{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T14:26:42Z","timestamp":1742999202452,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030649838"},{"type":"electronic","value":"9783030649845"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","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":[[2020]]},"DOI":"10.1007\/978-3-030-64984-5_4","type":"book-chapter","created":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T00:25:42Z","timestamp":1606436742000},"page":"42-54","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Novel Block Diagonalization for Reducing Features and Computations in Medical Diagnosis"],"prefix":"10.1007","author":[{"given":"Tahira","family":"Ghani","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"B.","family":"John Oommen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,27]]},"reference":[{"key":"4_CR1","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1016\/j.crad.2017.01.002","volume":"72","author":"B Al Mohammad","year":"2017","unstructured":"Al Mohammad, B., Brennan, P.C., Mello-Thoms, C.: A review of lung cancer screening and the role of computer-aided detection. Clin. Radiol. 72, 433\u2013442 (2017)","journal-title":"Clin. Radiol."},{"key":"4_CR2","doi-asserted-by":"publisher","first-page":"1552","DOI":"10.1118\/1.1387272","volume":"28","author":"SG Armato III","year":"2001","unstructured":"Armato III, S.G., Giger, M.L., MacMahon, H.: Automated detection of lung nodules in CT scans: preliminary results. Med. Phys. 28, 1552\u20131561 (2001)","journal-title":"Med. Phys."},{"key":"4_CR3","doi-asserted-by":"publisher","first-page":"1011","DOI":"10.1016\/j.acra.2004.06.005","volume":"11","author":"SG Armato III","year":"2004","unstructured":"Armato III, S.G., Sensakovic, W.F.: Automated lung segmentation for thoracic CT: impact on computer-aided diagnosis. Acad. Radiol. 11, 1011\u20131021 (2004)","journal-title":"Acad. Radiol."},{"issue":"3","key":"4_CR4","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1148\/radiol.2283020505","volume":"228","author":"F Chabat","year":"2003","unstructured":"Chabat, F., Yang, G.Z., Hansell, D.M.: Obstructive lung diseases: texture classification for differentiation at CT. Radiology 228(3), 871\u2013877 (2003)","journal-title":"Radiology"},{"key":"4_CR5","doi-asserted-by":"publisher","first-page":"e0174202","DOI":"10.1371\/journal.pone.0174202","volume":"12","author":"C Chen","year":"2017","unstructured":"Chen, C., Twycross, J., Garibaldi, J.M.: A new accuracy measure based on bounded relative error for time series forecasting. PLoS ONE 12, e0174202 (2017)","journal-title":"PLoS ONE"},{"key":"4_CR6","doi-asserted-by":"publisher","first-page":"S1213","DOI":"10.3233\/BME-151418","volume":"26","author":"O Demir","year":"2015","unstructured":"Demir, O., Camurcu, A.Y.: Computer-aided detection of lung nodules using outer surface features. Bio-Med. Mater. Eng. 26, S1213\u2013S1222 (2015)","journal-title":"Bio-Med. Mater. Eng."},{"key":"4_CR7","volume-title":"Fundamentals of High-Resolution Lung CT","author":"BM Elicker","year":"2013","unstructured":"Elicker, B.M., Webb, W.R.: Fundamentals of High-Resolution Lung CT. Wolters Kluwer, Alphen aan den Rijn (2013)"},{"issue":"2","key":"4_CR8","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1007\/s00330-018-5530-z","volume":"29","author":"L Fan","year":"2018","unstructured":"Fan, L., et al.: Radiomics signature: a biomarker for the preoperative discrimination of lung invasive adenocarcinoma manifesting as a ground-glass nodule. Eur. Radiol. 29(2), 889\u2013897 (2018). https:\/\/doi.org\/10.1007\/s00330-018-5530-z","journal-title":"Eur. Radiol."},{"key":"4_CR9","unstructured":"Ghani, T.: On forecasting lung cancer patients\u2019 survival rates using 3D feature engineering. MCS thesis, Carleton University (2019)"},{"key":"4_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1007\/978-3-030-50516-5_18","volume-title":"Image Analysis and Recognition","author":"T Ghani","year":"2020","unstructured":"Ghani, T., Oommen, B.J.: Enhancing the prediction of lung cancer survival rates using 2D features from 3D scans. In: Campilho, A., Karray, F., Wang, Z. (eds.) ICIAR 2020. LNCS, vol. 12132, pp. 202\u2013215. