Design and implementation of a new cooperative approach to brain tumour identification from MRI images

Beddad, Boucif; Hachemi, Kaddour; Vaidyanathan, Sundarapandian

doi:10.1504/ijcat.2019.097113

Cited by 2 publications

References 18 publications

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

Babu

Ksony

Indira

et al. 2021

J. Phys.: Conf. Ser.

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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.

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