Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Precise Computational Method for Hippocampus Segmentation from MRI of Brain to Assist Physicians in the Diagnosis of Alzheimer's Disease Subject The topic of the resource active contour; binary pattern; Hippocampus; segmentation; Weber's law Description An account of the resource 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. Creator An entity primarily responsible for making the resource Genish T.; Kavitha S.; Vijayalakshmi S. Source A related resource from which the described resource is derived International Journal of Computational Intelligence and Applications, Vol-21, No. 3 Publisher An entity responsible for making the resource available World Scientific Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1142/S1469026822500201" target="_blank" rel="noreferrer noopener">https://doi.org/10.1142/S1469026822500201</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140237668&amp;doi=10.1142%2FS1469026822500201&amp;partnerID=40&amp;md5=91e9b805abc43c5dac4322ac21c1a2f3" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140237668&amp;doi=10.1142%2fS1469026822500201&amp;partnerID=40&amp;md5=91e9b805abc43c5dac4322ac21c1a2f3</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 14690268 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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