This paper reviews ultrasound segmentation methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images. First, we present a review of articles by clinical application to highlight the approaches that have been investigated and degree of validation that has been done in different clinical domains. Then, we present a classification of methodology in terms of use of prior information. We conclude by selecting ten papers which have presented original ideas that have demonstrated particular clinical usefulness or potential specific to the ultrasound segmentation problem

/pdf/ultrasound-image-segmentation-a-survey-55ssqpzk7q.pdf

Ultrasound image segmentation: a survey

https://www.cs.virginia.edu/~skadron/Papers/cuda_jpdc08.pdf

A performance study of general-purpose applications on graphics processors using CUDA

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

With the introduction of spectral-domain optical coherence tomography (OCT), much larger image datasets are routinely acquired compared to what was possible using the previous generation of time-domain OCT. Thus, the need for 3-D segmentation methods for processing such data is becoming increasingly important. We report a graph-theoretic segmentation method for the simultaneous segmentation of multiple 3-D surfaces that is guaranteed to be optimal with respect to the cost function and that is directly applicable to the segmentation of 3-D spectral OCT image data. We present two extensions to the general layered graph segmentation method: the ability to incorporate varying feasibility constraints and the ability to incorporate true regional information. Appropriate feasibility constraints and cost functions were learned from a training set of 13 spectral-domain OCT images from 13 subjects. After training, our approach was tested on a test set of 28 images from 14 subjects. An overall mean unsigned border positioning error of 5.69 plusmn 2.41 mum was achieved when segmenting seven surfaces (six layers) and using the average of the manual tracings of two ophthalmologists as the reference standard. This result is very comparable to the measured interobserver variability of 5.71 plusmn 1.98 mum.

Automated 3-D Intraretinal Layer Segmentation of Macular Spectral-Domain Optical Coherence Tomography Images

Breast lesion detection using ultrasound imaging is considered an important step of computer-aided diagnosis systems. Over the past decade, researchers have demonstrated the possibilities to automate the initial lesion detection. However, the lack of a common dataset impedes research when comparing the performance of such algorithms. This paper proposes the use of deep learning approaches for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet. Their performance is compared against four state-of-the-art lesion detection algorithms (i.e., Radial Gradient Index, Multifractal Filtering, Rule-based Region Ranking, and Deformable Part Models). In addition, this paper compares and contrasts two conventional ultrasound image datasets acquired from two different ultrasound systems. Dataset A comprises 306 (60 malignant and 246 benign) images and Dataset B comprises 163 (53 malignant and 110 benign) images. To overcome the lack of public datasets in this domain, Dataset B will be made available for research purposes. The results demonstrate an overall improvement by the deep learning approaches when assessed on both datasets in terms of True Positive Fraction, False Positives per image, and F-measure.

/pdf/automated-breast-ultrasound-lesions-detection-using-2ppqkbizj7.pdf

Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks

This paper provides the derivation of speckle reducing anisotropic diffusion (SRAD), a diffusion method tailored to ultrasonic and radar imaging applications. SRAD is the edge-sensitive diffusion for speckled images, in the same way that conventional anisotropic diffusion is the edge-sensitive diffusion for images corrupted with additive noise. We first show that the Lee and Frost filters can be cast as partial differential equations, and then we derive SRAD by allowing edge-sensitive anisotropic diffusion within this context. Just as the Lee (1980, 1981, 1986) and Frost (1982) filters utilize the coefficient of variation in adaptive filtering, SRAD exploits the instantaneous coefficient of variation, which is shown to be a function of the local gradient magnitude and Laplacian operators. We validate the new algorithm using both synthetic and real linear scan ultrasonic imagery of the carotid artery. We also demonstrate the algorithm performance with real SAR data. The performance measures obtained by means of computer simulation of carotid artery images are compared with three existing speckle reduction schemes. In the presence of speckle noise, speckle reducing anisotropic diffusion excels over the traditional speckle removal filters and over the conventional anisotropic diffusion method in terms of mean preservation, variance reduction, and edge localization.

