Examination of morphological and synaptic features of calbindin-immunoreactive neurons in deep layers of the rat olfactory bulb with correlative laser and volume electron microscopy

Automated Drawing Tube (Camera Lucida) Method in Light Microscopy Images Analysis Can Comes True

BACKGROUND

In a light microscope, image acquisition with different component depths is difficult, and there are various approaches for solving this problem. One of the common approaches is Camera Lucida (CL). This method has some disadvantages such as time-consuming, handed problems in painting, causing user boring, and produce gray scale output images.

AIMS AND OBJECTIVES

In this study, we purposed a novel-combined hardware and software method. In this article, we try to present an automated method for our designed microscope.

MATERIALS AND METHODS

We have done a project with designed code number 377,694 to design and implement an upgraded light microscope. That project was about automatic movement of a stage with closed-looped control of a servomotor. Furthermore, automated camera catches images in predefined positions. That project has acceptable results in different parts, which encourage us to work on this study. This study help specialist have good fixative of all components in a sample. It is about trying to have useful Lucida Camera (drawing tube) in an automated scheme.

RESULTS

This method is an acceptable usual way for microscopic specialists, but with some disadvantages. It is time-consuming and boring that effect on the accuracy of results. Hence, how can be good if automated similar method could be implemented is exciting and affective. This studies idea comes from the basis of manual drawing tube (CL) method. In this experimental study, we have taken 400 handed an image of microorganisms. Captured images are from its whole body or various organs. They have been captured in different z-axis positions of stage, and hence components with different depths could be focused. Each patch checked for its edge strength to choose highest resolutions sub image and reconstruct focused image like a puzzle. This process has been continued for all areas to merge and complete reconstructed image as output.

CONCLUSION

Comparing edge strength with other images and mean square error with manual focused on confirm our method with pleasure outcomes. Furthermore, independent focusing of an internal component in a sample body has been surveyed. It helps to have better resolution in internal selected component for more analysis and replace in its primitive image. This article presents efficient consequences with good accuracy and saving time in process period, which could be useful in different microscopes types and various samples type.