From Event: SPIE Commercial + Scientific Sensing and Imaging, 2017
The automatic detection and quantification of skeletal structures has a variety of different applications for biological research. Accurate segmentation of the pelvis from X-ray images of mice in a high-throughput project such as the Mouse Genomes Project not only saves time and cost but also helps achieving an unbiased quantitative analysis within the phenotyping pipeline. This paper proposes an automatic solution for pelvis segmentation based on structural and orientation properties of the pelvis in X-ray images. The solution consists of three stages including pre-processing image to extract pelvis area, initial pelvis mask preparation and final pelvis segmentation. Experimental results on a set of 100 X-ray images showed consistent performance of the algorithm. The automated solution overcomes the weaknesses of a manual annotation procedure where intra- and inter-observer variations cannot be avoided.
Omar M. Al Okashi, Hongbo Du, and Hisham Al-Assam, "Automatic pelvis segmentation from x-ray images of a mouse model," Proc. SPIE 10221, Mobile Multimedia/Image Processing, Security, and Applications 2017, 1022108 (Presented at SPIE Commercial + Scientific Sensing and Imaging: April 10, 2017; Published: 10 May 2017); https://doi.org/10.1117/12.2264803.
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