{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T20:56:26Z","timestamp":1758056186066,"version":"3.44.0"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031962349"},{"type":"electronic","value":"9783031962356"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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\/978-3-031-96235-6_2","type":"book-chapter","created":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T01:03:09Z","timestamp":1750640589000},"page":"14-28","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Integrated System for the Texture Analysis of Prostate Ultrasound Images Based on Different Pre-processing Schemes"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9144-264X","authenticated-orcid":false,"given":"Haohan","family":"Yu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1248-3114","authenticated-orcid":false,"given":"Jiale","family":"Hou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2364-0479","authenticated-orcid":false,"given":"Xiwei","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1247-8573","authenticated-orcid":false,"given":"Christos P.","family":"Loizou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,24]]},"reference":[{"issue":"3","key":"2_CR1","doi-asserted-by":"publisher","first-page":"229","DOI":"10.3322\/caac.21834","volume":"74","author":"F Bray","year":"2024","unstructured":"Bray, F., Laversanne, M., Sung, H., Ferlay, J., Siegel, R.L., Soerjomataram, I., et al.: Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. A Cancer Journal for Clinicians 74(3), 229\u2013263 (2024)","journal-title":"A Cancer Journal for Clinicians"},{"issue":"3","key":"2_CR2","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A., et al.: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. A Cancer Journal for Clinicians 71(3), 209\u2013249 (2021)","journal-title":"A Cancer Journal for Clinicians"},{"issue":"e57","key":"2_CR3","first-page":"1","volume":"40","author":"JH Pyun","year":"2024","unstructured":"Pyun, J.H., Ko, Y.H., Kim, S.W., Kang, S.G., Son, N.-H., et al.: The older the patients, the more aggressive the prostate cancer detected even among those with a prostate-specific antigen level below the low-risk threshold: Analysis using nationwide Korean data. J. Korean Med. Sci. 40(e57), 1\u20134 (2024)","journal-title":"J. Korean Med. Sci."},{"issue":"7","key":"2_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e28949","volume":"10","author":"M Zniber","year":"2024","unstructured":"Zniber, M., Lamminen, T., Taimen, P., Bostr\u00f6m, P.J., Huynh, T.-P., et al.: 1H-NMR-based urine metabolomics of prostate cancer and benign prostatic hyperplasia. Heliyon 10(7), e28949 (2024)","journal-title":"Heliyon"},{"issue":"1","key":"2_CR5","first-page":"7359375","volume":"2020","author":"X Huang","year":"2020","unstructured":"Huang, X., Chen, M., Liu, P., Du, Y.: Texture feature-based classification on transrectal ultrasound image for prostatic cancer detection. Comput. Math. Methods Med. 2020(1), 7359375 (2020)","journal-title":"Comput. Math. Methods Med."},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"B\u00fclb\u00fcl, O., Nak, D., G\u00f6ksel, S.: Prediction of lesion-based treatment response after two cycles of Lu-177 prostate-specific membrane antigen treatment in metastatic castration-resistant prostate cancer using machine learning. Urol. Int. 1-7 (2024)","DOI":"10.1159\/000541628"},{"key":"2_CR7","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.948662","volume":"12","author":"K Wang","year":"2022","unstructured":"Wang, K., Chen, P., Feng, B., Tu, J., Hu, Z., Zhang, M., et al.: Machine learning prediction of prostate cancer from transrectal ultrasound video clips. Front. Oncol. 12, 948662 (2022)","journal-title":"Front. Oncol."},{"issue":"3","key":"2_CR8","first-page":"319","volume":"21","author":"Q Yang","year":"2024","unstructured":"Yang, Q., Li, Q., Li, N., Luo, Y., Tang, J.: A radiotranscriptomics approach for prediction of prostate cancer based on ultrasound image texture features (in Chinese). Chin. J. Med. Ultrasound (Electron. Ed.) 21(3), 319\u2013326 (2024)","journal-title":"Chin. J. Med. Ultrasound (Electron. Ed.)"},{"key":"2_CR9","doi-asserted-by":"publisher","first-page":"1491144","DOI":"10.3389\/fphys.2024.1491144","volume":"15","author":"M Hadjicharalambous","year":"2024","unstructured":"Hadjicharalambous, M., Roussakis, Y., Bourantas, G., Ioannou, E., Miller, K., Doolan, P., et al.: Personalised in silico biomechanical modelling towards the optimisation of high dose-rate brachytherapy planning and treatment against prostate cancer. Front. Physiol. 15, 1491144 (2024)","journal-title":"Front. Physiol."