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Journal of Medical Systems

, 43:282 | Cite as

Brain Tumor Detection and Segmentation by Intensity Adjustment

  • P. G. RajanEmail author
  • C. Sundar
Patient Facing Systems
Part of the following topical collections:
  1. Wearable Computing Techniques for Smart Health

Abstract

In recent years, Brain tumor detection and segmentation has created an interest on research areas. The process of identifying and segmenting brain tumor is a very tedious and time consuming task, since human physique has anatomical structure naturally. Magnetic Resonance Image (MRI) scan analysis is a powerful tool that makes effective detection of the abnormal tissues from the brain. Among different techniques, Magnetic Resonance Image (MRI) is a liable one which contains several modalities in scanning the images captured from interior structure of human brain. A novel hybrid energy-efficient method is proposed for automatic tumor detection and segmentation. The proposed system follows K-means clustering, integrated with Fuzzy C-Means (KMFCM) and active contour by level set for tumor segmentation. An effective segmentation, edge detection and intensity enhancement can detect brain tumor easily. For that, active contour with level set method has been utilized. The performance of the proposed approach has been evaluated in terms of white pixels, black pixels, tumor detected area, and the processing time. This technique can deal with a higher number of segmentation problem and minimum execution time by ensuring segmentation quality. Additionally, tumor area length in vertical and horizontal positions is determined to measure sensitivity, specificity, accuracy, and similarity index values. Further, tumor volume is computed. Knowledge of the information of tumor is helpful for the physicians for effective diagnosing in tumor for treatments. The entire experimentation was implemented in MATLAB environment and simulation results were compared with existing approaches.

Keywords

MRI brain tumor K-means clustering Fuzzy C-means Active contour by level set Edge detection and intensity adjustment 

Notes

Compliance with Ethical Standards

Disclosure of Potential Conflicts of Interest

No conflicts of interest: Author 1 & 2 declares that they have no conflict of interest.

Research Involving Human Participants and/or Animals

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008″.

Informed Consent

Informed consent was obtained from all patients for being included in the study.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Christian College of Engineering and TechnologyOddanchatramIndia

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