A Comparative Study of Structural Deformation Test Based on Edge Detection and Digital Image Correlation

Digital image-correlation (DIC) algorithms rely heavily on the accuracy of the initial values provided by whole-pixel search algorithms for structural displacement monitoring. When the measured displacement is too large or exceeds the search domain, the calculation time and memory consumption of the DIC algorithm will increase greatly, and even fail to obtain the correct result. The paper introduced two edge-detection algorithms, Canny and Zernike moments in digital image-processing (DIP) technology, to perform geometric fitting and sub-pixel positioning on the specific pattern target pasted on the measurement position, and to obtain the structural displacement according to the change of the target position before and after deformation. This paper compared the difference between edge detection and DIC in accuracy and calculation speed through numerical simulation, laboratory, and field tests. The study demonstrated that the structural displacement test based on edge detection is slightly inferior to the DIC algorithm in terms of accuracy and stability. As the search domain of the DIC algorithm becomes larger, its calculation speed decreases sharply, and is obviously slower than the Canny and Zernike moment algorithms.

Improved skin lesions detection using color space and artificial intelligence techniques

Background: Automatic skin lesion image identification is of utmost importance to develop a fully automatized computer-aided skin analysis system. This will be helping the medical practitioners to provide skin lesions disease treatment more efficiently and effectively.Material and method: In this article, two image processing techniques for accurate detection of skin lesions have been proposed. In first technique, the optimization of edge detection has been carried out by using a branch of artificial intelligence called nature inspired algorithm. Ant colony optimization (ACO) is used to increase effectiveness of edge detection in skin lesion. The second technique deals with the color space-based split-and-merge process in combination with global thresholding segmentation and edge smoothing operations.Result: The performance of both techniques has been measured by entropy performance evaluation parameter. The results show remarkable improvement in output images obtained by Canny edge detection technique optimized by ACO in comparison with ACO-Sobel, ACO-Prewitt and Edge Smoothing-Color Space techniques.Conclusion: ACO-Canny Edge detection technique shows far better effieciency for skin lesion detection as compared to ACO-Sobel, ACO-Prewitt and Edge Smoothing Color Space technique.

A new method for detecting the outer corneal contour in images from an ultra-fast Scheimpflug camera

Background

The Corvis^® ST tonometer is an innovative device which, by combining a classic non-contact tonometer with an ultra-fast Scheimpflug camera, provides a number of parameters allowing for the assessment of corneal biomechanics. The acquired biomechanical parameters improve medical diagnosis of selected eye diseases. One of the key elements in biomechanical measurements is the correct corneal contour detection, which is the basis for further calculations. The presented study deals with the problem of outer corneal edge detection based on a series of images from the afore-mentioned device. Corneal contour detection is the first and extremely important stage in the acquisition and analysis of corneal dynamic parameters.

Result

A total of 15,400 images from the Corvis^® ST tonometer acquired from 110 patients undergoing routine ophthalmologic examinations were analysed. A method of outer corneal edge detection on the basis of a series of images from the Corvis^® ST was proposed. The method was compared with known and commonly used edge detectors: Sobel, Roberts, and Canny operators, as well as others, known from the literature. The analysis was carried out in MATLAB^® version 9.0.0.341360 (R2016a) with the Image Processing Toolbox (version 9.4) and the Neural Network Toolbox (version 9.0). The method presented in this paper provided the smallest values of the mean error (0.16%), stability (standard deviation 0.19%) and resistance to noise, characteristic for Corvis^® ST tonometry tests, compared to the methods known from the literature. The errors were 5.78 ± 9.19%, 3.43 ± 6.21%, and 1.26 ± 3.11% for the Roberts, Sobel, and Canny methods, respectively.

Conclusions

The proposed new method for detecting the outer corneal contour increases the accuracy of intraocular pressure measurements. It can be used to analyse dynamic parameters of the cornea.