Abstract
In recent decades, human brain tumor detection has become one of the most challenging issues in medical science. In this paper, we propose a model that includes the template-based K means and improved fuzzy C means (TKFCM) algorithm for detecting human brain tumors in a magnetic resonance imaging (MRI) image. In this proposed algorithm, firstly, the template-based K-means algorithm is used to initialize segmentation significantly through the perfect selection of a template, based on gray-level intensity of image; secondly, the updated membership is determined by the distances from cluster centroid to cluster data points using the fuzzy C-means (FCM) algorithm while it contacts its best result, and finally, the improved FCM clustering algorithm is used for detecting tumor position by updating membership function that is obtained based on the different features of tumor image including Contrast, Energy, Dissimilarity, Homogeneity, Entropy, and Correlation. Simulation results show that the proposed algorithm achieves better detection of abnormal and normal tissues in the human brain under small detachment of gray-level intensity. In addition, this algorithm detects human brain tumors within a very short time—in seconds compared to minutes with other algorithms.
Highlights
Digital image processing (DIP) is an emerging field in biological sciences such as tumor detection and classification, cancer detection and classification, and testing and examining critical parts of the human body
The processes of tumor detection should be done with very high speed and accuracy. This is only possible by using magnetic resonance (MR) images, and suspicious regions are extracted through MR image segmentation from complex medical images
MR image pre-processing is very significant to ameliorate the visual effect of the image for further processing
Summary
Digital image processing (DIP) is an emerging field in biological sciences such as tumor detection and classification, cancer detection and classification, and testing and examining critical parts of the human body. The processes of tumor detection should be done with very high speed and accuracy This is only possible by using magnetic resonance (MR) images, and suspicious regions are extracted through MR image segmentation from complex medical images. The result of tumor detection may vary under different circumstances by the same physician, and the brightness and contrast of the display screen can vary the segmentation results For these reasons, the automatic detection of brain tumors becomes significant. Several research works are attempting to detect brain tumors automatically with improved accuracy, exactness, and speed of computation by minimizing manual effort [9]. The detection of brain tumors means identifying the affected part of the brain and to the tumor shape, size, boundary, and position Different imaging technologies such as magnetic resonance image (MRI), computed tomography (CT), positron emission tomography (PET), etc.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
