Automatic ultrasound image enhancement for 2D semi-automatic breast-lesion segmentation

Kongkuo Lu; Christopher S. Hall

doi:10.1117/12.2042242

18 March 2014 Automatic ultrasound image enhancement for 2D semi-automatic breast-lesion segmentation

Kongkuo Lu, Christopher S. Hall

Proceedings Volume 9035, Medical Imaging 2014: Computer-Aided Diagnosis; 90351M (2014) https://doi.org/10.1117/12.2042242
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Breast cancer is the fastest growing cancer, accounting for 29%, of new cases in 2012, and second leading cause of cancer death among women in the United States and worldwide. Ultrasound (US) has been used as an indispensable tool for breast cancer detection/diagnosis and treatment. In computer-aided assistance, lesion segmentation is a preliminary but vital step, but the task is quite challenging in US images, due to imaging artifacts that complicate detection and measurement of the suspect lesions. The lesions usually present with poor boundary features and vary significantly in size, shape, and intensity distribution between cases. Automatic methods are highly application dependent while manual tracing methods are extremely time consuming and have a great deal of intra- and inter- observer variability. Semi-automatic approaches are designed to counterbalance the advantage and drawbacks of the automatic and manual methods. However, considerable user interaction might be necessary to ensure reasonable segmentation for a wide range of lesions. This work proposes an automatic enhancement approach to improve the boundary searching ability of the live wire method to reduce necessary user interaction while keeping the segmentation performance. Based on the results of segmentation of 50 2D breast lesions in US images, less user interaction is required to achieve desired accuracy, i.e. < 80%, when auto-enhancement is applied for live-wire segmentation.

Citation Download Citation

Kongkuo Lu and Christopher S. Hall "Automatic ultrasound image enhancement for 2D semi-automatic breast-lesion segmentation", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90351M (18 March 2014); https://doi.org/10.1117/12.2042242

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