{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T06:20:15Z","timestamp":1761805215857},"reference-count":11,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2004,6]]},"abstract":"<jats:p> Segmentation of ovarian ultrasound images using cellular neural networks (CNNs) is studied in this paper. The segmentation method consists of five successive steps where the first four uses CNNs. In the first step, only rough position of follicles is determined. In the second step, the results are improved by expansion of detected follicles. In the third step, previously undetected inexpressive follicles are determined, while the fourth step detects the position of ovary. All results are joined in the fifth step. The templates for CNNs were obtained by applying genetic algorithm. The segmentation method has been tested on 50 ovarian ultrasound images. The recognition rate of follicles was around 60% and misidentification rate was around 30%. <\/jats:p>","DOI":"10.1142\/s0218001404003368","type":"journal-article","created":{"date-parts":[[2004,6,9]],"date-time":"2004-06-09T06:47:56Z","timestamp":1086763676000},"page":"563-581","source":"Crossref","is-referenced-by-count":30,"title":["SEGMENTATION OF OVARIAN ULTRASOUND IMAGES USING CELLULAR NEURAL NETWORKS"],"prefix":"10.1142","volume":"18","author":[{"given":"BORIS","family":"CIGALE","sequence":"first","affiliation":[{"name":"Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"DAMJAN","family":"ZAZULA","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.1145\/235815.235821"},{"key":"rf2","volume":"15","author":"Chua L. O.","journal-title":"Anal. Integr. Circuits Sign. Process."},{"key":"rf3","doi-asserted-by":"publisher","DOI":"10.1109\/31.7600"},{"key":"rf8","doi-asserted-by":"publisher","DOI":"10.1109\/81.238343"},{"key":"rf9","doi-asserted-by":"publisher","DOI":"10.1109\/42.746626"},{"key":"rf10","doi-asserted-by":"publisher","DOI":"10.1016\/S0262-8856(01)00096-8"},{"key":"rf11","doi-asserted-by":"publisher","DOI":"10.1016\/S0262-8856(01)00097-X"},{"key":"rf12","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022832515369"},{"key":"rf15","doi-asserted-by":"publisher","DOI":"10.1016\/S0301-5629(97)00213-5"},{"key":"rf16","doi-asserted-by":"publisher","DOI":"10.1016\/S1386-5056(98)00042-2"},{"key":"rf17","doi-asserted-by":"publisher","DOI":"10.1002\/uog.225"}],"container-title":["International Journal of Pattern Recognition and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218001404003368","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T12:44:04Z","timestamp":1565181844000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218001404003368"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2004,6]]},"references-count":11,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2011,11,21]]},"published-print":{"date-parts":[[2004,6]]}},"alternative-id":["10.1142\/S0218001404003368"],"URL":"https:\/\/doi.org\/10.1142\/s0218001404003368","relation":{},"ISSN":["0218-0014","1793-6381"],"issn-type":[{"value":"0218-0014","type":"print"},{"value":"1793-6381","type":"electronic"}],"subject":[],"published":{"date-parts":[[2004,6]]}}}