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Quantification of Stained Liver Cells
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In this experiment, Image processing techniques are applied for the quantification of the stained cells from an image of a liver tissue section.

 

 

Objective:

 

 

  • To automatically quantify the stained cells from  an image of a liver tissue section using online software. 

 

 

Theory:

 

The liver is the second largest organ in the human body after skin and is the largest internal organ. Liver is often called as both body’s manufacturing center and its filtering plant. This organ is so complex and is susceptible to a wide variety of diseases. Chronic liver fibrosis is one of the major public health problems which occur throughout the world. This is due to the excessive accumulation of tough, fibrous scar tissue including collagen in the liver. The accumulation of excessive collagen fiber deposits in the extracellular spaces of the liver cells causes the liver cells to lose blood infusion. Liver fibrosis is mainly caused due to viral infection, alcoholism or chemical toxicity which leads to most types of chronic liver diseases such as cirrhosis, liver failure and portal hyper tension and often requires liver transplantation. 

 

Quantification of liver fibrosis is essential for prognosis, diagnosis and evaluation of response to antifibrotic therapies. One of the most widely used methods to visualize fibrosis in liver tissue is by staining liver biopsy with appropriate dyes such as Trichrome, Picrosirius red, van Gieson etc. which further help for the quantification of collagen. Microscopic evaluation of trichrome-stained liver biopsies are commonly used method for liver specimen analysis. This analysis of stained specimens consists of identifying and counting stained cells. Visual interpretation of cells by the observer is not sensitive enough to detect small changes in fibrosis and also the visual observation of many samples is time-consuming, subjective and nonquantitative. These difficulties can be overcome by implementing an automated method for measuring and extracting the quantitative data from cell images. Computerized image analysis has lot of positives over visual analysis including reproducibility, rapidity, adaptability and the ability to simultaneously measure many features in the image.

 

ImageJ is a free tool for image processing and analysis using the platform java. ImageJ can count the total number of cells in a defined region. This software separates different color channels of the image, thresholds and performs cell counting and analysis. Image analysis of tissue sections using ImageJ is a modern accepted technique. Besides liver tissue analysis, ImageJ can also be used for counting of the nuclear or cytoplasmic staining samples. It can measure differences in staining intensity of a tissue sample. Using threshold feature, one can easily measure the area of the selected portion of the stained tissue depending on the intensity of staining. In ImageJ, there is a number of ways to express the data. In this example the area and the percentage stained area of the stained tissue sample is discussed.

 

 

 

 

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