Segmenting ultrasound video with a wavelet variational model

April 27, 2016

Image segmentation, the process of separating a digital image into multiple sections for individual examination, is frequently used in medical image analysis. For example, segmentation in ultrasound footage helps identify boundaries and regions of interest (ROI) that facilitate image interpretation. Efficient segmentation of ultrasound videos, however, is often complicated by low contrast, shadow effects, and complex "noise" statistics (unexplained variations). In addition, real-time applications such as navigation during operational surgery require efficient algorithms.

In an article published this month in the SIAM Journal on Imaging Sciences, authors Jiulong Liu, Xiaoqun Zhang, Bin Dong, Zuowei Shen, and Lixu Gu propose a video segmentation model to recognize ROI in ultrasounds. "The proposed model aims to track a moving boundary in ultrasound video efficiently and robustly, with a mathematically-sound framework," says Zhang. "Specifically, we tackle the problem by using wavelet frames and incorporating the noise statistics under a variational framework. The continuity and regularity of the moving boundary is effectively incorporated via weighted regularization, without introducing a heavy computational burden. The overall method can be efficiently solved with a recently-developed fast algorithm, making it useful in real-time clinical applications."

Multiple published methods of image segmentation currently exist, but Liu et al. specifically implement variational methods, which are commonly used for motion tracking and edge detection due to their modeling flexibility. "Variational methods have been demonstrated to be robust and effective for complicated image segmentation tasks," says Dong. "The variational framework permits solid theoretical analysis of the models that can well guide the modeling itself and provide fundamental understanding of the solutions."

Liu et al. also chose to incorporate wavelet frames, which collect more detail than other variational methods and efficiently segment low-quality footage, such as ultrasound video. This is especially true when the image includes features at various scales. "Wavelet frame regularization is used because the geometric structures and singularities in different scales can be identified and extracted efficiently from complex noise environments in the wavelet domain," says Shen. "It allows us to track and sharpen geometric shapes when they are segmented automatically through sequential images in the video."

The authors designed their model to segment an ultrasound video both sequentially and collectively. The model incorporates shape priors - a type of probability distribution - in single-image segmentation and computes consecutive shape priors automatically for subsequent segmentations.

Liu et al. apply their model to two ultrasound video data sets and obtain numerical results, which confirm the model's ability to efficiently track ROI. "Ultrasound imaging is an important modality in clinical application due to its low cost and portability," says Liu. "However, its related analysis for accurate diagnosis and monitoring is still challenging due to low image quality, artifacts, and noise. The numerical results on real ultrasound data sets demonstrate that the proposed wavelet frame model with distance prior can track the regions of interest effectively, in terms of both segmentation quality and computational time." The results compare favorably with other approaches.

The model's success could improve medical approaches and technology that rely on image segmentation, and Liu et al. are looking to expand its use. "The model can be further extended to other imaging modality or to locate multi-region simultaneously," says Liu. "More geometric and prior information can be used to enhance the robustness of the method." Such advancements will continue to increase the speed, efficiency, and performance of image segmentation.
-end-
Source Article:

A Wavelet Frame Method with Shape Prior for Ultrasound Video Segmentation SIAM Journal on Imaging Sciences, 9(2), 495-519. (Online publish date: April 7, 2016).

About the Authors:

Jiulong Liu is a Ph.D. student at in the Department of Mathematics at Shanghai Jiao Tong University. His research interests are mathematical modeling and computation in medical imaging. Xiaoqun Zhang is an associate professor in the Department of Mathematics and the Institute of Natural Sciences at Shanghai Jiao Tong University. Her research interests are mathematical modeling and the use of algorithms in imaging science. Bin Dong is an associate professor of mathematics at Peking University. His research interests include mathematical modeling, computation in imaging science, and high dimensional data analysis. Zuowei Shen is Tan Chin Tuan Centennial Professor in the Department of Mathematics at the National University of Singapore. He studies approximation theory and wavelet theory; time-frequency analysis, and imaging science. Lixu Gu is a professor in the School of Biomedical Engineering and Shanghai Jiao Tong University. His research interests are in medical image analysis, pattern recognition, and computer-aided diagnosis.

Society for Industrial and Applied Mathematics

Related Noise Articles from Brightsurf:

Community noise may affect dementia risk
Results from a new study published in Alzheimer's & Dementia support emerging evidence suggesting that noise may influence individuals' risk of developing dementia later in life.

Turning excess noise into signal
Excess noise fluctuations of light are widely considered to be detrimental in optics and photonics.

New method uses noise to make spectrometers more accurate
Optical spectrometers are instruments with a wide variety of uses.

Noise can put you off your food
Noise can make or break a dining experience, according to a laboratory study replicating common noise levels in restaurants.

To make a better sensor, just add noise
Adding noise to enhance a weak signal is a sensing phenomenon common in the animal world but unusual in manmade sensors.

Beating noise via superposition of order
Information can successfully be transmitted through noisy channels using quantum mechanics, according to new research from The University of Queensland and Griffith University.

Adding noise for completely secure communication
How can we protect communications against 'eavesdropping' if we don't trust the devices used in the process?

Noise disturbs the brain's compass
Our sense of direction tends to decline with age. In 'Nature Communications', researchers from the German Center for Neurodegenerative Diseases and experts from the USA report on new insights into the causes of this phenomenon.

A thin lensless camera free of noise
Scientists from Tsinghua University in China and MIT in the US report that applying a compressive sensing algorithm can significantly improve the quality of lensless imaging.

Insights into why loud noise is bad for your health
Two new mouse studies provide new insight into how noise exposure can lead to high blood pressure and cancer-related DNA damage.

Read More: Noise News and Noise Current Events
Brightsurf.com is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to Amazon.com.