Chapter title |
A Fast Approach to Automatic Detection of Brain Lesions
|
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Chapter number | 6 |
Book title |
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
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Published in |
Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries : second International Workshop, BrainLes 2016, with the challenges on BRATS, ISLES and mTOP 2016, held in conjunction with MICCAI 2016, Athens, Greece, Octob..., October 2016
|
DOI | 10.1007/978-3-319-55524-9_6 |
Pubmed ID | |
Book ISBNs |
978-3-31-955523-2, 978-3-31-955524-9
|
Authors |
Subhranil Koley, Chandan Chakraborty, Caterina Mainero, Bruce Fischl, Iman Aganj |
Abstract |
Template matching is a popular approach to computer-aided detection of brain lesions from magnetic resonance (MR) images. The outcomes are often sufficient for localizing lesions and assisting clinicians in diagnosis. However, processing large MR volumes with three-dimensional (3D) templates is demanding in terms of computational resources, hence the importance of the reduction of computational complexity of template matching, particularly in situations in which time is crucial (e.g. emergent stroke). In view of this, we make use of 3D Gaussian templates with varying radii and propose a new method to compute the normalized cross-correlation coefficient as a similarity metric between the MR volume and the template to detect brain lesions. Contrary to the conventional fast Fourier transform (FFT) based approach, whose runtime grows as O(N logN) with the number of voxels, the proposed method computes the cross-correlation in O(N). We show through our experiments that the proposed method outperforms the FFT approach in terms of computational time, and retains comparable accuracy. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 8 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 3 | 38% |
Student > Master | 2 | 25% |
Student > Postgraduate | 1 | 13% |
Unknown | 2 | 25% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 1 | 13% |
Psychology | 1 | 13% |
Energy | 1 | 13% |
Unknown | 5 | 63% |