Probabilistic hierarchical clustering based identification and segmentation of brain tumors in magnetic resonance imaging
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Ankit Vidyarthi
Abstract
The automatic segmentation of the abnormality region from the head MRI is a challenging task in the medical science domain. The abnormality in the form of the tumor comprises the uncontrolled growth of the cells. The automatic identification of the affected cells using computerized software systems is demanding in the past several years to provide a second opinion to radiologists. In this paper, a new clustering approach is introduced based on the machine learning aspect that clusters the tumor region from the input MRI using disjoint tree generation followed by tree merging. Further, the proposed algorithm is improved by introducing the theory of joint probabilities and nearest neighbors. Later, the proposed algorithm is automated to find the number of clusters required with its nearest neighbors to do semantic segmentation of the tumor cells. The proposed algorithm provides good semantic segmentation results having the DB index-0.11 and Dunn index-13.18 on the SMS dataset. While the experimentation with BRATS 2015 dataset yields Dicecomplete=80.5 %, Dicecore=73.2 %, and Diceenhanced=62.8 %. The comparative analysis of the proposed approach with benchmark models and algorithms proves the model’s significance and its applicability to do semantic segmentation of the tumor cells with the average increment in the accuracy of around ±2.5 % with machine learning algorithms.
Acknowledgments
We are thankful to the Sawai Man Singh (SMS) Medical College Jaipur for providing us the original brain tumor images. We would also like to thanks Dr. Sunil Jakhar, MD, Radio Diagnosis, Department of Radiology, SMS Medical College Jaipur for helping us in verifying the results.
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Research ethics: The local Institutional Review Board deemed the study exempt from review.
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Informed consent: Not applicable.
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Author contributions: The work is done by sole author ankit vidyarthi who have accepted responsibility for the entire content of this manuscript and approved its submission. The complete work including experimentation, modelling, and write up is done by Ankit Vidyarthi.
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Competing interests: Authors state no conflict of interest.
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Research funding: No funding available.
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Data availability: The raw data can be obtained on request from the corresponding author.
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© 2023 Walter de Gruyter GmbH, Berlin/Boston
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Articles in the same Issue
- Frontmatter
- Review
- Epileptic EEG patterns recognition through machine learning techniques and relevant time–frequency features
- Research Articles
- Hybrid optimization assisted channel selection of EEG for deep learning model-based classification of motor imagery task
- Development of audio-guided deep breathing and auditory Go/No-Go task on evaluating its impact on the wellness of young adults: a pilot study
- Detection of driver drowsiness level using a hybrid learning model based on ECG signals
- A portable household detection system based on the combination of bidirectional LSTM and residual block for automatical arrhythmia detection
- Probabilistic hierarchical clustering based identification and segmentation of brain tumors in magnetic resonance imaging
- Biomechanical comparison of three fixation strategies for radial head fractures: a biomechanical study
- Computer-based analysis of the taper connection strength of different revision head and adapter sleeve designs
- CT-based evaluation of tissue expansion in cryoablation of ex vivo kidney
