{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T08:09:45Z","timestamp":1783066185981,"version":"3.54.6"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030158866","type":"print"},{"value":"9783030158873","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-15887-3_20","type":"book-chapter","created":{"date-parts":[[2019,7,19]],"date-time":"2019-07-19T14:02:57Z","timestamp":1563544977000},"page":"421-429","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Segmentation-Less Efficient Alzheimer Detection Approach Using Hybrid Image Features"],"prefix":"10.1007","author":[{"given":"Sitara","family":"Afzal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mubashir","family":"Javed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muazzam","family":"Maqsood","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farhan","family":"Aadil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seungmin","family":"Rho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Irfan","family":"Mehmood","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,7,20]]},"reference":[{"issue":"4","key":"20_CR1","doi-asserted-by":"publisher","first-page":"1571","DOI":"10.3233\/JAD-160850","volume":"55","author":"Iman Beheshti","year":"2016","unstructured":"Beheshti, I., et al., Histogram-Based Feature Extraction from Individual Gray Matter Similarity-Matrix for Alzheimer\u2019s Disease Classification. Journal of Alzheimer\u2019s Disease, 2017. 55(4): p. 1571-1582.","journal-title":"Journal of Alzheimer's Disease"},{"issue":"1","key":"20_CR2","doi-asserted-by":"publisher","first-page":"233","DOI":"10.3233\/JAD-150848","volume":"50","author":"Shuihua Wang","year":"2015","unstructured":"Wang, S., et al., Detection of Alzheimer\u2019s disease by three-dimensional displacement field estimation in structural magnetic resonance imaging. Journal of Alzheimer\u2019s Disease, 2016. 50(1): p. 233-248.","journal-title":"Journal of Alzheimer's Disease"},{"issue":"s4","key":"20_CR3","doi-asserted-by":"publisher","first-page":"S375","DOI":"10.3233\/JAD-141470","volume":"42","author":"Sylvie Belleville","year":"2014","unstructured":"Belleville, S., et al., Detecting early preclinical Alzheimer\u2019s disease via cognition, neuropsychiatry, and neuroimaging: qualitative review and recommendations for testing. Journal of Alzheimer\u2019s disease, 2014. 42(s4): p. S375-S382.","journal-title":"Journal of Alzheimer's Disease"},{"issue":"3","key":"20_CR4","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/j.mri.2015.11.009","volume":"34","author":"Iman Beheshti","year":"2016","unstructured":"Beheshti, I., H. Demirel, and A.s.D.N. Initiative, Feature-ranking-based Alzheimer\u2019s disease classification from structural MRI. Magnetic resonance imaging, 2016. 34(3): p. 252-263.","journal-title":"Magnetic Resonance Imaging"},{"key":"20_CR5","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.bspc.2015.05.014","volume":"21","author":"Yudong Zhang","year":"2015","unstructured":"Zhang, Y., et al., Detection of Alzheimer\u2019s disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC. Biomedical Signal Processing and Control, 2015. 21: p. 58-73.","journal-title":"Biomedical Signal Processing and Control"},{"key":"20_CR6","unstructured":"Altaf, T., et al. Multi-class Alzheimer disease classification using hybrid features. in IEEE Future Technologies Conference. 2017."},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al. Discriminative MR image feature analysis for automatic schizophrenia and Alzheimer\u2019s disease classification. in International conference on medical image computing and computer-assisted intervention. 2004. Springer.","DOI":"10.1007\/978-3-540-30135-6_48"},{"issue":"1","key":"20_CR8","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.neuroimage.2003.09.027","volume":"21","author":"Zhiqiang Lao","year":"2004","unstructured":"Lao, Z., et al., Morphological classification of brains via high-dimensional shape transformations and machine learning methods. Neuroimage, 2004. 21(1): p. 46-57.","journal-title":"NeuroImage"},{"issue":"2","key":"20_CR9","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/s10115-006-0043-5","volume":"11","author":"Glenn Fung","year":"2006","unstructured":"Fung, G. and J. Stoeckel, SVM feature selection for classification of SPECT images of Alzheimer\u2019s disease using spatial information. Knowledge and Information Systems, 2007. 