PatchVision

Scientifica's PatchVision software has been developed to enhance patch clamp recording procedures by improving image quality and contrast during real time feeds, as well as recordings from tissue slices and cultured cells. Using live image processing to enhance image quality in real time, PatchVision enables researchers to work more effectively with lower cost microscope cameras with the benefit of digital image enhancement in electrophysiological experiments.

Major applications of the PatchVision software include:

• Enhancement of images of cultured cells and tissues (e.g. brain slices) obtained with transmitted light optics (differential interference contrast (DIC) and phase contrast)
• Fluorescence image enhancement – maximizing image contrast and increasing the effective dynamic range to detect weakly fluorescent cells
• Simple morphometric analysis

PatchVision Demonstration

Scientifica's PatchVision software has been developed to enhance patch clamp recording procedures by improving image quality and contrast during real time feeds, as well as recordings from tissue slices and cultured cells. Using live image processing to enhance image quality in real time, PatchVision enables researchers to work more effectively with lower cost microscope cameras with the benefit of digital image enhancement in electrophysiological experiments.

Major applications of the PatchVision software include:

• Enhancement of images of cultured cells and tissues (e.g. brain slices) obtained with transmitted light optics (differential interference contrast (DIC) and phase contrast)
• Fluorescence image enhancement – maximizing image contrast and increasing the effective dynamic range to detect weakly fluorescent cells
• Simple morphometric analysis

PatchVision Demonstration

The PatchVision application window has several controls which can be used to both reduce background noise and/or expand dynamic range and contrast. These features are particularly useful for enhancing images with high background noise and poor dynamic range eg. in a sequence of images in which weakly fluorescent cells are present against a 'noisy' image background.

Start Average

When a video sequence (.avi file) is opened, PatchVision will play the video in a continuous loop. When the Start Average button is clicked, an image processing algorithm is initiated which calculates and displays a cumulative average image where individual pixel intensity is averaged over the number of frames displayed. Thus, background noise is reduced in excessively noisy images and structures associated with low signal intensity (eg. weakly fluorescent cells) are more easily identifiable in the processed image. As the averaging process described is a cumulative average, the algorithm must be applied for a several seconds for effective background noise reduction to become apparent. Clicking Stop Average when the algorithm is in process terminates the image processing and opens up a dialog box which enables the user to save the average image as a .bmp file. Due to the nature of the cumulative average image processing algorithm employed, it is not recommended for use with video clips containing moving objects as the movement will be masked by the image processing procedure.

Normalise

Using the average function as described will dramatically improve background noise in images but will not improve dynamic range. Images containing low signal emitting structures of interest against a high noise background are marred both by high noise levels and poor contrast on account of the limited range of grey levels in the image. To improve the image contrast when the cumulative averaging alorithm is applied, the Normalise check box you can expand the dynamic range of the displayed image.

Apply Fourier

The Apply Fourier checkbox will apply the Fast Fourier Transform (FFT) filter to remove periodic low and high-frequency noise elements from the image for example vertical lines and repeating noise patterns.  

AutoEnhance Image

AutoEnhance Image can be used to improve the image contrast in either a live image feed direct from a microscope camera, video sequence, or a single bitmap image. The AutoEnhance Image function initiates an image histogram optimisation algorithm which operates continuously to improve contrast on each frame. When viewing a video sequence or a live image feed from a microscope, the grey levels of the image feed shown below the slider controls (eg. 24 and 36) will be constantly changing due to the fact that the image histogram will be changing frame by frame. AutoEnhance Image operates by expanding the range of grey levels in the image to display an image with a full range of grey levels from 0 to 255. In the image below, grey levels in the unprocessed image range from 144 to 255 resulting in a low contrast image. With the AutoEnhance Image feature applied the image contrast is greatly improved and, as the histogram shows, the range of grey levels is increased.

Effect of AutoEnhance Image algorithm on image contrast and histogram grey levels

When viewing a live image feed from a microscope camera AutoEnhance Image should be checked before adjusting the level of illumination intensity on the microscope. Once the microscope is focussed on the sample region of interest and illumination levels are set, the AutoEnhance Image box can be unchecked and contrast enhancement will remain constant. Min and Max slider controls do not alter the intrinsic image histogram but can be used to adjust the dynamic range of the image displayed on the monitor/screen.

Correct Background

The Correct Background function applies a median filter algorithm to the image to correct for spatial variation in background levels. This is used to activate the filter whilst the size of the pixel area (eg. 30) of the region used for the median filter calculations can be set using a slider control or simply typed into a box.

Pyramidal patch image image snapshot with and without background correction using 30 pixel area to define median filter parameters

Running average

The Running Average function applies an image processing algorithm which effectively reduces noise in the displayed image by displaying a running average image based on the number of frames specified by the user. Whilst this feature should not be used when viewing rapidly moving images, it can be used to reduce noise in a live image whilst approaching cells slowly with a patch pipette.

Image Flip

This feature is used to flip the orientation of the displayed image vertically or horizontally to achieve the correct on-screen image orientation. This feature should be used if the direct image-feed from the capture device results in a horizontally or vertically reversed image. By selecting the Flip Horizontally or Flip Vertically option as appropriate, the image can be viewed in the desired orientation. These options are not mutually exclusive and both can be applied simultaneously if required.

Using image optimisation to identify weakly fluorescent cells

PatchVision's image noise reduction and contrast enhancement capability is particularly useful in situations where signals from fluorescently labelled cells are too weak to allow clear identification of specific cells of interest within the field of view. Examples may include cells expressing very low levels of recombinant fluorescent photoproteins such as green fluorescent protein (GFP) or cells that have been fluorescently labelled by microinjection of fluorescent dyes. Once weakly fluorescent cells are identified by image enhancement within the PatchVision software, on-screen markers can be added to highlight the weakly fluorescent cells within the field of view.

Markers added to identify specific cells within the fluorescence image are maintained when the user switches back to a live transmitted light (eg. IR-DIC, IR-oblique) image which allows further investigation of the previously identified fluorescent cells. This powerful feature of the PatchVision software is particularly useful when dealing with mixed populations of cells and in situations where a sparse population of weakly fluorescent cells exists alongside a larger population of non-fluorescent cells. The process for this is shown below:

1 - Captured fluorescent image as video sequence which can be saved as an .avi file:

Snapshot from a typical weak signal fluorescence image before noise reduction and normalisation. 

2 - Video sequence using PatchVision's noise reduction & dynamic contrast normilisation to identify weakly fluorescent cells

Averaging and normalization algorithms applied.	Averaging, normalization and FFT filter settings applied	Averaging, normalization and FFT filter settings applied prior to using AutoEnhance to optimise displayed image contrast

3 - Markers added to optimised image

4 - This can then be switched back to a live transmitted light image (EG IR-DIC)

Ordering Information

* HASP USB Security dongle

Installation of the software requires a valid USB security dongle and activation of the HASP installation program provided on the PatchVision installation CD. If ordering a system with a PC included, the software will already be installed ready for use prior to shipping. If the HASP is not installed and activated correctly, the PatchVision software will not operate. 

> Download the PatchVision brochure (PDF)