Ultrasound Image Enhancement Using Structure-Based Filtering

Ultrasound images are prone to speckle noises. Speckles blur features which are essential for diagnosis and assessment. Thus despeckling is a necessity in ultrasound image processing. Linear filters can suppress speckles, but they smooth out features. Median filter based despeckling algorithms produce better results. However, they may produce artifact patterns in the resulted images and oversmooth nonuniform regions. This paper presents an innovative despeckle procedure for ultrasound images. In the proposed method, the diffusion tensor of intensity is computed at each pixel at first. Then the eigensystem of the diffusion tensor is calculated and employed to detect and classify the underlying structure. Based on the classification result, a feasible filter is selected to suppress speckles and enhance features. Test results show that the proposed despeckle method reduces speckles in uniform areas and enhances tissue boundaries and spots.

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Using the method of renormalization with limitation for medical ultrasound image processing.

Journal of Radio Electronics ◽

10.30898/1684-1719.2020.10.1 ◽

2020 ◽

Vol 2020 (10) ◽

Author(s):

A.V. Kokoshkin ◽

Keyword(s):

Image Processing ◽

Ultrasound Image ◽

Speckle Noise ◽

Ultrasound Images ◽

Medical Ultrasound ◽

Bilateral Filtering ◽

Ultrasound Image Processing ◽

Images Processing

This article proposes the application of the technique of combining the modernized method of renormalization with limitation with subsequent bilateral filtering to improve the quality of medical ultrasound images. Processing according to this algorithm increases the overall contrast of the image, smoothes out speckle noise and makes it possible to cope well with determining the localization of significant objects. The presented results indicate a significant improvement in the quality of ultrasound images, which can serve as an auxiliary tool for medical workers when clarifying the diagnosis.

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2D Ultrasound Image Processing in Identifying Responses of Urogenital Structures to Pelvic Floor Muscle Activity

Annals of Biomedical Engineering ◽

10.1007/s10439-005-9059-3 ◽

2006 ◽

Vol 34 (3) ◽

pp. 477-493 ◽

Cited By ~ 29

Author(s):

Qiyu Peng ◽

Ruth C. Jones ◽

Christos E. Constantinou

Keyword(s):

Image Processing ◽

Pelvic Floor ◽

Muscle Activity ◽

Pelvic Floor Muscle ◽

Ultrasound Image ◽

2D Ultrasound ◽

Ultrasound Image Processing

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Assessing the blood pressure waveform of the carotid artery using an ultrasound image processing method

ULTRASONOGRAPHY ◽

10.14366/usg.16019 ◽

2017 ◽

Vol 36 (2) ◽

pp. 144-152 ◽

Cited By ~ 2

Author(s):

Effat Soleimani ◽

Manijhe Mokhtari-Dizaji ◽

Nasser Fatouraee ◽

Hazhir Saberi

Keyword(s):

Blood Pressure ◽

Image Processing ◽

Carotid Artery ◽

Ultrasound Image ◽

Processing Method ◽

Pressure Waveform ◽

Image Processing Method ◽

Blood Pressure Waveform ◽

Ultrasound Image Processing

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Enhancing ultrasound texture differences for developing an in vivo 'virtual histology' approach to bovine ovarian imaging

Reproduction Fertility and Development ◽

10.1071/rd06167 ◽

2007 ◽

Vol 19 (8) ◽

pp. 910 ◽

Cited By ~ 7

Author(s):

Mark G. Eramian ◽

Gregg P. Adams ◽

Roger A. Pierson

Keyword(s):

Image Processing ◽

Ultrasound Image ◽

Ultrasound Images ◽

Virtual Histology ◽

Form And Function ◽

Non Invasive ◽

Candidate Image ◽

And Function ◽

Processing Techniques

A ‘virtual histology’ can be thought of as the ‘staining’ of a digital ultrasound image via image processing techniques in order to enhance the visualisation of differences in the echotexture of different types of tissues. Several candidate image-processing algorithms for virtual histology using ultrasound images of the bovine ovary were studied. The candidate algorithms were evaluated qualitatively for the ability to enhance the visual differences in intra-ovarian structures and quantitatively, using standard texture description features, for the ability to increase statistical differences in the echotexture of different ovarian tissues. Certain algorithms were found to create textures that were representative of ovarian micro-anatomical structures that one would observe in actual histology. Quantitative analysis using standard texture description features showed that our algorithms increased the statistical differences in the echotexture of stroma regions and corpus luteum regions. This work represents a first step toward both a general algorithm for the virtual histology of ultrasound images and understanding dynamic changes in form and function of the ovary at the microscopic level in a safe, repeatable and non-invasive way.

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