View source for 20240055102. IMAGE CLASSIFICATION METHOD, IN PARTICULAR MEDICAL IMAGES, FOR EXAMPLE RADIOGRAPHIC IMAGES simplified abstract (INOVA BIOMEDICAL TECHNOLOGY S.R.L.)

=IMAGE CLASSIFICATION METHOD, IN PARTICULAR MEDICAL IMAGES, FOR EXAMPLE RADIOGRAPHIC IMAGES=


==Organization Name==

[[:Category:INOVA BIOMEDICAL TECHNOLOGY S.R.L.|INOVA BIOMEDICAL TECHNOLOGY S.R.L.]]


[[Category:INOVA BIOMEDICAL TECHNOLOGY S.R.L.]]

==Inventor(s)==

[[:Category:Sabrina Conoci of Tremestieri Etnco (CT) (IT)|Sabrina Conoci of Tremestieri Etnco (CT) (IT)]][[Category:Sabrina Conoci of Tremestieri Etnco (CT) (IT)]]

[[:Category:Francesco Traina of Ferrara (IT)|Francesco Traina of Ferrara (IT)]][[Category:Francesco Traina of Ferrara (IT)]]

==IMAGE CLASSIFICATION METHOD, IN PARTICULAR MEDICAL IMAGES, FOR EXAMPLE RADIOGRAPHIC IMAGES - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240055102 titled 'IMAGE CLASSIFICATION METHOD, IN PARTICULAR MEDICAL IMAGES, FOR EXAMPLE RADIOGRAPHIC IMAGES

==Simplified Explanation==

The patent application describes a method for classifying medical images, such as radiographic images, to determine whether a sub-image containing a region of interest (ROI) with a portion of a limb and a prosthesis inserted into the same limb belongs to a first class of images with a probability p1 or to a second class of images with a probability p2.

* Classification process for medical images, specifically radiographic images
* Identifying sub-images with a region of interest (ROI) showing a limb and a prosthesis
* Determining if the sub-image belongs to a first class (c1) or a second class (c2) based on probabilities
* Analyzing the visibility of the limb and prosthesis in the sub-image

== Potential Applications ==
* Medical image analysis and classification
* Prosthesis evaluation and monitoring
* Automated detection of limb abnormalities

== Problems Solved ==
* Efficient classification of medical images
* Identification of limbs and prostheses in radiographic images
* Streamlining the analysis process for healthcare professionals

== Benefits ==
* Improved accuracy in classifying medical images
* Time-saving for healthcare providers
* Enhanced monitoring of prosthetic devices


==Original Abstract Submitted==

an image classification method, in particular medical images, for example radiographic images, wherein a sub-image rwhich contains, for example, a region of interest (roi) in which a portion of limb and a prosthesis inserted into the same limb are visible is subjected to a classification process designed to define whether the sub-image rbelongs to a first class c1 of images with a respective first probability p1 or to a second class of images c2 with a respective probability p2.



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