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Fixation and Sectioning

Fixation and Sectioning

Two basic types of preparation are used to visualize specimens with a light microscope: wet mounts and fixed specimens.
The simplest type of preparation is the wet mount, in which the specimen is placed in a drop of liquid on the slide. A liquid specimen can be directly deposited on the slide using a dropper. Solid specimens, such as skin scraping, can be placed on the slide before adding a drop of liquid to prepare the wet mount. Sometimes the liquid is simply water, but stains are often added...

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Related Experiment Video

Updated: Apr 24, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

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Two-tier tissue decomposition for histopathological image representation and classification.

Tunc Gultekin, Can Fahrettin Koyuncu, Cenk Sokmensuer

IEEE Transactions on Medical Imaging

|September 10, 2014

Summary

This study introduces a new method for representing histopathological images using multityped objects defined by texture, shape, and size. This approach improves automated diagnosis systems by providing more accurate classification of normal versus cancerous colon tissues.

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Area of Science:

Digital pathology
Computational biology
Medical image analysis

Background:

Automated diagnosis systems in digital pathology require effective image representations.
Object-based representations are preferred over pixel-based ones due to noise in histopathological images.
Previous object-based methods primarily used color information, limiting their representational power.

Purpose of the Study:

To develop a novel model for effective histopathological image representation for classification systems.
To improve the accuracy of automated diagnosis in digital pathology.

Main Methods:

A two-tier tissue decomposition method to define multityped objects based on texture, shape, and size.
Introduction of a 'dominant blob scale' metric to characterize object shape and size.
Experimental validation on colon tissue images.

Main Results:

The new object definition and characterization effectively distinguish between normal and cancerous histopathological images.
The proposed model achieved more accurate classification results compared to existing methods.
The multifeature object definition captures complex tissue characteristics.

Conclusions:

The novel object definition and characterization method enhances histopathological image representation.
This approach leads to improved classification accuracy for automated diagnosis systems.
The dominant blob scale metric provides a concise and informative characterization of image objects.