{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T14:49:40Z","timestamp":1787496580377,"version":"build-2736575974"},"reference-count":28,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2017,12,5]],"date-time":"2017-12-05T00:00:00Z","timestamp":1512432000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11531005"],"award-info":[{"award-number":["11531005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["91330101"],"award-info":[{"award-number":["91330101"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20140965"],"award-info":[{"award-number":["BK20140965"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2018,1]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Segmentation of lesions in ultrasound images is widely used for preliminary diagnosis. In this paper, we develop an automatic segmentation algorithm for multiple types of lesions in ultrasound images. The proposed method is able to detect and segment lesions automatically as well as generate accurate segmentation results for lesion regions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In the detection step, two saliency detection frameworks which adopt global image information are designed to capture the differences between normal and abnormal organs as well as these between lesions and the normal tissues around them. In the segmentation step, three types of local information, i.e., image intensity, improved local binary patterns (LBP) features, and an edge indicator, are embedded into a modified level set framework to carry out the segmentation task.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The cyst and carcinoma regions in the ultrasound images of the human kidneys can be automatically detected and segmented by using the proposed method. The efficiency and accuracy of the method are validated by quantitative evaluations and comparative measurements with three well\u2010recognized segmentation methods. Specifically, the average precision and dice coefficient of the proposed method in segmenting renal cysts are 95.33% and 90.16%, respectively, while those in segmenting renal carcinomas are 94.22% and 91.13%, respectively. The average precision and dice coefficient of the proposed method are higher than those of three compared segmentation methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed method can efficiently detect and segment the renal lesions in ultrasound images. In addition, since the proposed method utilizes the differences between normal and abnormal organs as well as these between lesions and the normal tissues around them, it can be possibly extended to deal with lesions in other organs of ultrasound images as well as lesions in medical images of other modalities.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.12661","type":"journal-article","created":{"date-parts":[[2017,11,13]],"date-time":"2017-11-13T05:42:38Z","timestamp":1510551758000},"page":"223-235","source":"Crossref","is-referenced-by-count":8,"title":["Automatic renal lesion segmentation in ultrasound images based on saliency features, improved LBP, and an edge indicator under level set framework"],"prefix":"10.1002","volume":"45","author":[{"given":"Luying","family":"Gui","sequence":"first","affiliation":[{"name":"Nanjing University of Science and Technology Nanjing Jiangsu 210094 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoping","family":"Yang","sequence":"additional","affiliation":[{"name":"Nanjing University Nanjing Jiangsu 210093 China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2017,12,5]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mri.2014.05.003"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.01.021"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2011.10.011"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.06.038"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4950721"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4950706"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2014.07.026"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2012.2230403"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2014.6868089"},{"key":"e_1_2_7_11_1","first-page":"422","volume-title":"2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR05)","author":"Zhang X","year":"2005"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1600-0846.2005.00092.x"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2012.2201717"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2014.07.005"},{"key":"e_1_2_7_15_1","first-page":"293","article-title":"Helical CT of von Hippel\u2010Lindau: semi\u2010automated segmentation of renal lesions","volume":"2","author":"Summers RM","year":"2001","journal-title":"Image Process"},{"key":"e_1_2_7_16_1","first-page":"1310","article-title":"Renal tumor quantification and classification in triple\u2010phase contrast\u2010enhanced abdominal CT","volume":"42","author":"Linguraru MG","year":"2009","journal-title":"Proc IEEE Int Symp Biomed Imaging"},{"key":"e_1_2_7_17_1","doi-asserted-by":"crossref","unstructured":"LandgrenM SjK OhlssonM StD OvergaardNC.An automated system for the detection and diagnosis of kidney lesions in children from scintigraphy images. 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