This paper presents a method which uses multiple types of expert knowledge together in 3D medical image segmentation based on rough set theory. The focus of this paper is how to approximate a ROI(region of interest) w...This paper presents a method which uses multiple types of expert knowledge together in 3D medical image segmentation based on rough set theory. The focus of this paper is how to approximate a ROI(region of interest) when there are multiple types of expert knowledge. Based on rough set theory, the image can be split into three regions: positive regions; negative regions; boundary regions. With multiple knowledge we refine ROI as an intersection of all of the expected shapes with single knowledge. At last we show the results of implementing a rough 3D image segmentation and visualization system.展开更多
This study was to investigate the diagnostic value of the computed tomography（CT） histogram in thyroid benign solitary coarse calcification nodules（BSCNs）. A total of 89 thyroid solitary coarse calcification nodu...This study was to investigate the diagnostic value of the computed tomography（CT） histogram in thyroid benign solitary coarse calcification nodules（BSCNs）. A total of 89 thyroid solitary coarse calcification nodules（coarse calcification ≥5 mm, no definite soft tissue around calcification） confirmed either by surgery or histopathological examination in 86 cases enrolled from January 2009 to December 2015 were evaluated. These included 56 BSCNs and 33 malignant solitary coarse calcification nodules（MSCNs）. Overall, 27 cut-off values were calculated by N（4≤N≤30） times of 50 Hounsfield units（HU） in the range of 200 to 1500 HU, and each cut-off value and the differences in the corresponding area percentages in the CT histogram were recorded for BSCN and MSCN. The optimal cut-off value and the corresponding area percentage were established by receiver operating characteristic（ROC） curve analysis. In the 19 groups with an ROC area under curve（AUC） of more than 0.7, at a cut-off value of 800 HU and at an area percentage of no more than 93.8%, the ROC AUC reached the maximum of 0.79, and the accuracy, sensitivity, and specificity were 75.3%, 80.4%, and 66.7%, respectively. At a cut-off value of 1050 HU and at an area percentage of no more than 93.6%, the accuracy, sensitivity, and specificity were 71.9%, 60.7%, and 90.9%, respectively. At a cut-off of 1150 HU and area of no more than 98.4%, the accuracy, sensitivity, and specificity were 70.8%, 57.1%, and 93.9%, respectively. At a cut-off of 600 HU and area of no more than 12.1%, the accuracy, sensitivity, and specificity were 61.8%, 39.3%, and 100.0%, respectively. Compared with the cut-off value of 800 HU and an area percentage of no more than 93.8%, the sensitivity of cut-off values and minimum areas of 1050 HU and 93.6%, of 1150 HU and 98.4%, and of 600 HU and 12.1%, was gradually decreasing; however, their specificity was gradually increasing. This can provide an important basis for reducing the misdiagnosis and unnecessary surgical trauma.展开更多
基金PHD Site from Chinese Educational Department,Grant number:20040699015
文摘This paper presents a method which uses multiple types of expert knowledge together in 3D medical image segmentation based on rough set theory. The focus of this paper is how to approximate a ROI(region of interest) when there are multiple types of expert knowledge. Based on rough set theory, the image can be split into three regions: positive regions; negative regions; boundary regions. With multiple knowledge we refine ROI as an intersection of all of the expected shapes with single knowledge. At last we show the results of implementing a rough 3D image segmentation and visualization system.
文摘This study was to investigate the diagnostic value of the computed tomography（CT） histogram in thyroid benign solitary coarse calcification nodules（BSCNs）. A total of 89 thyroid solitary coarse calcification nodules（coarse calcification ≥5 mm, no definite soft tissue around calcification） confirmed either by surgery or histopathological examination in 86 cases enrolled from January 2009 to December 2015 were evaluated. These included 56 BSCNs and 33 malignant solitary coarse calcification nodules（MSCNs）. Overall, 27 cut-off values were calculated by N（4≤N≤30） times of 50 Hounsfield units（HU） in the range of 200 to 1500 HU, and each cut-off value and the differences in the corresponding area percentages in the CT histogram were recorded for BSCN and MSCN. The optimal cut-off value and the corresponding area percentage were established by receiver operating characteristic（ROC） curve analysis. In the 19 groups with an ROC area under curve（AUC） of more than 0.7, at a cut-off value of 800 HU and at an area percentage of no more than 93.8%, the ROC AUC reached the maximum of 0.79, and the accuracy, sensitivity, and specificity were 75.3%, 80.4%, and 66.7%, respectively. At a cut-off value of 1050 HU and at an area percentage of no more than 93.6%, the accuracy, sensitivity, and specificity were 71.9%, 60.7%, and 90.9%, respectively. At a cut-off of 1150 HU and area of no more than 98.4%, the accuracy, sensitivity, and specificity were 70.8%, 57.1%, and 93.9%, respectively. At a cut-off of 600 HU and area of no more than 12.1%, the accuracy, sensitivity, and specificity were 61.8%, 39.3%, and 100.0%, respectively. Compared with the cut-off value of 800 HU and an area percentage of no more than 93.8%, the sensitivity of cut-off values and minimum areas of 1050 HU and 93.6%, of 1150 HU and 98.4%, and of 600 HU and 12.1%, was gradually decreasing; however, their specificity was gradually increasing. This can provide an important basis for reducing the misdiagnosis and unnecessary surgical trauma.
