کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4969843 | 1449984 | 2017 | 19 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Supervoxel classification forests for estimating pairwise image correspondences
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
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چکیده انگلیسی
This article presents a general method for estimating pairwise image correspondences, which is a fundamental problem in image analysis. The method consists of over-segmenting a pair of images into supervoxels. A forest classifier is then trained on one of the images, the source, by using supervoxel indices as voxel-wise class labels. Applying the forest on the other image, the target, yields a supervoxel labelling, which is then regularised using majority voting within the boundaries of the target's supervoxels. This yields semi-dense correspondences in a fully automatic, unsupervised, efficient and robust manner. The advantage of our approach is that no prior information or manual annotations are required, making it suitable as a general initialisation component for various medical imaging tasks that require coarse correspondences, such as atlas/patch-based segmentation, registration, and atlas construction. We demonstrate the effectiveness of our approach in two different applications: a) initialisation of longitudinal registration on spine CT data of 96 patients, and b) atlas-based image segmentation using 150 abdominal CT images. Comparison to state-of-the-art methods demonstrate the potential of supervoxel classification forests for estimating image correspondences.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 63, March 2017, Pages 561-569
Journal: Pattern Recognition - Volume 63, March 2017, Pages 561-569
نویسندگان
Fahdi Kanavati, Tong Tong, Kazunari Misawa, Michitaka Fujiwara, Kensaku Mori, Daniel Rueckert, Ben Glocker,