{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T00:10:26Z","timestamp":1763338226572,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031765834"},{"type":"electronic","value":"9783031765841"}],"license":[{"start":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:00:00Z","timestamp":1730592000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:00:00Z","timestamp":1730592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-76584-1_7","type":"book-chapter","created":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T17:02:38Z","timestamp":1730566958000},"page":"69-87","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Evaluating Radiomics Feature Reduction for Thyroid Nodule Segmentation in Thermal Imaging"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-2162-1780","authenticated-orcid":false,"given":"Mehdi","family":"Etehadtavakol","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0841-1894","authenticated-orcid":false,"given":"Mahnaz","family":"Etehadtavakol","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5505-7480","authenticated-orcid":false,"given":"Golnaz","family":"Moallem","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5701-1080","authenticated-orcid":false,"given":"Eddie Y. K.","family":"Ng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,3]]},"reference":[{"key":"7_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-10-3147-2","volume-title":"Application of infrared to biomedical sciences","author":"EYK Ng","year":"2017","unstructured":"Ng, E.Y.K., Etehadtavakol, M.: Application of infrared to biomedical sciences. Springer, Germany (2017)"},{"issue":"4","key":"7_CR2","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","volume":"48","author":"P Lambin","year":"2012","unstructured":"Lambin, P., et al.: Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer 48(4), 441\u2013446 (2012)","journal-title":"Eur. J. Cancer"},{"key":"7_CR3","unstructured":"Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3(Mar), 1157\u20131182 (2003)"},{"key":"7_CR4","doi-asserted-by":"crossref","unstructured":"Moran, M.B., Conci, A., Gonz\u00e1lez, J.R., Ara\u00fajo, A.S., Fiirst, W., Dami\u00e3o, C.P., et al.: Identification of thyroid nodules in infrared images by convolutional neural networks. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2018)","DOI":"10.1109\/IJCNN.2018.8489032"},{"key":"7_CR5","unstructured":"Moran, M.B.H., Conci, A., dos Santos Ara\u00fajo, A.: Evaluation of quantitative features and convolutional neural networks for nodule identification in thyroid thermographies. In: 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE), pp. 1\u20136. IEEE (2019)"},{"issue":"1","key":"7_CR6","doi-asserted-by":"publisher","first-page":"21010","DOI":"10.1038\/s41598-020-78047-1","volume":"10","author":"C Dami\u00e3o","year":"2020","unstructured":"Dami\u00e3o, C., Montero, J., Moran, M., da Cruz Filho, R., Fontes, C., Lima, G., et al.: On the possibility of using temperature to aid in thyroid nodule investigation. Sci. Rep. 10(1), 21010 (2020)","journal-title":"Sci. Rep."},{"key":"7_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-031-44511-8_1","volume-title":"Artificial Intelligence Over Infrared Images for Medical Applications (AIIIMA 2023), LNCS","author":"M Etehadtavakol","year":"2023","unstructured":"Etehadtavakol, M., Sirati-Amsheh, M., Ng, E.Y.K.: Radiomics feature selection from thyroid thermal images to improve thyroid nodules interpretations. In: Kakileti, S.T., Manjunath, G., Schwartz, R.G., Frangi, A.F. (eds.) Artificial Intelligence Over Infrared Images for Medical Applications (AIIIMA 2023), LNCS, vol. 14298, pp. 1\u201310. Springer, Cham (2023)"},{"key":"7_CR8","doi-asserted-by":"crossref","unstructured":"Etehadtavakol, M., Etehadtavakol, M., Ng, E.Y.K.: Enhanced thyroid nodule segmentation through U-Net and VGG16 fusion with feature engineering: a comprehensive study. Comput. Methods Programs Biomed. 108209 (2024)","DOI":"10.1016\/j.cmpb.2024.108209"},{"key":"7_CR9","unstructured":"Infrared Image for the Diagnosis of Thyroid Nodules. Database and images. http:\/\/visual.ic.uff.br\/en\/thyroid. Accessed 25 Oct 2023"},{"issue":"5","key":"7_CR10","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1507\/endocrj.EJ20-0541","volume":"68","author":"CP Dami\u00e3o","year":"2021","unstructured":"Dami\u00e3o, C.P., Montero, J.R.G., Moran, M.B.H., de Farias, C.G., Brito, I.B., Saad, M.A.N., et al.: Application of thermography in the diagnostic investigation of thyroid nodules. Endocr. J. 68(5), 573\u2013581 (2021)","journal-title":"Endocr. J."