An Effective Brain Tumor Detection from T1w MR Images Using Active Contour Segmentation Techniques

In the medical imaging application, early detection of tumors from magnetic resonance imaging (MRI) brain images is a challenging task, it reduces the risk of human health and increases the chance of survival. The manual detection of brain tissue was requiring a lot of experience and time-consuming....

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Bibliographic Details
Published in:Journal of physics. Conference series 2021-02, Vol.1804 (1), p.12174
Main Authors: Babu, KRajesh, Ksony, Indira, NDurga, Vara Prasad, K, Shameem, Syed
Format: Article
Language:English
Subjects:
Algorithms
Brain
Brain cancer
Brain research
Clustering
Datasets
Image processing
Image segmentation
Magnetic resonance imaging
Medical imaging
Medical research
Metric space
Physics
Tumors
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Summary:In the medical imaging application, early detection of tumors from magnetic resonance imaging (MRI) brain images is a challenging task, it reduces the risk of human health and increases the chance of survival. The manual detection of brain tissue was requiring a lot of experience and time-consuming. Magnetic resonance images (MRI) scan helps to detect brain tumors in the early stage, and it has become a hot research topic in medical image processing. In this research article, an automated framework is presented intended to segment and extraction of brain tumors of MRI images in an efficient manner. In this work, three primary steps are involved in the suggested brain tumor segmentation system including pre-processing, image thresholding, and segmentation. This article focusses on to study the accurate brain tumor segmentation system by comparing the level set (LSM) and Chan-vese (C-V) techniques. All the experiments are conducted on Harvard datasets to validate the performance of both methods. The performance of the algorithms was calculated in terms of dice coefficient (DC), Hausdorff distance (HD), and Jaccard similarity index (JSI). It has been observed that KIFCM has performed better than the other clustering methods and Chan-Vase has shown better performance in tumor detection on selected datasets. The overall work shows that the presented framework outer performs for the detection of brain tumors with minimum distance error and loss of information over existingmethods.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1804/1/012174