11(2): p. 243-258.","journal-title":"Knowledge and Information Systems"},{"issue":"3","key":"20_CR10","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1093\/brain\/awm319","volume":"131","author":"S. Kloppel","year":"2008","unstructured":"Kl\u00f6ppel, S., et al., Automatic classification of MR scans in Alzheimer\u2019s disease. Brain, 2008. 131(3): p. 681-689.","journal-title":"Brain"},{"issue":"2","key":"20_CR11","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1016\/j.neuroimage.2011.05.083","volume":"58","author":"Andrea Chincarini","year":"2011","unstructured":"Chincarini, A., et al., Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer\u2019s disease. Neuroimage, 2011. 58(2): p. 469-480.","journal-title":"NeuroImage"},{"issue":"7","key":"20_CR12","doi-asserted-by":"publisher","first-page":"e22506","DOI":"10.1371\/journal.pone.0022506","volume":"6","author":"Eric Westman","year":"2011","unstructured":"Westman, E., et al., Sensitivity and specificity of medial temporal lobe visual ratings and multivariate regional MRI classification in Alzheimer\u2019s disease. PloS one, 2011. 6(7): p. e22506.","journal-title":"PLoS ONE"},{"key":"20_CR13","doi-asserted-by":"crossref","unstructured":"Ahmed, O.B., et al., Classification of Alzheimer\u2019s disease subjects from MRI using hippocampal visual features. Multimedia Tools and Applications, 2015. 74(4): p. 1249-1266.","DOI":"10.1007\/s11042-014-2123-y"},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"Vemuri, P., D.T. Jones, and C.R. Jack, Resting state functional MRI in Alzheimer\u2019s Disease. Alzheimer\u2019s research & therapy, 2012. 4(1): p. 2.","DOI":"10.1186\/alzrt100"},{"issue":"2","key":"20_CR15","doi-asserted-by":"publisher","first-page":"488","DOI":"10.1016\/j.neuroimage.2006.11.042","volume":"35","author":"Yong He","year":"2007","unstructured":"He, Y., et al., Regional coherence changes in the early stages of Alzheimer\u2019s disease: a combined structural and resting-state functional MRI study. Neuroimage, 2007. 35(2): p. 488-500.","journal-title":"NeuroImage"},{"key":"20_CR16","doi-asserted-by":"crossref","unstructured":"Tripoliti, E.E., D.I. Fotiadis, and M. Argyropoulou. A supervised method to assist the diagnosis and classification of the status of Alzheimer\u2019s disease using data from an fMRI experiment. in Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE. 2008. IEEE.","DOI":"10.1109\/IEMBS.2008.4650191"},{"key":"20_CR17","doi-asserted-by":"crossref","unstructured":"Ateeq, T., et al., Ensemble-classifiers-assisted detection of cerebral microbleeds in brain MRI. Computers & Electrical Engineering, 2018.","DOI":"10.1016\/j.compeleceng.2018.02.021"},{"key":"20_CR18","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.compbiomed.2017.02.011","volume":"83","author":"Iman Beheshti","year":"2017","unstructured":"Beheshti, I., et al., Classification of Alzheimer\u2019s disease and prediction of mild cognitive impairment-to-Alzheimer\u2019s conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm. Computers in biology and medicine, 2017. 83: p. 109-119.","journal-title":"Computers in Biology and Medicine"},{"key":"20_CR19","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/j.cmpb.2016.09.019","volume":"137","author":"Iman Beheshti","year":"2016","unstructured":"Beheshti, I., et al., Structural MRI-based detection of Alzheimer\u2019s disease using feature ranking and classification error. Computer methods and programs in biomedicine, 2016. 137: p. 177-193.","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"20_CR20","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.eswa.2016.04.029","volume":"59","author":"Anandh Kilpattu Ramaniharan","year":"2016","unstructured":"Ramaniharan, A.K., S.C. Manoharan, and R. Swaminathan, Laplace Beltrami eigen value based classification of normal and Alzheimer MR images using parametric and non-parametric classifiers. Expert Systems with Applications, 2016. 59: p. 208-216.","journal-title":"Expert Systems with Applications"},{"key":"20_CR21","doi-asserted-by":"crossref","unstructured":"Guerrero, R., et al., Manifold population modeling as a neuro-imaging biomarker: application to ADNI and ADNI-GO. NeuroImage, 2014. 