CAJ | 학술논문

目的：本文尝试设计一款基于高清晰度 CT （HDCT ）图像的孤立性肺结节（SPN ）计算机辅助诊断系统（CADS），以提高恶性 SPN 的检出率，使诊断更加客观、科学。方法收集经临床病理证实的孤立性肺结节120例，包括恶性肿瘤、良性肿瘤、结核和炎性假瘤，随机抽取60例作为实验集，60例作为验证集。实验集 HDCT 图像经图像预处理、感兴趣区域（ROI）基于标记的分水岭算法分割和 ROI 纹理特征参数提取，对获得的5项纹理特征参数做统计学处理，将统计结果应用于系统以对 SPN 做良恶性分析并给出提示信息。验证集 HDCT 图像输入系统后，对比系统预测结果与主任医师和住院医师预测结果来评价系统的可靠性。结果对比度、相关性、熵、平稳度和二阶矩 t 检验 P 值分别为0.000、0．002、0．914、0．295、0．002。对比度、相关性和二阶矩良性区间分别为（903，2003）、（2．76，3．48）、（0．01，1．54），恶性区间分别为（502，898）、（3．49，3．71）、（1．79，29．86）。系统、主任医师和住院医师的敏感度分别为83．3％、93．3％、76.7％，假阳性率分别为13．3％、16．7％、26．7％，正确率分别为85％、88．3％、75％。结论基于标记的分水岭算法对与胸壁或纵隔粘连的结节及磨玻璃病变等均可以较好地将其分割提取出来。对比度、相关性和二阶矩有统计学意义。系统在预测 SPN 良恶性具有较高的敏感性和准确性及最低的假阳性。 CAD 在 SPN 良恶性诊断具有一定的临床使用价值，本系统可以辅助临床医师诊断 SPN 良恶性。
목적：본문상시설계일관기우고청석도 CT （HDCT ）도상적고립성폐결절（SPN ）계산궤보조진단계통（CADS），이제고악성 SPN 적검출솔，사진단경가객관、과학。방법수집경림상병리증실적고립성폐결절120례，포괄악성종류、량성종류、결핵화염성가류，수궤추취60례작위실험집，60례작위험증집。실험집 HDCT 도상경도상예처리、감흥취구역（ROI）기우표기적분수령산법분할화 ROI 문리특정삼수제취，대획득적5항문리특정삼수주통계학처리，장통계결과응용우계통이대 SPN 주량악성분석병급출제시신식。험증집 HDCT 도상수입계통후，대비계통예측결과여주임의사화주원의사예측결과래평개계통적가고성。결과대비도、상관성、적、평은도화이계구 t 검험 P 치분별위0.000、0．002、0．914、0．295、0．002。대비도、상관성화이계구량성구간분별위（903，2003）、（2．76，3．48）、（0．01，1．54），악성구간분별위（502，898）、（3．49，3．71）、（1．79，29．86）。계통、주임의사화주원의사적민감도분별위83．3％、93．3％、76.7％，가양성솔분별위13．3％、16．7％、26．7％，정학솔분별위85％、88．3％、75％。결론기우표기적분수령산법대여흉벽혹종격점련적결절급마파리병변등균가이교호지장기분할제취출래。대비도、상관성화이계구유통계학의의。계통재예측 SPN 량악성구유교고적민감성화준학성급최저적가양성。 CAD 재 SPN 량악성진단구유일정적림상사용개치，본계통가이보조림상의사진단 SPN 량악성。
Objective In this paper ,we try to design a computered‐aided diagnostic system (CADS) of solitary pulmona‐ry nodule (SPN) based on high definition CT (HDCT ) images in order to improve the detection rate of malignant SPN and to make the diagnosis more objective and scientific .Methods 120 cases of SPN confirmed by pathology were collected ,in‐cluding malignant tumors ,benign tumors ,tuberculosis and inflammatory pseudotumors .60 cases were randomly selected as the experimental set ,60 cases as the validation set .We acquired five texture feature parameters from HDCT images of the experimental set by images preprocessed ,regions of interest (ROI) segmented by watershed algorithm based on the mark and texture feature parameters of ROI extracted .A statistical method was used to process the five texture feature pa‐rameters .The statistical results were applied in the CAD to differentiate malignant SPN from benign SPN and give messa‐ges .The HDCT images of the validation set were inputed in the CAD and we could evaluate the reliability of the CAD by comparing the forecasts of the CAD ,chief physicians and residencies .Results The results of t test of the five texture fea‐ture parameters were 0 .000 ,0 .002 ,0 .914 ,0 .295 ,0 .002 respectively .The benign ranges of the parameters were respec‐tively (903 ,2003) ,(2 .76 ,3 .48) ,(0 .01 ,1 .54) and the malignant ranges were (502 ,898) ,(3 .49 ,3 .71) ,(1.79 , 29.86) respectively .The sensitivity of the CAD was 83 .3% ,the false positive rate was 13 .3% and the correct rate was 85% .Conclusion The watershed algorithm based on the mark could better segment the nodules adjoined with the chest wall or mediastinum and the ground glass lesions .There was statistical significance in contrast ,correlation and energy .The CAD had higher sensitivity and accuracy and the lowest false positive .The CAD possessed certain clinical value in dif‐ferentiating malignant SPN from benign SPN and could assist clinicians .