},{"key":"7_CR11","doi-asserted-by":"publisher","unstructured":"Lambin, P., Leijenaar, R.T., Deist, T.M., et al.: Radiomics: the bridge between medical imaging and personalized medicine. Nature Rev. Clin. Oncol. 14(12), 749\u2013762 (2017). https:\/\/doi.org\/10.1038\/nrclinonc.2017.141","DOI":"10.1038\/nrclinonc.2017.141"},{"key":"7_CR12","doi-asserted-by":"publisher","first-page":"810","DOI":"10.3389\/fnins.2019.00810","volume":"13","author":"L Sun","year":"2019","unstructured":"Sun, L., Zhang, S., Chen, H., Luo, L.: Brain tumor segmentation and survival prediction using multimodal MRI scans with deep learning. Front. Neurosci. 13, 810 (2019). https:\/\/doi.org\/10.3389\/fnins.2019.00810","journal-title":"Front. Neurosci."},{"key":"7_CR13","doi-asserted-by":"publisher","unstructured":"Calesella, F., Testolin, A., De Filippo De Grazia, M., Zorzi, M.: A comparison of feature extraction methods for prediction of neuropsychological scores from functional connectivity data of stroke patients. Brain Inform. 8(1), 1\u201313 (2021). https:\/\/doi.org\/10.1186\/s40708-021-00089-0","DOI":"10.1186\/s40708-021-00089-0"},{"key":"7_CR14","doi-asserted-by":"publisher","first-page":"54776","DOI":"10.1109\/ACCESS.2020.2980942","volume":"8","author":"GT Reddy","year":"2020","unstructured":"Reddy, G.T., Reddy, M.P.K., Lakshmanna, K., Kaluri, R., Rajput, D.S., Srivastava, G., et al.: Analysis of dimensionality reduction techniques on big data. IEEE Access 8, 54776\u201354788 (2020)","journal-title":"IEEE Access"},{"issue":"1","key":"7_CR15","doi-asserted-by":"publisher","first-page":"105","DOI":"10.3390\/bioengineering10010105","volume":"10","author":"S Tawalbeh","year":"2023","unstructured":"Tawalbeh, S., Alquran, H., Alsalatie, M.: Deep feature engineering in colposcopy image recognition: a comparative study. Bioengineering 10(1), 105 (2023)","journal-title":"Bioengineering"},{"issue":"2","key":"7_CR16","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1109\/MEMB.2006.1607672","volume":"25","author":"VD Calhoun","year":"2006","unstructured":"Calhoun, V.D., Adali, T.: Unmixing fMRI with independent component analysis. IEEE Eng. Med. Biol. Mag. 25(2), 79\u201390 (2006)","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"7_CR17","volume-title":"Principal component analysis","author":"IT Jolliffe","year":"2002","unstructured":"Jolliffe, I.T.: Principal component analysis. Springer Series in Statistics. Springer, New York (2002)"},{"key":"7_CR18","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","volume":"7","author":"RA Fisher","year":"1936","unstructured":"Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugen. 7, 179\u2013188 (1936). https:\/\/doi.org\/10.1111\/j.1469-1809.1936.tb02137.x","journal-title":"Ann. Eugen."},{"key":"7_CR19","unstructured":"van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(Nov), 2579\u20132605 (2008)"},{"issue":"1\u20133","key":"7_CR20","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1023\/A:1012487302797","volume":"46","author":"I Guyon","year":"2002","unstructured":"Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46(1\u20133), 389\u2013422 (2002). https:\/\/doi.org\/10.1023\/A:1012487302797","journal-title":"Mach. Learn."},{"key":"7_CR21","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"7_CR22","doi-asserted-by":"publisher","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4700\u20134708 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence over Infrared Images for Medical Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-76584-1_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T17:03:11Z","timestamp":1730566991000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-76584-1_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,3]]},"ISBN":["9783031765834","9783031765841"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-76584-1_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,3]]},"assertion":[{"value":"3 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIIIMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Workshop on Artificial Intelligence over Infrared Images for Medical Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aiiima2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/niramai.com\/aiiima","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}