Program Information
Structural Similarity-Based Ultrasound Image Similarity Measurement
Li ZHU1*, Ruixing WANG2 , Kele XU3 , (1) Institute of Electrostatic and Electromagnetic Protection, Mechanical Engineering College, Shijiazhuang, Hebei, (2) College of Optoelectronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, (3) College of Electronical Science and Engineering, National University of Defense Technology, Changsha, Hunan
Presentations
SU-F-J-226 (Sunday, July 31, 2016) 3:00 PM - 6:00 PM Room: Exhibit Hall
Purpose:
We explore a method to measure the global ultrasound image similarity measurement, with the aim to facilitate abnormal region detection and automatic tracking failure re-initialization in the ultrasound image sequences.
Methods:
Structural similarity is used in our similarity measurement framework. The classical structural similarity assesses the visual impact of three characteristics of the ultrasound imaging region from three aspects: luminance, contrast and structure. Unlike the approach to calculate the local means, standard deviations and cross-covariance for adjacent two ultrasound images directly, we propose to compute the three aspects based on the local speckle statistics, in which locally adaptive Nakagami distribution-based measurements are used, resulting in significantly increasing the robustness against the speckle noise.
Results:
As the tongue can stay in the rest position for long duration and it’s easy to conduct the experiment, the proposed similarity measurement is firstly tested on the ultrasound tongue image. The performance demonstrates that the proposed method can provide an accurate similarity description between the frames. Moreover, the potential application of the similarity measurement-based automatic contour tracking failure re-initialization is also explored in our experiment. And the results show that using the proposed similarity-based automatic tracking failure re-initialization method can improve the accuracy of the tracking. And the tracking error reduce from about 2-3 mm to about 1.5 mm using Euclidean distance measurement.
Conclusion:
The performance demonstrates that the proposed method can provide an accurate similarity description between the frames. And, the potential applications of the similarity measurement seem evident in several aspects.
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