) are described. Then, the comparative experiments of eleven despeckle filters for the two types of simulated images and clinical ultrasound breast images are presented. Finally, to objectively and systematically compare the performance of eleven despeckle filters, several comparison methods are used, such as the full-reference image quality metrics, the nonreference/blind image quality metrics, observing the removed noise images, as well as the visual evaluation of experts.

Chen Wang

Papers

Wavelet and fast bilateral filter based de-speckling method for medical ultrasound images

Comparison of Despeckle Filters for Breast Ultrasound Images

Multi-label Deep Forest: Towards Automatic Modulation Recognition of Compound Wireless Signals at Low-SNR Environment