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. 2018 May 5;18(5):1441. doi: 10.3390/s18051441

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© 2018 by the authors.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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Steps of the segmentation algorithm for both multispectral imaging systems. (a) Example of how the segmentation is done for a lesion taken with the exNIR system: (1) Reflectance image taken with the exNIR device. (2) Reflectance image taken with the VIS-NIR camera. (3) Reflectance images from both cameras superimposed. Here, it can be seen how both systems take images at different resolutions, rotated with respect to each other a certain angle. (4) Reflectance image of the exNIR imaging system. (5) Reflectance image from the VIS-NIR system, to which a mathematical operation for correlation has been applied. (6) These last images were superimposed. (b) Example of how the segmentation is done for a lesion taken with the VIS-NIR imaging system: images correspond to the implementation of the Otsu method for each subimage. A mask for each of them is calculated in (1–4), and they are put together to form the (5) final mask for segmentation. (6) is the result of segmenting the lesion.

Steps of the segmentation algorithm for both multispectral imaging systems. (a) Example of how the segmentation is done for a lesion taken with the exNIR system: (1) Reflectance image taken with the exNIR device. (2) Reflectance image taken with the VIS-NIR camera. (3) Reflectance images from both cameras superimposed. Here, it can be seen how both systems take images at different resolutions, rotated with respect to each other a certain angle. (4) Reflectance image of the exNIR imaging system. (5) Reflectance image from the VIS-NIR system, to which a mathematical operation for correlation has been applied. (6) These last images were superimposed. (b) Example of how the segmentation is done for a lesion taken with the VIS-NIR imaging system: images correspond to the implementation of the Otsu method for each subimage. A mask for each of them is calculated in (1–4), and they are put together to form the (5) final mask for segmentation. (6) is the result of segmenting the lesion.