Binocular Indirect Fundus Microscope in Vitreoretinal Surgery

With the rapid development of computer technology, the application of computer technology in various fields is more and more common, and it also plays an increasingly important role in biomedicine. In recent years, microscopic image processing has always been an important part of biomedicine, and binocular indirect fundus microscope is playing an increasingly important role in vitreoretinal surgery. The purpose of this paper is to study the application effect of binocular indirect fundus microscope in vitreoretinal surgery, and to master the role of binocular indirect fundus microscope, which is important for biomedicine. This paper studies the effect of binocular indirect fundus microscope in vitreoretinal surgery through the study of the role of vitreous and binocular indirect fundus microscope, as well as the investigation of experimental methods. It highlights that the effect of binocular indirect fundus microscope is better than that of direct microscope in retinal surgery.The results show that binocular indirect fundus microscope is more suitable for vitreoretinal surgery. 85% of the patients with vitreoretinal surgery have better effect after surgery. No matter from the comparison of visual acuity improvement or retinal thickness, binocular indirect fundus microscope has better effect in vitreoretinal surgery. It also provides reference for how to prevent vitreoretinal diseases disease has positive significance. We expect to produce effective methods as soon as possible to solve the problems related to vitreous diseases, which can bring the bright future to ophthalmic patients.

Characterization of Porous Cementitious Materials Using Microscopic Image Processing and X-ray CT Analysis

The use of lightweight concrete has continuously increased because it has a primary benefit of reducing dead load in a concrete infrastructure. Various properties of lightweight concrete, such as compressive strength, elastic modulus, sound absorption performance, and thermal insulation, are highly related to its pore characteristics. Consequently, the identification of the characteristics of its pores is an important task. This study performs a comparative analysis for characterizing the pores in cementitious materials using three different testing methods: a water absorption test, microscopic image processing, and X-ray computed tomography (X-ray CT) analysis. For all 12 porous cementitious materials, conventional water absorption test was conducted to obtain their water permeable porosities. Using the microscopic image processing method, various characteristics of pores were identified in terms of the 2D pore ratio (i.e., ratio of pore area to total surface area), the pore size, and the number of pores in the cross-sectional area. The 3D tomographic image-based X-ray CT analysis was conducted for the selected samples to show the 3D pore ratio (i.e., ratio of pore volume to total volume), the pore size, the spatial distribution of pores along the height direction of specimen, and open and closed pores. Based on the experimental results, the relationships of oven-dried density with these porosities were identified. Research findings revealed that the complementary use of these testing methods is beneficial for analyzing the characteristics of pores in cementitious materials.

Microscopic Image Processing for the Analysis of Nosema Disease

In this chapter, the authors tried to develop a tool to automatize and facilitate the detection of Nosema disease. This work develops new technologies in order to solve one of the bottlenecks found on the analysis bee population. The images contain various objects; moreover, this work will be structured on three main steps. The first step is focused on the detection and study of the objects of interest, which are Nosema cells. The second step is to study others' objects in the images: extract characteristics. The last step is to compare the other objects with Nosema. The authors can recognize their object of interest, determining where the edges of an object are, counting similar objects. Finally, the authors have images that contain only their objects of interest. The selection of an appropriate set of features is a fundamental challenge in pattern recognition problems, so the method makes use of segmentation techniques and computer vision. The authors believe that the attainment of this work will facilitate the diary work in many laboratories and provide measures that are more precise for biologists.