Dataset Information

Breast ultrasound image improvement by pixel compounding of compression sequence.

ABSTRACT: Pixel compounding is a technique that synthesizes the information of an image sequence involving slow decorrelation of the speckle to form a detail-recovered and speckle reduced image. To avoid extra data acquisition time and patient exposure, reuse of the existing data is desirable. In the procedure of elasticity imaging, a set of B-mode images with slight changes due to deformation is produced, which provides an ideal input for the pixel compounding. The improvement in image quality is evaluated quantitatively using a figure-of-merit (FOM) that indicates the quality of boundary information recovery and the contrast-to-noise ratio (CNR) over the phantom images. The increase in average CNR is from 0.4 in the original images to 0.8 in the pixel compounded images. The improvement in average FOM is from 0.15 to more than 0.5 on a scale of 0 to 1. In vivo results with a breast cyst, a fibroadenoma, and a breast cancer1 are also presented and the image quality improvement is subjectively evaluated. The results suggest that B-mode breast images from compression procedures are suitable data for pixel compounding, and that a speckle-reduced and detail-recovered or detail-maintained image can be produced. The improved imaging may provide alternative or better information for detection and diagnosis. A similar approach could be extended to elasticity imaging with other modalities.

SUBMITTER: Yang Z

PROVIDER: S-EPMC2778488 | biostudies-literature | 2009 Mar

REPOSITORIES: biostudies-literature

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Breast ultrasound image improvement by pixel compounding of compression sequence.

Yang Zhi Z Sinha Sumedha P SP Booi Rebecca C RC Roubidoux Marilyn A MA Ma Bing B Fowlkes J Brian JB LeCarpentier Gerald L GL Carson Paul L PL

IEEE transactions on ultrasonics, ferroelectrics, and frequency control 20090301 3

Pixel compounding is a technique that synthesizes the information of an image sequence involving slow decorrelation of the speckle to form a detail-recovered and speckle reduced image. To avoid extra data acquisition time and patient exposure, reuse of the existing data is desirable. In the procedure of elasticity imaging, a set of B-mode images with slight changes due to deformation is produced, which provides an ideal input for the pixel compounding. The improvement in image quality is evaluat ...[more]

PMID: 19411207

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Project description:BackgroundThe segmentation of breast ultrasound (US) images has been a challenging task, mainly due to limited data and the inherent image characteristics involved, such as low contrast and speckle noise. Although convolutional neural network-based (CNN-based) methods have made significant progress over the past decade, they lack the ability to model long-range interactions. Recently, the transformer method has been successfully applied to the tasks of computer vision. It has a strong ability to capture distant interactions. However, most transformer-based methods with excellent performance rely on pre-training on large datasets, making it infeasible to directly apply them to medical images analysis, especially that of breast US images with limited high-quality labels. Therefore, it is of great significance to find a robust and efficient transformer-based method for use on small breast US image datasets.MethodsWe developed a dilated transformer (DT) method which mainly uses the proposed residual axial attention layers to build encoder blocks and the introduced dilation module (DM) to further increase the receptive field. We evaluated the proposed method on 2 breast US image datasets using the 5-fold cross-validation method. Dataset A was a public dataset with 562 images, while dataset B was a private dataset with 878 images. Ground truth (GT) was delineated by 2 radiologists with more than 5 years of experience. The evaluation was followed by related ablation experiments.ResultsThe DT was found to be comparable with the state-of-the-art (SOTA) CNN-based method and outperformed the related transformer-based method, medical transformer (MT), on both datasets. Especially on dataset B, the DT outperformed the MT on metrics of Jaccard index (JI) and Dice similarity coefficient (DSC) by 2.67% and 4.68%, respectively. Meanwhile, when compared with Unet, the DT improved JI and DSC by 4.89% and 4.66%, respectively. Moreover, the results of the ablation experiments showed that each add-on part of the DT is important and contributes to the segmentation accuracy.ConclusionsThe proposed transformer-based method could achieve advanced segmentation performance on different small breast US image datasets without pretraining.

Dataset Information

Breast ultrasound image improvement by pixel compounding of compression sequence.

Publications

Breast ultrasound image improvement by pixel compounding of compression sequence.

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