Jie Wu, Pavani Davuluri, Ashwin Belle, Charles Cockrell, Yang Tang, Kevin Ward, R. Hobson, K. Najarian
{"title":"骨盆CT图像的骨折检测与位移定量测量","authors":"Jie Wu, Pavani Davuluri, Ashwin Belle, Charles Cockrell, Yang Tang, Kevin Ward, R. Hobson, K. Najarian","doi":"10.1109/BIBMW.2011.6112437","DOIUrl":null,"url":null,"abstract":"Traumatic pelvic injury is a severe and common injury in the United States. The automatic detection of fractures in pelvic CT images is a significant contribution for assisting physicians in making faster and more accurate patient diagnostic decisions and treatment planning. However, due to the low resolution and quality of the original images, the complexity of pelvic structures, and the difference in visual characteristics of fracture by their location, it is difficult to detect and accurately locate the pelvic fractures and determine the severity of the injury. In this paper, an automatic hierarchical algorithm for detecting pelvic bone fractures in CT scans is proposed. The algorithm utilizes symmetric comparison, adaptive windowing, boundary tracing, wavelet transform. Also, the quantitative measure of fracture severity in pelvic CT scans is defined. The results are promising, demonstrating that the proposed method is capable of automatically detecting both major and minor fractures accurately, shows potential for clinical application. Statistical results also indicate the superiority of the proposed method.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"14 1","pages":"600-606"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Fracture detection and quantitative measure of displacement in pelvic CT images\",\"authors\":\"Jie Wu, Pavani Davuluri, Ashwin Belle, Charles Cockrell, Yang Tang, Kevin Ward, R. Hobson, K. Najarian\",\"doi\":\"10.1109/BIBMW.2011.6112437\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Traumatic pelvic injury is a severe and common injury in the United States. The automatic detection of fractures in pelvic CT images is a significant contribution for assisting physicians in making faster and more accurate patient diagnostic decisions and treatment planning. However, due to the low resolution and quality of the original images, the complexity of pelvic structures, and the difference in visual characteristics of fracture by their location, it is difficult to detect and accurately locate the pelvic fractures and determine the severity of the injury. In this paper, an automatic hierarchical algorithm for detecting pelvic bone fractures in CT scans is proposed. The algorithm utilizes symmetric comparison, adaptive windowing, boundary tracing, wavelet transform. Also, the quantitative measure of fracture severity in pelvic CT scans is defined. The results are promising, demonstrating that the proposed method is capable of automatically detecting both major and minor fractures accurately, shows potential for clinical application. Statistical results also indicate the superiority of the proposed method.\",\"PeriodicalId\":6345,\"journal\":{\"name\":\"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)\",\"volume\":\"14 1\",\"pages\":\"600-606\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIBMW.2011.6112437\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBMW.2011.6112437","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fracture detection and quantitative measure of displacement in pelvic CT images
Traumatic pelvic injury is a severe and common injury in the United States. The automatic detection of fractures in pelvic CT images is a significant contribution for assisting physicians in making faster and more accurate patient diagnostic decisions and treatment planning. However, due to the low resolution and quality of the original images, the complexity of pelvic structures, and the difference in visual characteristics of fracture by their location, it is difficult to detect and accurately locate the pelvic fractures and determine the severity of the injury. In this paper, an automatic hierarchical algorithm for detecting pelvic bone fractures in CT scans is proposed. The algorithm utilizes symmetric comparison, adaptive windowing, boundary tracing, wavelet transform. Also, the quantitative measure of fracture severity in pelvic CT scans is defined. The results are promising, demonstrating that the proposed method is capable of automatically detecting both major and minor fractures accurately, shows potential for clinical application. Statistical results also indicate the superiority of the proposed method.