A parametric imaging approach for the segmentation of ultrasound data

Davignon, F.; Deprez, J.; Basset, Olivier

doi:10.1016/j.ultras.2005.06.001

Cited by 47 publications

(40 citation statements)

References 30 publications

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“…Davignon et al used the integrated backscatter and mean central frequency to improve their multi-resolution Bayesian region-based algorithm that was based on envelope data. Their algorithm was tested on agar-gel phantom and proved that a multiparameteric approach could improve the segmentation result [63]. These examples show that it is feasible to use spectrum information of the RF signals for the purpose of segmentation.…”

Section: Current Development Of Ultrasound Segmentationmentioning

confidence: 95%

“…The first spectrum property discussed is the total energy of the spectrum, also referred to as the integrated backscatter coefficient (IBS) [71,63]. The IBS coefficient indicates the total reflected power and strength of the backscattering; a larger value corresponds to more energy reflected from the tissues.…”

Section: Calculation Of Spectrum Propertiesmentioning

confidence: 99%

“…f c can be calculated using the moment analysis. Moment analysis has been used by several authors [56,63] to calculate local spectrum properties of RF signals. Fink et al [56] proved that the moment analysis can be used calculate the central frequency and variance of the power spectrum.…”

Section: Calculation Of Spectrum Propertiesmentioning

confidence: 99%

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Automatic Measurement of Human Subcutaneous Fat with Ultrasound

Rohling

Lawrence

2009

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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Measuring human subcutaneous fat is useful for assessing health risks due to obesity and for monitoring athletes' health status, body shapes and weight for various sports competitions such as gymnastics and wrestling. Our aim is to investigate the use of ultrasound imaging in automatically measuring human subcutaneous fat thickness.We proposed to use the spectrum properties extracted from the raw radio frequency (RF) signals of ultrasound for the purpose of fat boundary detection. Our fat detection framework consists of four main steps. The first step is capturing RF data from 11 beam steering angles and at four focal positions. Secondly, two spectrum properties (spectrum variance and integrated backscatter coefficient) are calculated from the local spectrum of RF data using the short time Fourier transform and moment analysis. The values of the spectrum properties are encoded as gray-scale parametric images. Thirdly, spatial compounding is used to reduce speckle noise in the parametric images and improve the visualization of the subcutaneous fat layer. Finally, we apply Rosin's thresholding and Random Sample Consensus boundary detection on the parametric images to extract the fat boundary.The detection framework was tested on 36 samples obtained at the suprailiac, thigh and triceps of nine human participants in vivo. When compared to manual boundary detection on ultrasound images, the best result was obtained from segmenting the spatial compounded spectrum variance values averaged over multiple focuses. A reasonable result could also be obtained by using a single focus. Further, our automatic detection results were compared with the results using skinfold caliper measurements. We found that the correlation is high between our automatic detection and skinfold caliper measurement, and is similar to the previous studies which are not automatic.Our work has shown that the spatial compounded spectrum properties of RF data can be used to segment the subcutaneous fat layer. Based on our results, it is feasible to detect fat at the suprailiac, thigh and triceps sites using the spectrum variance. The values of spectrum variance change more rapidly in the fat tissue than the non-fat tissue.iii

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Section: Current Development Of Ultrasound Segmentationmentioning

confidence: 95%

Section: Calculation Of Spectrum Propertiesmentioning

confidence: 99%

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Automatic Measurement of Human Subcutaneous Fat with Ultrasound

Rohling

Lawrence

2009

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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“…Rf-data were sampled at 16 MHz and transmitted to an external hard disk. The data were band pass filtered (FIR least squares filter [2][3][4][5]) to prevent disturbance by clutter and noise from frequencies outside the frequency band of the transducer. For constructing echograms, the data were amplitude demodulated using the Hilbert transform method.…”

Section: Methodsmentioning

confidence: 99%

“…The addition of temporal information by using cross-correlation techniques might facilitate segmentation in these problematic low contrast regions. Few segmentation methods use the rich phase information available in the radio frequency (rf) signal [3], [4], [5]. Yan et al [6] propose the use of maximum correlation coefficients obtained from a phase sensitive speckle tracking method for segmentation of the left ventricle.…”

Section: Introductionmentioning

confidence: 99%

3D Cardiac Segmentation Using Temporal Correlation of Radio Frequency Ultrasound Data

Nillesen¹,

Lopata²,

Huisman³

et al. 2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

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Abstract. Semi-automatic segmentation of the myocardium in 3D echographic images may substantially support clinical diagnosis of heart disease. Particularly in children with congenital heart disease, segmentation should be based on the echo features solely since a priori knowledge on the shape of the heart cannot be used. Segmentation of echocardiographic images is challenging because of the poor echogenicity contrast between blood and the myocardium in some regions and the inherent speckle noise from randomly backscattered echoes. Phase information present in the radio frequency (rf) ultrasound data might yield useful, additional features in these regions. A semi-3D technique was used to determine maximum temporal cross-correlation values locally from the rf data. To segment the endocardial surface, maximum cross-correlation values were used as additional external force in a deformable model approach and were tested against and combined with adaptive filtered, demodulated rf data. The method was tested on full volume images (Philips, iE33) of four healthy children and evaluated by comparison with contours obtained from manual segmentation.

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Ultrasound Image Post-Processing – Application to Segmentation

Basset

Cachard

Physics for Medical Imaging Applications

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A parametric imaging approach for the segmentation of ultrasound data

Cited by 47 publications

References 30 publications

Automatic Measurement of Human Subcutaneous Fat with Ultrasound

Automatic Measurement of Human Subcutaneous Fat with Ultrasound

3D Cardiac Segmentation Using Temporal Correlation of Radio Frequency Ultrasound Data

Ultrasound Image Post-Processing – Application to Segmentation

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