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Semi-automatic Segmentation of MRI Brain Metastases Combining Support Vector Machine and Morphological Operators

Topics: Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World Applications, Financial Applications, Neural Prostheses and Medical Applications, Neural Based Data Mining and Complex Information Process; Support Vector Machines and Kernel Methods

Authors: Gloria Gonella 1 ; Elisabetta Binaghi 1 ; Paola Nocera 2 and Cinzia Mordacchini 3

Affiliations: 1 Department of Theorical and Applied Science, Insubria University, Varese and Italy ; 2 C. S. Health Physics, ASST dei Sette Laghi, Varese, Italy, Department of Physics, University of Milan, Milan and Italy ; 3 C. S. Health Physics, ASST dei Sette Laghi, Varese and Italy

Keyword(s): MRI Brain Tumour Segmentation, Support Vector Machine, Morphological Operators.

Abstract: The objective of this study is to develop a semi-automatic, interactive segmentation strategy for efficient and accurate brain metastases delineation on Post Gadolinium T1-weighted brain MRI images. Salient aspects of the proposed solutions are the combined use of machine learning and image processing techniques, based on Support Vector Machine and Morphological Operators respectively, to delineate pathological and healthy tissues. The overall segmentation procedure is designed to operate on a clinical setting to reduce the workload of health-care professionals but leaving to them full control of the process. The segmentation process was validated for in-house collected image data obtained from radiation therapy studies. The results prove that the allied use of SVM and Morphological Operators produces accurate segmentations, useful for their insertion in clinical practice.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Gonella, G.; Binaghi, E.; Nocera, P. and Mordacchini, C. (2019). Semi-automatic Segmentation of MRI Brain Metastases Combining Support Vector Machine and Morphological Operators. In Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - NCTA; ISBN 978-989-758-384-1; ISSN 2184-3236, SciTePress, pages 457-463. DOI: 10.5220/0008019304570463

@conference{ncta19,
author={Gloria Gonella. and Elisabetta Binaghi. and Paola Nocera. and Cinzia Mordacchini.},
title={Semi-automatic Segmentation of MRI Brain Metastases Combining Support Vector Machine and Morphological Operators},
booktitle={Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - NCTA},
year={2019},
pages={457-463},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008019304570463},
isbn={978-989-758-384-1},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - NCTA
TI - Semi-automatic Segmentation of MRI Brain Metastases Combining Support Vector Machine and Morphological Operators
SN - 978-989-758-384-1
IS - 2184-3236
AU - Gonella, G.
AU - Binaghi, E.
AU - Nocera, P.
AU - Mordacchini, C.
PY - 2019
SP - 457
EP - 463
DO - 10.5220/0008019304570463
PB - SciTePress

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