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Brain tumor segmentation from MRI using FCM clustering, morphological reconstruction, and active contour

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Abstract

Brain tumors are a leading cause of death in humans of various ages, making early detection and treatment crucial for improving patient outcomes. It is important to accurately determine the precise location, size, and dimensions of the tumor for successful radiotherapy. One reliable method for diagnosing brain tumors is Magnetic Resonance Imaging (MRI), as it can detect small, non-invasive lesions in the brain with great clarity and contrast. However, manual segmentation of tumors on MRI images is time-consuming, despite its accuracy. To address this challenge, computerized techniques can provide more precise and extensive results in less time. In this article, we propose a three-part method for segmenting tumors on MRI images. In the pre-processing stage, the contrast of the image is improved by matching the histogram, removing noise, and sharpening the image. In the next step, the tumor-related cluster is identified using fuzzy clustering. In the post-processing stage, the tumor areas are delineated using morphological reconstruction and active contour techniques. The proposed approach is evaluated on the training portions of two datasets: BraTS 2012 and BraTS 2013. This approach has shown robustness against noise, and intensity non-uniformity in experiments. Additionally, it is quick and more precise than other state-of-the-art segmentation methods, with an average running time of 2.33 s. Additionally, the average segmentation Sensitivity, Dice, Precision, Accuracy, Jaccard, and Hausdorff distance are 92.10%, 0.92, 92.05%, and 99.06%, 0.86, 3.60, respectively. The proposed method demonstrates satisfactory results for Glioma brain tumor segmentation due to fuzzy c-means clustering, morphological reconstruction, and active contour accurate segmentation results and short time. Since most medical images suffer from these issues, this method has the potential to be more effective in the segmentation of complex medical images.

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Data availability

The datasets analysed during the current study are available in the BraTS repository, https://www.smir.ch/BRATS/Start2012, https://www.smir.ch/BRATS/Start2013

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Authors and Affiliations

  1. School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran

    Motahare Shekari

  2. School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran

    Milad Rostamian

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Correspondence to Milad Rostamian.

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Shekari, M., Rostamian, M. Brain tumor segmentation from MRI using FCM clustering, morphological reconstruction, and active contour. Multimed Tools Appl 83, 42973–42998 (2024). https://doi.org/10.1007/s11042-023-17233-5

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