S. Abdullah, M. A. Zulkifley, W. Zaki, Mohd Faisal Ibrahim
{"title":"x线图像增强诊断前骨赘","authors":"S. Abdullah, M. A. Zulkifley, W. Zaki, Mohd Faisal Ibrahim","doi":"10.1109/ELECSYM.2015.7380812","DOIUrl":null,"url":null,"abstract":"Computer assisted system has been implemented virtually in many domains within the field of medical imaging. An automated assessment system based on computer is important for early detection and diagnosis of various diseases. Moreover, image enhancement in medical imaging is a vital component in improving the quality of medical image, so that pre-screening of the disease can be carried accurately without performing any open surgery. Hence, pre-processing module is an important step to enhance the medical image, especially for X-ray modality, which normally affected by artefacts, noise and hardware limitations. A low quality of X-ray image will usually lead to inaccurate diagnosis. Therefore, a semi-automated assessment system has been developed to diagnose Anterior Osteophytes (AO) condition based on X-ray image that focused on cervical vertebrae. The system consists of four main modules: image enhancement, image segmentation, feature extraction and classification. This system is intended to facilitate medical practitioners in screening the AO condition with more accurate and faster diagnosis. A graphical user interface has also been developed to help the medical practitioners to perform the image enhancement easily. The proposed system is tested with 100 cervical X-ray images and the obtained accuracy is 60%. Finally, this system is suitable for real time implementation and can be implemented on most recent desktop technology.","PeriodicalId":248906,"journal":{"name":"2015 International Electronics Symposium (IES)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"X-Ray image enhancement for anterior Osteophyte diagnosis\",\"authors\":\"S. Abdullah, M. A. Zulkifley, W. Zaki, Mohd Faisal Ibrahim\",\"doi\":\"10.1109/ELECSYM.2015.7380812\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Computer assisted system has been implemented virtually in many domains within the field of medical imaging. An automated assessment system based on computer is important for early detection and diagnosis of various diseases. Moreover, image enhancement in medical imaging is a vital component in improving the quality of medical image, so that pre-screening of the disease can be carried accurately without performing any open surgery. Hence, pre-processing module is an important step to enhance the medical image, especially for X-ray modality, which normally affected by artefacts, noise and hardware limitations. A low quality of X-ray image will usually lead to inaccurate diagnosis. Therefore, a semi-automated assessment system has been developed to diagnose Anterior Osteophytes (AO) condition based on X-ray image that focused on cervical vertebrae. The system consists of four main modules: image enhancement, image segmentation, feature extraction and classification. This system is intended to facilitate medical practitioners in screening the AO condition with more accurate and faster diagnosis. A graphical user interface has also been developed to help the medical practitioners to perform the image enhancement easily. The proposed system is tested with 100 cervical X-ray images and the obtained accuracy is 60%. Finally, this system is suitable for real time implementation and can be implemented on most recent desktop technology.\",\"PeriodicalId\":248906,\"journal\":{\"name\":\"2015 International Electronics Symposium (IES)\",\"volume\":\"98 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 International Electronics Symposium (IES)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ELECSYM.2015.7380812\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Electronics Symposium (IES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ELECSYM.2015.7380812","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
X-Ray image enhancement for anterior Osteophyte diagnosis
Computer assisted system has been implemented virtually in many domains within the field of medical imaging. An automated assessment system based on computer is important for early detection and diagnosis of various diseases. Moreover, image enhancement in medical imaging is a vital component in improving the quality of medical image, so that pre-screening of the disease can be carried accurately without performing any open surgery. Hence, pre-processing module is an important step to enhance the medical image, especially for X-ray modality, which normally affected by artefacts, noise and hardware limitations. A low quality of X-ray image will usually lead to inaccurate diagnosis. Therefore, a semi-automated assessment system has been developed to diagnose Anterior Osteophytes (AO) condition based on X-ray image that focused on cervical vertebrae. The system consists of four main modules: image enhancement, image segmentation, feature extraction and classification. This system is intended to facilitate medical practitioners in screening the AO condition with more accurate and faster diagnosis. A graphical user interface has also been developed to help the medical practitioners to perform the image enhancement easily. The proposed system is tested with 100 cervical X-ray images and the obtained accuracy is 60%. Finally, this system is suitable for real time implementation and can be implemented on most recent desktop technology.