Brain early infarct detection using gamma correction extreme-level eliminating with weighting distribution

Area of Science:

• Medical Imaging
• Image Processing
• Neurology

Background:

• Stroke is a leading global cause of death, necessitating accurate and timely diagnosis.
• Computed Tomography (CT) scans are crucial for diagnosing ischemic stroke, providing brain images in DICOM format.
• Standard CT image window settings often lack sufficient contrast for effective ischemic stroke detection.

Purpose of the Study:

• To introduce and evaluate a novel contrast enhancement technique, Gamma Correction Extreme-Level Eliminating with Weighting Distribution (GCELEWD), for CT brain images.
• To improve the visualization of hypodense regions indicative of ischemic stroke.
• To compare the performance of GCELEWD against existing contrast enhancement methods.

Main Methods:

• Implementation of the proposed GCELEWD algorithm for CT brain image contrast enhancement.
• Comparative analysis of GCELEWD with Brightness Preserving Bi-Histogram Equalization (BBHE), Dualistic Sub-Image Histogram Equalization (DSIHE), Extreme-Level Eliminating Histogram Equalization (ELEHE), and Adaptive Gamma Correction with Weighting Distribution (AGCWD).
• Quantitative evaluation using an Image Quality Assessment (IQA) module.

Main Results:

• GCELEWD effectively enhances contrast in CT brain images, highlighting hypodense areas crucial for ischemic stroke diagnosis.
• The proposed GCELEWD method demonstrated superior visualization capabilities compared to BBHE, DSIHE, ELEHE, and AGCWD.
• GCELEWD achieved higher scores in the image quality assessment module, indicating improved image fidelity.

Conclusions:

• GCELEWD is a promising technique for improving contrast in CT brain images for ischemic stroke detection.
• The method offers enhanced visualization and diagnostic support for medical professionals.
• GCELEWD represents an advancement in medical image processing for neurological applications.