Edge detection and mathematic fitting for corneal surface with Matlab software

Digital measuring the ocular morphological parameters of guinea pig eye in vivo with Python

AIM

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.

CONCLUSION

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.

Prediction of condylar movement envelope surface based on facial morphology

The present study aimed to predict the envelope surfaces from facial morphology. Condylar envelope surfaces for 34 healthy adults were formed and simplified as sagittal section curves. Cephalometric and maximum mandibular moving distances measurement were performed on the participants. There was no statistically significant difference (p = 0.763) between the left and right maximum lateral movements. There was a statistically significant difference in the mandibular body length between the sexes. The envelope surfaces were divided into type 1 with H_p2 ≥ 1/3 H_p1 and type 2 with H_p2 < 1/3 × H_p1. SNA and SNB for type 2 were significantly greater than those for type 1 (p < 0.001). Therefore, the participants were divided into four groups based on gender and envelope surface morphology. The curves could be fitted using the second-order Fourier function (R-square ≥0.95). Six facial parameters were selected and a matrix was used to map facial morphology to the envelope surface. Individual sagittal curves were predicted using the matrix and facial parameters, and the envelope surface was predicted using the curve and the condyle model. Deviation analysis for the predicted envelope surface using the actual envelope as a reference was carried out (root mean square = 0.9970 mm ± 0.2918 mm). This method may lay a foundation for the geometric design of artificial fossa components of temporomandibular joint replacement systems. It may improve prosthesis design without flexible tissue repair and guide the movement of the artificial joint head.

Application of 3-Dimensional Technology for Evaluating Muscular Type and Muscle-Fat Pad Mixed-Type Nasolabial Folds With Botulinum Toxin-A Treatment

BACKGROUND

Botulinum toxin-A (BTX-A) is used in the treatment of nasolabial folds (NLFs). However, lighting and clinician subjectivity play a major role in evaluating the efficacy of this treatment.

OBJECTIVES

By applying 3-dimensional (3D) technology, this study aimed to quantitatively evaluate the effects of BTX-A injection on muscular (M) and muscle-fat pad mixed-type (MF) NLFs.

METHODS

BTX-A was injected into bilateral marked points on the NLFs, where the levator labii alaeque nasi, zygomaticus minor, and zygomaticus major pull the skin to form the NLF (2 U at each injection site). Pretreatment and posttreatment 3D facial images were captured with static and laughing expressions. The curvature, width, depth, and lateral fat volume of the NLFs were measured to compare the therapeutic efficacy for type M and MF NLFs.

RESULTS

Thirty-nine patients with type M and 37 with type MF NLFs completed the follow-up data. In these patients, the curvature, width, and depth of the NLF showed a significant reduction at 1 month and gradually recovered at 3 and 6 months after treatment, with more significant improvement when laughing than when static. Variations compared to the pretreatment values of type MF were greater than those of type M at each time point. The lateral fat volume of the type MF NLF was significantly reduced (P < .05).

CONCLUSIONS

3D technology can quantitatively evaluate the effects BTX-A injection for treating type M and type MF NLFs. BTX-A is more effective on type MF than on type M NLFs.

LEVEL OF EVIDENCE: 4

A pitfall of using the circular-edge technique with image averaging for spatial resolution measurement in iteratively reconstructed CT images

The circular‐edge technique using a low‐contrast cylindrical object is commonly used to measure the modulation transfer functions (MTFs) in computed tomography (CT) images reconstructed with iterative reconstruction (IR) algorithms. This method generally entails averaging multiple images of the cylinder to reduce the image noise. We suspected that the cylinder edge shape depicted in the IR images might exhibit slight deformation with respect to the true shape because of the intrinsic nonlinearity of IR algorithms. Image averaging can reduce the image noise, but does not effectively improve the deformation of the edge shape; thereby causing errors in the MTF measurements. We address this issue and propose a method to correct the MTF. We scanned a phantom including cylindrical objects with a CT scanner (Ingenuity Elite, Philips Healthcare). We obtained cylinder images with iterative model reconstruction (IMR) algorithms. The images suggested that the depicted edge shape deforms and fluctuates depending on slice positions. Because of this deformation, image averaging can potentially cause additional blurring. We define the deformation function D that describes the additional blurring, and obtain D by analyzing multiple images. The MTF measured by the circular‐edge method (referred to as MTF') can be thought of as the multiplication of the true MTF by the Fourier transformation (FT) of D. We thus obtain the corrected MTF (MTF_corrected) by dividing MTF' by the FT of D. We validate our correction method by comparing the calculated images based on the convolution theorem using MTF' and MTF_corrected with the actual images obtained with the scanner. The calculated image using MTF_corrected is more similar to the actual image compared with the image calculated using MTF', particularly in edge regions. We describe a pitfall in MTF measurement using the circular‐edge technique with image averaging, and suggest a method to correct it.