CAJ | 학술논문

目的采用矩阵实验室(MATLAB)图像处理技术构建一种非线性变换法,用以提高平扫头颅CT图像的脑灰、白质对比度.方法使用西门子definition型双源CT采集38例(女16例,男22例)在本院就诊怀疑脑部病变的患者脑正常平扫图像数据,测量脑灰、白质的平均CT值,并计算对应的像素值,对各例DICOM图像行MATLAB后处理.通过频域内的圆形滤波器,实现图像的高、低通滤波分离.采用基于灰度值调整的非线性变换法,以脑灰质和脑白质的平均灰度值为两个转折点,对图像的灰度进行拉伸,以增强图像的灰白质对比.结果 38例患者的正常脑平扫图像,经灰度值非线性变换法处理后,两转折点处灰度值差值较原始图像增加了1.5个像素值(0＜△p≤3)；脑灰质和脑白质对比度也明显提高；灰白质分界更加清楚；且处理后图像与原始图像保持接近.结论基于MATLAB图像处理软件的灰度值非线性变换法,能够在不增加噪声的前提下,有效增强平扫头颅CT图像的灰白质对比度,适用于任何CT机型采集的DICOM图像数据.
목적 채용구진실험실(MATLAB)도상처리기술구건일충비선성변환법,용이제고평소두로CT도상적뇌회、백질대비도.방법 사용서문자definition형쌍원CT채집38례(녀16례,남22례)재본원취진부의뇌부병변적환자뇌정상평소도상수거,측량뇌회、백질적평균CT치,병계산대응적상소치,대각례DICOM도상행MATLAB후처리.통과빈역내적원형려파기,실현도상적고、저통려파분리.채용기우회도치조정적비선성변환법,이뇌회질화뇌백질적평균회도치위량개전절점,대도상적회도진행랍신,이증강도상적회백질대비.결과 38례환자적정상뇌평소도상,경회도치비선성변환법처리후,량전절점처회도치차치교원시도상증가료1.5개상소치(0＜△p≤3)；뇌회질화뇌백질대비도야명현제고；회백질분계경가청초；차처리후도상여원시도상보지접근.결론 기우MATLAB도상처리연건적회도치비선성변환법,능구재불증가조성적전제하,유효증강평소두로CT도상적회백질대비도,괄용우임하CT궤형채집적DICOM도상수거.
Objective To improve the contrast between gray matter (GM) and white matter (WM) in patients undergoing plain cerebral computed tomography (CT) with nonlinear transformation using image processing technique of matrix laboratory (MATLAB).Methods Image processing technique of nonlinear transformation was established by MATLAB.Imaging data of plain cerebral CT in 38 patients wiht suspect brain diseases (16 females and 22 males) were collected from our hospital using Siemens Dual-source CT (Model definition) to determine the mean CT value of cerebral GM and WM and their pixels,followed by post- processing of DICOM images using MATLAB.DICOM images of high and low frequencies were separated via a round filter within frequency spectrum.The contrast between GM and WM was enhanced via stretching grey scales of the images,based on nonlinear transformation for modification,with the mean of cerebral GM and WM as breakover points respectively.Results CT images of above 38 cases were normal.Of 38 normal subjects,nonlinear transformation yielded an increase of 1.5Δp in the pixel (0＜Δp＜3) at both breakover points as compared with baseline level.This was associated with enhanced contrast and separationbetween GM and WM,as well as similar quality with the primitive images.Conclusion The nonlinear transformation approach for grey scale,based on MATLAB image processing software,can improve contrast between GM and WM in cerebral plain CT in a noise-free manner and can therefore be applied in capturiug DICOM images regardless of the type of CT scanners.