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-50516-5_18"},{"key":"4_CR11","doi-asserted-by":"publisher","first-page":"e0118261","DOI":"10.1371\/journal.pone.0118261","volume":"10","author":"O Grove","year":"2015","unstructured":"Grove, O., et al.: Quantitative computed tomographic descriptors associate tumour shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. PLoS ONE 10, e0118261 (2015)","journal-title":"PLoS ONE"},{"key":"4_CR12","doi-asserted-by":"publisher","first-page":"1032","DOI":"10.1109\/T-C.1971.223399","volume":"100","author":"EL Hall","year":"1971","unstructured":"Hall, E.L., Kruger, R.P., Dwyer, S.J., Hall, D.L., McLaren, R.W., Lodwick, G.S.: A survey of preprocessing and feature extraction techniques for radiographic images. IEEE Trans. Comput. 100, 1032\u20131044 (1971)","journal-title":"IEEE Trans. Comput."},{"key":"4_CR13","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","volume":"6","author":"RM Haralick","year":"1973","unstructured":"Haralick, R.M., Shanmugam, K., Dinstein, H.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 6, 610\u2013621 (1973)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"4_CR14","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1007\/s10278-008-9147-7","volume":"22","author":"N Kim","year":"2009","unstructured":"Kim, N., Seo, J.B., Lee, Y., Lee, J.G., Kim, S.S., Kang, S.-H.: Development of an automatic classification system for differentiation of obstructive lung disease using HRCT. J. Digit. Imaging 22, 136\u2013148 (2009). https:\/\/doi.org\/10.1007\/s10278-008-9147-7","journal-title":"J. Digit. Imaging"},{"key":"4_CR15","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.media.2015.02.002","volume":"22","author":"T Messay","year":"2015","unstructured":"Messay, T., Hardie, R.C., Tuinstra, T.R.: Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the lung image database consortium and image database resource initiative dataset. Med. Image Anal. 22, 48\u201362 (2015)","journal-title":"Med. Image Anal."},{"key":"4_CR16","doi-asserted-by":"publisher","first-page":"011021","DOI":"10.1117\/1.JMI.5.1.011021","volume":"5","author":"R Paul","year":"2018","unstructured":"Paul, R., Hawkins, S.H., Schabath, M.B., Gillies, R.J., Hall, L.O., Goldgof, D.B.: Predicting malignant nodules by fusing deep features with classical radiomics features. J. Med. Imaging 5, 011021 (2018)","journal-title":"J. Med. Imaging"},{"key":"4_CR17","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/caac.21442","volume":"68","author":"RL Siegel","year":"2018","unstructured":"Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics. CA Cancer J. Clin. 68, 7\u201330 (2018)","journal-title":"CA Cancer J. Clin."},{"key":"4_CR18","doi-asserted-by":"crossref","unstructured":"Singadkar, G., Mahajan, A., Thakur, M., Talbar, S.: Automatic lung segmentation for the inclusion of juxtapleural nodules and pulmonary vessels using curvature based border correction. J. King Saud Univ. Comput. Inf. Sci. (2018)","DOI":"10.1016\/j.jksuci.2018.07.005"},{"key":"4_CR19","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1120\/1.1582411","volume":"4","author":"B Zhao","year":"2003","unstructured":"Zhao, B., Gamsu, G., Ginsberg, M.S., Jiang, L., Schwartz, L.H.: Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm. J. Appl. Clin. Med. Phys. 4, 248\u2013260 (2003)","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"4_CR20","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.bspc.2014.03.010","volume":"13","author":"S Zhou","year":"2014","unstructured":"Zhou, S., Cheng, Y., Tamura, S.: Automated lung segmentation and smoothing techniques for inclusion of juxtapleural nodules and pulmonary vessels on chest CT images. Biomed. Signal Process. Control 13, 62\u201370 (2014)","journal-title":"Biomed. Signal Process. Control"}],"container-title":["Lecture Notes in Computer Science","AI 2020: Advances in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-64984-5_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T16:12:21Z","timestamp":1619280741000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-64984-5_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030649838","9783030649845"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-64984-5_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"27 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australasian Joint Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canberra, ACT","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"33","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ausai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ajcai2020.net\/index.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"57","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"36","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"63% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}