Learning evaluation of ultrasound image segmentation using combined measures

Mengjie Fang; Yongkang Luo; Mingyue Ding

doi:10.1117/12.2214425

24 March 2016 Learning evaluation of ultrasound image segmentation using combined measures

Mengjie Fang, Yongkang Luo, Mingyue Ding

Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis; 97853R (2016) https://doi.org/10.1117/12.2214425
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Objective evaluation of medical image segmentation is one of the important steps for proving its validity and clinical applicability. Although there are many researches presenting segmentation methods on medical image, while with few studying the evaluation methods on their results, this paper presents a learning evaluation method with combined measures to make it as close as possible to the clinicians’ judgment. This evaluation method is more quantitative and precise for the clinical diagnose. In our experiment, the same data sets include 120 segmentation results of lumen-intima boundary (LIB) and media-adventitia boundary (MAB) of carotid ultrasound images respectively. And the 15 measures of goodness method and discrepancy method are used to evaluate the different segmentation results alone. Furthermore, the experimental results showed that compared with the discrepancy method, the accuracy with the measures of goodness method is poor. Then, by combining with the measures of two methods, the average accuracy and the area under the receiver operating characteristic (ROC) curve of 2 segmentation groups are higher than 93% and 0.9 respectively. And the results of MAB are better than LIB, which proved that this novel method can effectively evaluate the segmentation results. Moreover, it lays the foundation for the non-supervised segmentation evaluation system.

Citation Download Citation

Mengjie Fang, Yongkang Luo, and Mingyue Ding "Learning evaluation of ultrasound image segmentation using combined measures", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97853R (24 March 2016); https://doi.org/10.1117/12.2214425

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KEYWORDS

Image segmentation

Laser induced breakdown spectroscopy

Image processing algorithms and systems

Ultrasonography

Data acquisition

Gold

Image processing

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