Hair removal from skin cancer images using autoregressivedistribution estimation method

Abstract

The presence of hair in dermoscopic images poses a significant challenge to the accurate diagnosis of skin cancer. Traditional hair removal methods often compromise the underlying skin features, essential for precise image analysis. This research explores the application of the autoregressive (AR) distribution estimation method to remove hair from dermoscopic images while preserving critical diagnostic details. The proposed methodology involves pre-processing the images to detect hair, followed by employing the AR model to predict and replace the pixel values in hair-occluded regions based on the surrounding skin texture. Postprocessing techniques, including smoothing and enhancement, are applied to ensure seamless integration of the reconstructed regions with the original image. Evaluation of the method is conducted through visual inspection by dermatologists, quantitative metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and assessment of diagnostic performance on a dataset of dermoscopic images. Results indicate that the AR distribution estimation method significantly improves image quality and retains essential diagnostic features, enhancing the accuracy of skin cancer diagnosis. This study demonstrates that the autoregressive distribution estimation method offers a robust solution for hair removal in dermoscopic imaging, providing a foundation for further integration with advanced deep learning models to achieve even greater accuracy and efficiency in medical image processing.