Interactive GPU active contours for segmenting inhomogeneous objects

• Published in: Journal of real-time image processing Vol. 16; no. 6; pp. 2305 - 2318
• Main Authors: Willcocks, Chris G., Jackson, Philip T. G., Nelson, Carl J., Nasrulloh, Amar V., Obara, Boguslaw
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2019; Springer Nature B.V
• Subjects: Algorithms; Biology; Computer Graphics; Computer Science; Datasets; Energy; Energy distribution; Feedback; Image Processing and Computer Vision; Medical imaging; Memory; Microscopy; Multimedia Information Systems; Neurosciences; Normal distribution; Original Research Paper; Pattern Recognition; Signal,Image and Speech Processing; Software; Three dimensional imaging; Level set methods; Medical applications; Segmentation; GPU; Biological applications; Active contours
• ISSN: 1861-8200; 1861-8219
• DOI: 10.1007/s11554-017-0740-1

We present a segmentation software package primarily targeting medical and biological applications, with a high level of visual feedback and several usability enhancements over existing packages. Specifically, we provide a substantially faster GPU implementation of the local Gaussian distribution fitting energy model, which can segment inhomogeneous objects with poorly defined boundaries as often encountered in biomedical images. We also provide interactive brushes to guide the segmentation process in a semiautomated framework. The speed of our implementation allows us to visualize the active surface in real time with a built-in ray tracer, where users may halt evolution at any time step to correct implausible segmentation by painting new blocking regions or new seeds. Quantitative and qualitative validation is presented, demonstrating the practical efficacy of our interactive elements for a variety of real-world datasets.