},{"key":"2_CR10","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1007\/s11517-006-0045-1","volume":"44","author":"CP Loizou","year":"2006","unstructured":"Loizou, C.P., Pattichis, C.S., Pantziaris, M., Tyllis, T., Nicolaides, A.: Quality evaluation of ultrasound imaging in the carotid artery based on normalization and speckle reduction filtering. Med. Biol. Eng. Comput. 44, 414\u2013426 (2006)","journal-title":"Med. Biol. Eng. Comput."},{"key":"2_CR11","first-page":"38","volume":"14","author":"O Magud","year":"2017","unstructured":"Magud, O., Tuba, E., Bacanin, N.: Medical ultrasound image speckle noise reduction by adaptive median filter. WSEAS Trans. Biol. Biomed. 14, 38\u201346 (2017)","journal-title":"WSEAS Trans. Biol. Biomed."},{"issue":"6","key":"2_CR12","first-page":"36","volume":"4","author":"D Bhonsle","year":"2012","unstructured":"Bhonsle, D., Chandra, V., Sinha, G.R.: Medical image denoising using bilateral filter. Int. J. Image Graph. Signal Process. 4(6), 36\u201343 (2012)","journal-title":"Int. J. Image Graph. Signal Process."},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Ar\u0131n, E., \u015eamil Yetik, \u0130.: Noise reduction filter optimization for prostate cancer localization. In: 2019 27th Signal Processing and Communications Applications Conference (SIU), pp. 1\u20134. IEEE, Sivas, Turkey (2019)","DOI":"10.1109\/SIU.2019.8806426"},{"key":"2_CR14","doi-asserted-by":"publisher","first-page":"81293","DOI":"10.1109\/ACCESS.2024.3411709","volume":"12","author":"K Sikhakhane","year":"2024","unstructured":"Sikhakhane, K., Rimer, S., Gololo, M., Ouahada, K., Abu-Mahfouz, A.M.: Evaluation of speckle noise reduction filters and machine learning algorithms for ultrasound images. IEEE Access 12, 81293\u201381312 (2024)","journal-title":"IEEE Access"},{"issue":"4","key":"2_CR15","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1080\/13682199.2023.2165242","volume":"70","author":"GV Kumar","year":"2022","unstructured":"Kumar, G.V., Bellary, M.I., Reddy, T.B.: Prostate cancer classification with MRI using Taylor-Bird squirrel optimization-based deep recurrent neural network. Imaging Sci. J. 70(4), 214\u2013227 (2022)","journal-title":"Imaging Sci. J."},{"key":"2_CR16","series-title":"LNCS","first-page":"801","volume-title":"ECCV 2018","author":"LC Chen","year":"2018","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., et al.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 801\u2013818. Springer, Cham (2018)"},{"key":"2_CR17","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.bspc.2016.02.006","volume":"27","author":"LS Chow","year":"2016","unstructured":"Chow, L.S., Paramesran, R.: Review of medical image quality assessment. Biomed. Signal Process. Control 27, 145\u2013154 (2016)","journal-title":"Biomed. Signal Process. Control"},{"issue":"21","key":"2_CR18","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","volume":"77","author":"JJM Van Griethuysen","year":"2017","unstructured":"Van Griethuysen, J.J.M., Fedorov, A., Parmar, C., Hosny, A., Aucoin, N., Narayan, V., et al.: Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77(21), e104\u2013e107 (2017)","journal-title":"Cancer Res."},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Mandal, S., Bhuiya, S., Lugez, E.: Using attention-based convolutional auto-encoders for catheter path reconstruction in ultrasound images. In: Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions and Modeling. 12928, pp. 360\u2013367. SPIE, San Diego, California (2024)","DOI":"10.1117\/12.3003897"}],"container-title":["IFIP Advances in Information and Communication Technology","Artificial Intelligence Applications and Innovations"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-96235-6_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T13:21:39Z","timestamp":1757942499000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-96235-6_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031962349","9783031962356"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-96235-6_2","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"type":"print","value":"1868-4238"},{"type":"electronic","value":"1868-422X"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"24 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"IFIP International Conference on Artificial Intelligence Applications and Innovations","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Limassol","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cyprus","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aiai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ifipaiai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}