Abstract
Neurosurgeons non-invasively use Magnetic Resonance Imaging (MRI) as guidance to plan for laser treatment. Such a technique is mainly used for treating brain cancer cells, i.e., a brain tumor. In this paper, a novel approach is being implemented that may provide computational guidance for laser treatment planning. The approach applies a linear pathfinding algorithm on a MRI-Slice. A linear path’s cost is calculated based on certain reasonable assumption and best of these path are suggested to the treatment planner. The preliminary results display that our approach can locate those voxels that are assigned tumor designation based on simple thresholding technique on voxel values of the MRI-Slice data. These lines are calculated and drawn over the MRI image to indicate the best spot. We evaluate the preliminary results using MRI software tools such as 3D Slicer and ImageJ/Fiji. The evaluation shows that our approach could provide a cost based linear path guidance mimicking the cost of applying laser treatment along the same path. Our algorithm therefore could provide computational guidance for a laser treatment planner.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
National Institutes of Health. https://www.cancer.gov/about-cancer/treatment/types/surgery/lasers
Larobina, M., Murino, L.: Medical image file formats. J. Digit. Imag. 27(2), 200–206 (2014). https://doi.org/10.1007/s10278-013-9657-9
DICOM Image Library. https://www.osirix-viewer.com/resources/dicom-image-library/
OpenNeuro. https://openneuro.org/datasets/ds000114/versions/1.0.1
Cancer Imaging Archive. https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=50135264
BraTS2020 Dataset. https://www.kaggle.com/awsaf49/brats20-dataset-training-validation
Van Der Walt, S., Colbert, S.C., Varoquaux, G.: The NumPy array: a structure for efficient numerical computation. Comput. Sci. Eng. 13(2), 22–30 (2011)
Ari, N., Ustazhanov, M.: Matplotlib in Python. In: 11th International Conference on Electronics, Computer and Computation (ICECCO), pp. 1–6. IEEE (2014)
Zhang, X., Huang, J., Yang, Y., He, X., Liu, R., Zhong, N.: Applying Python in brain science education. In: International Joint Conference on Information, Media and Engineering (IJCIME), pp. 396–400. IEEE (2019)
Ice-Free, A.: PYTHON: BATTERIES INCLUDED (2007)
Boulogne, F., Warner, J.D., Neil Yager, E.: Scikit-image: image processing in Python. J. PeerJ 2, 453 (2014)
Zingl, A.: A rasterizing algorithm for drawing curves (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bahkali, I.M., Semwal, S.K. (2022). A Linear Pathfinding Algorithm for Planning Laser Treatment. In: Arai, K. (eds) Advances in Information and Communication. FICC 2022. Lecture Notes in Networks and Systems, vol 438. Springer, Cham. https://doi.org/10.1007/978-3-030-98012-2_47
Download citation
DOI: https://doi.org/10.1007/978-3-030-98012-2_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-98011-5
Online ISBN: 978-3-030-98012-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)