A novel skin pigment separation method based on sub-block selection and local clustering

Area of Science:

• Dermatology
• Medical Aesthetics
• Image Analysis

Background:

• Skin pigment separation is crucial for medical aesthetics, clinical analysis, and dermatological diagnosis.
• Current methods struggle with accurate differentiation between melanin and hemoglobin.

Purpose of the Study:

• To develop an advanced method for precise skin pigment separation.
• To overcome the limitations of existing techniques in distinguishing melanin and hemoglobin.

Main Methods:

• A novel approach combining a sub-block selection algorithm and local clustering for skin pigment separation.
• Sub-block selection sorts data based on pixel differences for accurate melanin and hemoglobin separation.
• Local clustering utilizes Euclidean distance for sample point assignment, ensuring Independent Component Analysis convergence.

Main Results:

• The proposed method demonstrates accurate and precise separation of skin pigments.
• Achieved an average success convergence rate of 92%, exceeding current methods.
• Experimental evaluations confirm the efficacy of the novel approach.

Conclusions:

• The developed method offers a significant advancement in skin pigment separation.
• It provides a more accurate and reliable tool for dermatological diagnosis and analysis.
• The high convergence rate indicates robustness and effectiveness.