Automatic Brain Tumor Segmentation and Detection with Histogram Equalization of Morphological Image Processing

DOI

10.2991/978-94-6463-662-8_28How to use a DOI?

Keywords

FCM (Fuzzy C-Means algorithm); Brain Tumour; Magnetic Resonance Imaging (MRI); Computed Tomography (CT); Histogram Equalization

Abstract

Nowadays, one of the primary causes of increasing adult and kids death rate is brain tumors. Most worldwide research has found that throughout the previous few decades, the number of people suffering from and dying from brain tumours has increased to 300 each year. Early brain tumour detection is still a difficult problem. An uncontrollably growing mass of cells is called a brain tumour. Malignant or cancerous tumours and benign tumours are the two primary categories of tumours. When diagnosing brain tumours, medical imaging is essential. Medical image data gathered from many biomedical devices is an essential component of medical diagnosis. This information was gathered using a number of imaging methods, including CT (Computed Tomography), Magnetic resonance imaging (MRI) and X-rays. Tumour segmentation is a difficult process that medical experts carry out using data from MRI. Segmenting brain tumours from complex MRI brain images is an important way to extract information. In this system, a tumour in a brain MRI is segmented using a new FCM formulation technique (Fuzzy C Means algorithm). Therefore, by using this system brain tumour is detected at initially and accurately. Hence, automatic segmentation and detection of brain tumours using histogram equalization in morphological image processing provides better results in terms of F1-score, precision and accuracy.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - P. Sunanda
AU  - K. Asha Rani
PY  - 2025
DA  - 2025/03/17
TI  - Automatic Brain Tumor Segmentation and Detection with Histogram Equalization of Morphological Image Processing
BT  - Proceedings of the International Conference on Advanced Materials, Manufacturing and Sustainable Development (ICAMMSD 2024)
PB  - Atlantis Press
SP  - 343
EP  - 355
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-662-8_28
DO  - 10.2991/978-94-6463-662-8_28
ID  - Sunanda2025
ER  -