PDF(Pubmed)
Abstract：
OBJECTIVE: To quantitatively measure ocular morphological parameters of guinea pig with Python technology.
METHODS: Thirty-six eyeballs of eighteen 3-week-old guinea pigs were measured with keratometer and photographed to obtain the horizontal, coronal, and sagittal planes respectively. The corresponding photo pixels-actual length ratio was acquired by a proportional scale. The edge coordinates were identified artificially by ginput function. Circle and conic curve fitting were applied to fit the contour of the eyeball in the sagittal, coronal and horizontal view. The curvature, curvature radius, eccentricity, tilt angle, corneal diameter, and binocular separation angle were calculated according to the geometric principles. Next, the eyeballs were removed, canny edge detection was applied to identify the contour of eyeball in vitro. The results were compared between in vivo and in vitro.
RESULTS: Regarding the corneal curvature and curvature radius on the horizontal and sagittal planes, no significant differences were observed among results in vivo, in vitro, and the keratometer. The horizontal and vertical binocular separation angles were 130.6°±6.39° and 129.8°±9.58° respectively. For the corneal curvature radius and eccentricity in vivo, significant differences were observed between horizontal and vertical planes.
CONCLUSIONS: The Graphical interface window of Python makes up the deficiency of edge detection, which requires too much definition in Matlab. There are significant differences between guinea pig and human beings, such as exotropic eye position, oblique oval eyeball, and obvious discrepancy of binoculus. This study helps evaluate objectively the ocular morphological parameters of small experimental animals in emmetropization research.
METHODS: Thirty-six eyeballs of eighteen 3-week-old guinea pigs were measured with keratometer and photographed to obtain the horizontal, coronal, and sagittal planes respectively. The corresponding photo pixels-actual length ratio was acquired by a proportional scale. The edge coordinates were identified artificially by ginput function. Circle and conic curve fitting were applied to fit the contour of the eyeball in the sagittal, coronal and horizontal view. The curvature, curvature radius, eccentricity, tilt angle, corneal diameter, and binocular separation angle were calculated according to the geometric principles. Next, the eyeballs were removed, canny edge detection was applied to identify the contour of eyeball in vitro. The results were compared between in vivo and in vitro.
RESULTS: Regarding the corneal curvature and curvature radius on the horizontal and sagittal planes, no significant differences were observed among results in vivo, in vitro, and the keratometer. The horizontal and vertical binocular separation angles were 130.6°±6.39° and 129.8°±9.58° respectively. For the corneal curvature radius and eccentricity in vivo, significant differences were observed between horizontal and vertical planes.
CONCLUSIONS: The Graphical interface window of Python makes up the deficiency of edge detection, which requires too much definition in Matlab. There are significant differences between guinea pig and human beings, such as exotropic eye position, oblique oval eyeball, and obvious discrepancy of binoculus. This study helps evaluate objectively the ocular morphological parameters of small experimental animals in emmetropization research.
摘要：
目的：应用Python技术定量测量豚鼠眼部形态学参数。
方法：用角膜曲率计测量18只3周龄豚鼠的36只眼球，日冕,分别为矢状面。通过比例标度获得相应的照片像素-实际长度比。边缘坐标由ginput函数人工识别。应用圆曲线拟合和圆锥曲线拟合来拟合矢状中的眼球轮廓，日冕和水平视图。曲率，曲率半径,偏心率,倾斜角度,角膜直径,根据几何原理计算双目分离角。接下来,眼球被移除，canny边缘检测应用于体外眼球轮廓的识别。在体内和体外之间比较结果。
结果：关于水平和矢状平面上的角膜曲率和曲率半径，在体内结果之间没有观察到显著差异，在体外，还有角膜曲率计.水平和垂直双眼分离角分别为130.6°±6.39°和129.8°±9.58°。对于体内的角膜曲率半径和偏心率，在水平和垂直平面之间观察到显着差异。
结论：Python的图形界面窗口弥补了边缘检测的不足，这在Matlab中需要太多的定义。豚鼠与人类之间存在显著差异,例如外倾的眼睛位置，斜椭圆形眼球，双眼差异明显。这项研究有助于客观地评估小实验动物的眼部形态参数。
方法：用角膜曲率计测量18只3周龄豚鼠的36只眼球，日冕,分别为矢状面。通过比例标度获得相应的照片像素-实际长度比。边缘坐标由ginput函数人工识别。应用圆曲线拟合和圆锥曲线拟合来拟合矢状中的眼球轮廓，日冕和水平视图。曲率，曲率半径,偏心率,倾斜角度,角膜直径,根据几何原理计算双目分离角。接下来,眼球被移除，canny边缘检测应用于体外眼球轮廓的识别。在体内和体外之间比较结果。
结果：关于水平和矢状平面上的角膜曲率和曲率半径，在体内结果之间没有观察到显著差异，在体外，还有角膜曲率计.水平和垂直双眼分离角分别为130.6°±6.39°和129.8°±9.58°。对于体内的角膜曲率半径和偏心率，在水平和垂直平面之间观察到显着差异。
结论：Python的图形界面窗口弥补了边缘检测的不足，这在Matlab中需要太多的定义。豚鼠与人类之间存在显著差异,例如外倾的眼睛位置，斜椭圆形眼球，双眼差异明显。这项研究有助于客观地评估小实验动物的眼部形态参数。
