Introduction

Method and system designed to automatically identify any diseased cells (including cancer) in any human body tissue starting from an image of cell nuclei marked with immunofluorescence acquired through a confocal microscope.

Technical features

Method to automatically identify any diseased cells of any tissue (including cancer cells) starting from an image showing cell nuclei marked with the immunofluorescence technique and which has been acquired through a confocal microscope and to determine if a cell of interest is sick or healthy on the basis of results obtained by applying a plurality of statistical functions chosen to characterize the morphology of the cell of interest, in which said statistical functions are calculated starting from a co-occurrence matrix that characterizes the texture or size of the cell of interest. In the known art there is no such thing, it is possible to perform a classification between healthy and diseased cells of hepatocarcinoma on images of hepatic tissue, allowing an early diagnosis of the neoplasm. The diagnosis is not made by identifying changes in tissue color but on the morphological alteration and the texture variation of the single cell. The algorithm divides the images of sick / healthy cells into special folders on the desktop so that they can subsequently be checked by the pathologist for any diagnostic confirmations and is based on machine learning. The method is ready for use.

Possible Applications

• Research in the biological field
• Pharmacological research
• In medicine for the recognition of diseased cells

Advantages

• Ease of use in pathological anatomy laboratories without performing computers
• Diagnosis carried out on the morphological alteration and on the texture variation of the single cell
• Early diagnosis of the neoplasm