Level-set segmentation of 2D and 3D ultrasound data using local gamma distribution fitting energy

• Main Authors: Thanh Minh Bui, Coron, Alain, Mamou, Jonathan, Saegusa-Beecroft, Emi, Machi, Junji, Dizeux, Alexandre, Bridal, S. Lori, Feleppa, Ernest J.
• Format: Conference Proceeding
• Language: English
• Subjects: 3D ultrasound; high frequency; Image segmentation; intensity inhomogeneity; Kernel; level set; lymph node; Noise; Nonhomogeneous media; segmentation; Speckle; Three-dimensional displays; Ultrasonic imaging
• ISSN: 1945-7928; 1945-8452
• DOI: 10.1109/ISBI.2015.7164066

Ultrasound (US) data suffer from speckle noise as well as intensity inhomogeneities due to underlying changes in acoustic properties of tissue structure and/or the effects of acoustic focusing and attenuation. This paper describes a 2D and 3D variational level-set method for segmenting such data. To deal with the local statistics of speckle noise, the data term of the level-set energy function is based on local gamma distributions which have shown an ability to model envelope data and gray-level pixel intensities of B-mode clinical images. Local statistics are estimated at a controllable scale using a smooth function with a compact support, a mollifyer, and the method of moments. Compared to manual segmentation, the investigated method provides a high Dice similarity coefficient (DSC) on 3D simulated data, an average DSC of 0.915 on 12 B-mode images of murine tumors acquired with a clinical US system, and average DSCs of 0.920, 0.806 and 0.975 for three media of 54 3D envelope data sets acquired with a high-frequency, focused transducer from dissected human lymph nodes. It also outperforms methods that employ local Gaussian statistics instead of local gamma statistics.