Automated segmentation of synchrotron radiation micro-computed tomography biomedical images using Graph Cuts and neural networks

作者：de Moura Meneses Anderson Alvarenga; Giusti Alessandro; de Almeida Andre Pereira; Nogueira Liebert Parreira; Braz Delson; Barroso Regina Cely; deAlmeida Carlos Eduardo

来源：Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment , 2011, 660(1): 121-129.

DOI：10.1016/j.nima.2011.08.007

• Synchrotron radiation
• Phase contrast imaging
• micro-Computed Tomography
• Image segmentation
• Energy minimization via Graph Cuts
• Artificial neural networks

Synchrotron Radiation (SR) X-ray micro-Computed Tomography (mu CT) enables magnified images to be used as a non-invasive and non-destructive technique with a high space resolution for the qualitative and quantitative analyses of biomedical samples. The research on applications of segmentation algorithms to SR-mu CT is an open problem, due to the interesting and well-known characteristics of SR images for visualization, such as the high resolution and the phase contrast effect. In this article, we describe and assess the application of the Energy Minimization via Graph Cuts (EMvGC) algorithm for the segmentation of SR-mu CT biomedical images acquired at the Synchrotron Radiation for MEdical Physics (SYRMEP) beam line at the Elettra Laboratory (Trieste, Italy). We also propose a method using EMvGC with Artificial Neural Networks (EMANNs) for correcting misclassifications due to intensity variation of phase contrast, which are important effects and sometimes indispensable in certain biomedical applications, although they impair the segmentation provided by conventional techniques. Results demonstrate considerable success in the segmentation of siz-mu CT biomedical images, with average Dice Similarity Coefficient 99.88% for bony tissue in Wistar Rats rib samples (EMvGC), as well as 98.95% and 98.02% for scans of Rhodnius prolixus insect samples (Chagas's disease vector) with EMANNs, in relation to manual segmentation. The techniques EMvGC and EMANNs cope with the task of performing segmentation in images with the intensity variation due to phase contrast effects, presenting a superior performance in comparison to conventional segmentation techniques based on thresholding and linear/nonlinear image filtering, which is also discussed in the present article.

• 出版日期2011-12-21