Brain tumor segmentation and detection using MRI images
- Title
- Brain tumor segmentation and detection using MRI images
- Creator
- Thomas B.; Nizar Banu P.K.
- Description
- Brain tumor is caused due to the increased abnormal in brain. It is not something that we might say is limited to aged people alone, but is known to affect newborn babies as well. It affects many people worldwide. With the applications of Machine Learning (ML) and Image Processing (IP), the early detection of brain tumor is possible. In this research work, the different stages in image processing which help to detect brain tumor, is addressed vividly. This work provides information about the various sets of filtering and segmentation methods which can be used to detect whether it is brain tumor or not. All of the filtering methods are defined in image preprocessing techniques. The next procedure is to apply segmentation methods namely watershed segmentation and gray level threshold segmentation. After this, certain features are considered for feature extraction such as area, major axis, minor axis and eccentricity. According to the outcomes from the feature extraction technique, the classification of the tumor is done. In this paper, we achieve an accuracy of 92.35 by using K-Nearest Neighbor (KNN) algorithm. IAEME Publication.
- Source
- International Journal of Mechanical Engineering and Technology, Vol-9, No. 5, pp. 514-523.
- Date
- 2018-01-01
- Publisher
- IAEME Publication
- Subject
- Brain Tumor; Canny Edge Detection.; Gaussian Filter; Gray-level threshold Segmentation; Image Processing; Median Filter; MRI; Watershed Segmentation
- Coverage
- Thomas B., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Nizar Banu P.K., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISSN: 9766340
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Thomas B.; Nizar Banu P.K., “Brain tumor segmentation and detection using MRI images,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/16998.