Fractal Dimension-Based Infection Detection in Chest X-ray Images

Area of Science:

• Medical Imaging
• Fractal Geometry
• Diagnostic Analysis

Background:

• Accurate dimension detection in medical imaging is crucial for diagnosis but challenging due to fractal object shapes.
• Fractal dimension detection methodologies offer advanced tools for investigating medical images.
• Existing methods face challenges in extracting diagnostically significant information.

Purpose of the Study:

• To propose a novel methodology for fractal dimension detection in medical images.
• To evaluate the proposed method's effectiveness using the box counting technique.
• To compare the novel approach against state-of-the-art methods for diagnostic imaging.

Main Methods:

• A novel fractal dimension detection methodology based on the box counting technique is proposed.
• The method evaluates fractal dimensions of medical images.
• Performance is assessed by comparing with existing state-of-the-art approaches.

Main Results:

• The proposed fractal dimension detection technique shows improved outcomes compared to other methods.
• The algorithm effectively extracts diagnostically significant information from clinical images.
• Results graphically validate the mathematical derivation of the box counting approach in terms of Hurst exponent.

Conclusions:

• The novel box counting-based fractal dimension detection method is efficient for medical image analysis.
• This technique offers a promising tool for improving medical diagnosis accuracy.
• The study highlights the utility of fractal geometry in medical image investigation.