A Comparison of Filters for Ultrasound Images

Calóope, P. B.; Medeiros, Fátima N. S. de; Marques, Régis C. P.; Costa, Rafael Santiago da

doi:10.1007/978-3-540-27824-5_135

Cited by 10 publications

(9 citation statements)

References 9 publications

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“…Mean filter. The mean filter [41,42] replaces each pixel by the average value of the intensities in its neighborhood. It can locally reduce the variance and is easy to implement.…”

Section: Linear Filtersmentioning

confidence: 99%

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Automated breast cancer detection and classification using ultrasound images: A survey

Cheng

Shan

et al. 2010

Pattern Recognition

635

346

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Breast cancer is the second leading cause of death for women all over the world. Since the cause of the disease remains unknown, early detection and diagnosis is the key for breast cancer control, and it can increase the success of treatment, save lives and reduce cost. Ultrasound imaging is one of the most frequently used diagnosis tools to detect and classify abnormalities of the breast. In order to eliminate the operator dependency and improve the diagnostic accuracy, computer-aided diagnosis (CAD) system is a valuable and beneficial means for breast cancer detection and classification. Generally, a CAD system consists of four stages: preprocessing, segmentation, feature extraction and selection, and classification. In this paper, the approaches used in these stages are summarized and their advantages and disadvantages are discussed. The performance evaluation of CAD system is investigated as well.

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“…Mean filter. The mean filter [41,42] replaces each pixel by the average value of the intensities in its neighborhood. It can locally reduce the variance and is easy to implement.…”

Section: Linear Filtersmentioning

confidence: 99%

“…Order-statistic filters are particularly effective in reducing noise whose probability density function has significant tail. The median filter [34,41,42] is a special case of order-statistic filters. It preserves the edge sharpness and produces less blurring than mean filter.…”

Section: Adaptive Mean Filtermentioning

confidence: 99%

Automated breast cancer detection and classification using ultrasound images: A survey

Cheng

Shan

et al. 2010

Pattern Recognition

635

346

View full text Add to dashboard Cite

show abstract

“…More complex operations include the "sticks" filter (short line segments) [9], [10] or various types of diffusion filters [11], [12], [13], [14]. Calóope et al [15] provide a comparison of filtering operations for US images. They present and evaluate the mean, median, Frost and MAP filter and also the applicability of the wavelet transformation for this task.…”

Section: A Pre-processingmentioning

confidence: 99%

“…The criteria for the evaluation of the pre-processing are: The mean squared error (MSE), the signal-to-MSE ratio (S/MSE) and the edge preservation β [15]. The reduction of noise is analyzed with MSE and S/MSE.…”

Section: A Pre-processing 1) Evaluation Criteriamentioning

confidence: 99%

Quantification of Thyroid Volume Using 3-D Ultrasound Imaging

Kollorz

Hahn

Linke

et al. 2008

IEEE Trans. Med. Imaging

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Abstract-Ultrasound (US) is among the most popular diagnostic techniques today. It is non-invasive, fast, comparably cheap, and does not require ionizing radiation. US is commonly used to examine the size, and structure of the thyroid gland. In clinical routine, thyroid imaging is usually performed by means of 2-D US. Conventional approaches for measuring the volume of the thyroid gland or its nodules may therefore be inaccurate due to the lack of 3-D information. This work reports a semi-automatic segmentation approach for the classification, and analysis of the thyroid gland based on 3-D US data. The images are scanned in 3-D, pre-processed, and segmented. Several pre-processing methods, and an extension of a commonly used geodesic active contour level set formulation are discussed in detail. The results obtained by this approach are compared to manual interactive segmentations by a medical expert in five representative patients. Our work proposes a novel framework for the volumetric quantification of thyroid gland lobes, which may also be expanded to other parenchymatous organs.

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“…Ein hoher PI-Wert kann allerdings sowohl auf eine hohe Kontrastmittelkonzentration als auch auf Rauschen hinweisen. Da die Glättung der einzelnen Bilder der Ultraschallsequenz zeitaufwendig und rechenintensiv ist[24,25], wird hier ein anderer Ansatz verfolgt. Anstatt Bild für Bild zu entrauschen, werden die TICs geglättet.…”

unclassified

Bolus Harmonic Imaging zur automatischen Erkennung ischämiebedingter Perfusionsdefizite im Gehirn

Maciak¹,

Seidel²,

Meyer‐Wiethe³

et al. 2008

Ultraschall in Med

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The presented methods seem to be adequate for detecting ischemic brain tissue. The classification of contrast agent kinetics provides the best results and has further advantages. It is robust with respect to noise and the calculation is fast because the extraction of parametric images is omitted. The very high sensitivity and specificity must be validated in a larger patient population. Reliable and automated detection of perfusion defects at the bedside seems to be possible.

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A Comparison of Filters for Ultrasound Images

Cited by 10 publications

References 9 publications

Automated breast cancer detection and classification using ultrasound images: A survey

Automated breast cancer detection and classification using ultrasound images: A survey

Quantification of Thyroid Volume Using 3-D Ultrasound Imaging

Bolus Harmonic Imaging zur automatischen Erkennung ischämiebedingter Perfusionsdefizite im Gehirn

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