/pdf/speckle-reducing-anisotropic-diffusion-3598kp8s0a.pdf

Speckle reducing anisotropic diffusion

The instantaneous coefficient of variation (ICOV) edge detector, based on normalized gradient and Laplacian operators, has been proposed for edge detection in ultrasound images. In this paper, the edge detection and localization performance of the ICOV-squared (ICOVS) detector are examined. First, a simplified version of the ICOVS detector, the normalized gradient magnitude squared, is scrutinized in order to reveal the statistical performance of edge detection and localization in speckled ultrasound imagery. Both the probability of detection and the probability of false alarm are evaluated for the detector. Edge localization is characterized by the position of the peak and the 3-dB width of the detector response. Then, the speckle-edge response of the ICOVS as applied to a realistic edge model is studied. Through theoretical analysis, we reveal the compensatory effects of the normalized Laplacian operator in the ICOV edge detector for edge-localization error. An ICOV-based edge-detection algorithm is implemented in which the ICOV detector is embedded in a diffusion coefficient in an anisotropic diffusion process. Experiments with real ultrasound images have shown that the proposed algorithm is effective in extracting edges in the presence of speckle. Quantitatively, the ICOVS provides a lower localization error, and qualitatively, a dramatic improvement in edge-detection performance over an existing edge-detection method for speckled imagery.

Edge detection in ultrasound imagery using the instantaneous coefficient of variation

We present a technique for semiautomated segmentation of human prostates using suprapubic ultrasound (US) images. In this approach, a speckle reducing anisotropic diffusion (SRAD) is applied to enhance the images and the instantaneous coefficient of variation (ICOV) is utilized for edge detection. Segmentation is accomplished via a parametric active contour model in a polar coordinate system that is tailored to the application. The algorithm initially approximates the prostate boundary in two stages. First a primary contour is detected using an elliptical model, followed by a primary contour optimization using an area-weighted mean-difference binary flow geometric snake model. The algorithm was assessed by comparing the computer-derived contours with contours produced manually by three sonographers. The proposed method has application in radiation therapy planning and delivery, as well as in automated volume measurements for ultrasonic diagnosis. The average root mean square discrepancy between computed and manual outlines is less than the inter-observer variability. Furthermore, 76% of the computer-outlined contour is less than 1 s manual outline variance away from “true” boundary of prostate. We conclude that the methods developed herein possess acceptable agreement with manually contoured prostate boundaries and that they are potentially valuable tools for radiotherapy treatment planning and verification. © 2004 American Association of Physicists in Medicine . [DOI: 10.1118/1.1809791]

/pdf/segmentation-of-the-prostate-from-suprapubic-ultrasound-2vzx4zeu5q.pdf

Segmentation of the prostate from suprapubic ultrasound images.

We first derive rigorously a partial differential equation (PDE) for speckle reduction from minimizing a cost functional of the instantaneous coefficient of variation. Then, we show that the piecewise exponential function is the solution of the derived PDE. Next, we express the derived PDE using log-compressed ultrasound data, followed by a numerical implementation scheme. Finally, we demonstrate the performance of the proposed PDE using examples and compare the results with those obtained from the speckle reducing anisotropic diffusion (SRAD) algorithm.

Generalized speckle reducing anisotropic diffusion for ultrasound imagery

Ultrasound B-scan exhibits shadowing and enhancement artifacts due to acoustic wave propagation and spatially varying scatter attenuation across layers of tissues. These artifacts hide underlying echo signals that are truly clinically indicative of diseases. Attenuation compensation estimates and corrects for shadowing and enhancement artifacts, which improves the quality of ultrasound imaging. Block-based attenuation compensation methods, widely employed in commercial scanners, produce results with resolutions limited by the block size. To obtain higher spatial resolution (as desired for quantitative analysis), we present a backscatter-contour-attenuation (BCA) joint estimation model for attenuation compensation in pulse-echo imaging using a set of self-consistent partial differential equations and a contour evolution model. The problem is posed as reconstructing sources of information from observations. We derive the joint estimation model from minimizing a cost functional of separated attributes with region-based isotropic regularizations. A three-step alternating minimization method is adopted towards a tractable numerical solution. Detailed numerical methods are described. The efficacy of the proposed approach is demonstrated using simulated and real images.

Yongjian Yu

Papers

Speckle reducing anisotropic diffusion

Edge detection in ultrasound imagery using the instantaneous coefficient of variation

Segmentation of the prostate from suprapubic ultrasound images.

Generalized speckle reducing anisotropic diffusion for ultrasound imagery

Backscatter-Contour-Attenuation Joint Estimation Model for Attenuation Compensation in Ultrasound Imagery