94: p. 275-286.","DOI":"10.1016\/j.neuroimage.2014.03.036"},{"key":"20_CR22","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.cmpb.2016.05.009","volume":"133","author":"Maciej Plocharski","year":"2016","unstructured":"Plocharski, M., L.R. \u00d8stergaard, and A.s.D.N. Initiative, Extraction of sulcal medial surface and classification of Alzheimer\u2019s disease using sulcal features. Computer methods and programs in biomedicine, 2016. 133: p. 35-44.","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"20_CR23","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1016\/j.nicl.2016.11.025","volume":"13","author":"Lauge S\u00f8rensen","year":"2017","unstructured":"S\u00f8rensen, L., et al., Differential diagnosis of mild cognitive impairment and Alzheimer\u2019s disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry. NeuroImage: Clinical, 2017. 13: p. 470-482.","journal-title":"NeuroImage: Clinical"},{"key":"20_CR24","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.compmedimag.2015.04.007","volume":"44","author":"Olfa Ben Ahmed","year":"2015","unstructured":"Ahmed, O.B., et al., Alzheimer\u2019s disease diagnosis on structural MR images using circular harmonic functions descriptors on hippocampus and posterior cingulate cortex. Computerized Medical Imaging and Graphics, 2015. 44: p. 13-25.","journal-title":"Computerized Medical Imaging and Graphics"},{"key":"20_CR25","doi-asserted-by":"crossref","unstructured":"Sarraf, S. and G. Tofighi, DeepAD: Alzheimer\u2019s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI. bioRxiv, 2016: p. 070441.","DOI":"10.1101\/070441"},{"key":"20_CR26","unstructured":"Payan, A. and G. Montana, Predicting Alzheimer\u2019s disease: a neuroimaging study with 3D convolutional neural networks. arXiv preprint arXiv:1502.02506, 2015."},{"key":"20_CR27","doi-asserted-by":"crossref","unstructured":"Farooq, A., et al. Artificial intelligence based smart diagnosis of Alzheimer\u2019s disease and mild cognitive impairment. in Smart Cities Conference (ISC2), 2017 International. 2017. IEEE.","DOI":"10.1109\/ISC2.2017.8090871"},{"key":"20_CR28","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1109\/ACCESS.2014.2325029","volume":"2","author":"Xue-Wen Chen","year":"2014","unstructured":"Chen, X.-W. and X. Lin, Big data deep learning: challenges and perspectives. IEEE access, 2014. 2: p. 514-525.","journal-title":"IEEE Access"},{"key":"20_CR29","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1016\/j.bspc.2016.11.021","volume":"33","author":"Sonali Mishra","year":"2017","unstructured":"Mishra, S., et al., Gray level co-occurrence matrix and random forest based acute lymphoblastic leukemia detection. Biomedical Signal Processing and Control, 2017. 33: p. 272-280.","journal-title":"Biomedical Signal Processing and Control"},{"key":"20_CR30","doi-asserted-by":"crossref","unstructured":"Kalsoom, A., et al., A dimensionality reduction-based efficient software fault prediction using Fisher linear discriminant analysis (FLDA). The Journal of Supercomputing, 2018: p. 1-35.","DOI":"10.1007\/s11227-018-2326-5"},{"key":"20_CR31","doi-asserted-by":"crossref","unstructured":"Khan, S., et al., Optimized Gabor feature extraction for mass classification using cuckoo search for big data e-healthcare. Journal of Grid Computing, 2018: p. 1-16.","DOI":"10.1007\/s10723-018-9459-x"},{"key":"20_CR32","doi-asserted-by":"crossref","unstructured":"Nazir, F., et al., Social media signal detection using tweets volume, hashtag, and sentiment analysis. Multimedia Tools and Applications, 2018: p. 1-34.","DOI":"10.1007\/s11042-018-6437-z"}],"container-title":["Handbook of Multimedia Information Security: Techniques and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-15887-3_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,24]],"date-time":"2022-09-24T07:50:06Z","timestamp":1664005806000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-15887-3_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030158866","9783030158873"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-15887-3_20","relation":{},"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"20 July 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}