A Precise Computational Method for Hippocampus Segmentation from MRI of Brain to Assist Physicians in the Diagnosis of Alzheimer's Disease
- Title
- A Precise Computational Method for Hippocampus Segmentation from MRI of Brain to Assist Physicians in the Diagnosis of Alzheimer's Disease
- Creator
- Genish T.; Kavitha S.; Vijayalakshmi S.
- Description
- Hippocampus segmentation on magnetic resonance imaging is more significant for diagnosis, treatment and analyzing of neuropsychiatric disorders. Automatic segmentation is an active research field. Previous state-of-the-art hippocampus segmentation methods train their methods on healthy or Alzheimer's disease patients from public datasets. It arises the question whether these methods are capable for recognizing the hippocampus in a different domain. Therefore, this study proposes a precise computational method for hippocampus segmentation from MRI of brain to assist physicians in the diagnosis of Alzheimer's disease (HCS-MRI-DAD-LBP). Initially, the input images are pre-processed by Trimmed mean filter for image quality enhancement. Then the pre-processed images are given to ROI detection, ROI detection utilizes Weber's law which determines the luminance factor of the image. In the region extraction process, Chan-Vese active contour model (ACM) and level sets are used (UACM). Finally, local binary pattern (LBP) is utilized to remove the erroneous pixel that maximizes the segmentation accuracy. The proposed model is implemented in MATLAB, and its performance is analyzed with performance metrics, like precision, recall, mean, variance, standard deviation and disc similarity coefficient. The proposed HCS-MRI-DAD-LBP method attains in OASIS dataset provides high disc similarity coefficient of 12.64%, 10.11% and 1.03% compared with the existing methods, like HCS-DAS-MLT, HCS-DAS-RNN and HCS-DAS-GMM and in ADNI dataset provides high precision of 20%, 9.09% and 1.05% compared with existing methods like HCS-MRI-DAD-CNN-ADNI, HCS-MRI-DAD-MCNN-ADNI and HCS-MRI-DAD-CNN-RNN-ADNI, respectively. 2022 World Scientific Publishing Europe Ltd.
- Source
- International Journal of Computational Intelligence and Applications, Vol-21, No. 3
- Date
- 2022-01-01
- Publisher
- World Scientific
- Subject
- active contour; binary pattern; Hippocampus; segmentation; Weber's law
- Coverage
- Genish T., School of Computing Science, KPR College of Arts Science and Research, Avinashi Road, Coimbatore, India; Kavitha S., PG and Research, Department of Computer Science, Sakthi College of Arts and Science for Women, Dindigul, Oddanchatram, India; Vijayalakshmi S., Department of Data Science, CHRIST (Deemed to be University), Lavasa Campus, Pune, India
- Rights
- Restricted Access
- Relation
- ISSN: 14690268
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Genish T.; Kavitha S.; Vijayalakshmi S., “A Precise Computational Method for Hippocampus Segmentation from MRI of Brain to Assist Physicians in the Diagnosis of Alzheimer's Disease,” CHRIST (Deemed To Be University) Institutional Repository, accessed April 21, 2025, https://archives.christuniversity.in/items/show/14888.