Threshold selection using quadtrees

Evaluation of retinal microvasculature in exotropia with abnormal binocular vision by optical coherence tomography angiography

BACKGROUND

To explore the retinal microvasculature in large-angle concomitant exotropia patients with abnormal binocular vision using optical coherence tomography angiography (OCTA) analysis.

METHODS

OCTA images of 52 healthy and 100 strabismic eyes were analyzed to quantify the retinal thickness (RT), superficial capillary plexus (SCP), deep capillary plexus (DCP), and foveal avascular zone (FAZ). Paired t-tests were performed to compare differences between the two groups, the dominant eye and the deviated eye in the exotropia group, respectively. A p-value < 0.01 was considered significant.

RESULTS

The mean angle of deviation was 79.38 [± 25.64] (prism diopters, PD). There were significant differences in the DCP in deviated eyes between the exotropia group and the control group (fovea: p = 0.007; temporal: p = 0.014; nasal: p = 0.028; inferior: p = 0.013). The temporal SCP in the exotropia group was significantly higher than in the control group in deviated eyes (p = 0.020). No significant difference was found between dominant eyes and strabismic eyes (p > 0.01).

CONCLUSIONS

The study showed that OCTA revealed subnormal DCP in patients with large-angle exotropia and abnormal binocularity which may be related to retinal suppression. Changes in the macular microvasculature may provide valuable insights into the development of strabismus. Further studies are needed to determine the clinical relevance of this finding.

TRIAL REGISTRATION

This trial is registered as ChiCTR2100052577 at www.Chictr.org.cn .

Global Image Thresholding Adaptive Neuro-Fuzzy Inference System Trained with Fuzzy Inclusion and Entropy Measures

Thresholding algorithms segment an image into two parts (foreground and background) by producing a binary version of our initial input. It is a complex procedure (due to the distinctive characteristics of each image) which often constitutes the initial step of other image processing or computer vision applications. Global techniques calculate a single threshold for the whole image while local techniques calculate a different threshold for each pixel based on specific attributes of its local area. In some of our previous work, we introduced some specific fuzzy inclusion and entropy measures which we efficiently managed to use on both global and local thresholding. The general method which we presented was an open and adaptable procedure, it was free of sensitivity or bias parameters and it involved image classification, mathematical functions, a fuzzy symmetrical triangular number and some criteria of choosing between two possible thresholds. Here, we continue this research and try to avoid all these by automatically connecting our measures with the wanted threshold using some Artificial Neural Network (ANN). Using an ANN in image segmentation is not uncommon especially in the domain of medical images. However, our proposition involves the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) which means that all we need is a proper database. It is a simple and immediate method which could provide researchers with an alternative approach to the thresholding problem considering that they probably have at their disposal some appropriate and specialized data.

Directional Histogram Ratio at Random Probes: A Local Thresholding Criterion for Capillary Images

With the development of micron-scale imaging techniques, capillaries can be conveniently visualized using methods such as two-photon and whole mount microscopy. However, the presence of background staining, leaky vessels and the diffusion of small fluorescent molecules can lead to significant complexity in image analysis and loss of information necessary to accurately quantify vascular metrics. One solution to this problem is the development of accurate thresholding algorithms that reliably distinguish blood vessels from surrounding tissue. Although various thresholding algorithms have been proposed, our results suggest that without appropriate pre- or post-processing, the existing approaches may fail to obtain satisfactory results for capillary images that include areas of contamination. In this study, we propose a novel local thresholding algorithm, called directional histogram ratio at random probes (DHR-RP). This method explicitly considers the geometric features of tube-like objects in conducting image binarization, and has a reliable performance in distinguishing small vessels from either clean or contaminated background. Experimental and simulation studies suggest that our DHR-RP algorithm is superior over existing thresholding methods.

Image processing for femoral endosteal anatomy detection: description and testing of a computed tomography based program

A computed tomography (CT)-based image processing computer program was developed for three-dimensional (3D) femoral endosteal cavity shape modelling. For the examinations 50 cadaver femora were used. In the CT imaging 30 axial slices were taken above and below the lesser trochanter area from each femur. Different image analysis methods were used for femoral cavity detection depending on the structure of the processed slice. In the femoral shaft area simple thresholding methods succeeded, but in the problem areas of the metaphyseal femur edge, detection operators and local thresholding were required. In contour tracking several criteria were used to check the validity of the border pixels. The results were saved as four output data files: (i) a file for the longest anteroposterior (ap), mediolateral (ml) and oblique diameters computed by a Euclidian method, (ii) and (iii) files for 2D and 3D data respectively, and (iv) a file for centre points of each slice. Finally, testing of the results and dimensions obtained from the image analysis were carried out manually by sawing the femora into 10 stipulated horizontal slices. The ap and ml dimensions were measured with a caliper ruler. The CT-based image processing yielded a peak distribution of dimensions with a negative difference to those obtained in manual measurements. The mean difference between the image processing and the manual measurements was 1.1 mm (+/-0.7 mm, +/-1 SD). The difference was highest in the proximal slices of the femora of group I (with lowest cortical thickness), i.e. 1.3 mm (+/-0.8 mm) and lowest in the distal slices of the femora from group III (with highest cortical thickness), i.e. 0.9 mm (+/-0.6 mm). The results are acceptable for further use of the program to study endosteal anatomy for individual femoral component selection and designing basis.