Authors :

Hafiz Ismail and Nurul Azman

Address :

Postdoctoral Fellow, Department of Robotics, Monash University Malaysia, Malaysia

Research Scientist, Department of Artificial Intelligence, Universiti Putra Malaysia, Malaysia

Abstract :

The use of acupuncture as a supplementary intervention has been more widely acknowledged in recent years; it is a vital component of traditional Chinese medicine that has been practiced in China for over two thousand years. Precise acupoint localization is thus an important process in acupuncture treatments and hand reflexology; hence, precise identification is critical for effective treatment. However, this accurate localization of acupoints in hand images still faces some challenges regarding variations in skin texture, hand structure, lighting conditions, and image quality. Current techniques do not present perfect accuracy on complex and noisy datasets, resulting in a lack of dependability in methods for therapeutic acupoint identification. This paper proposes an improved framework of the ALDAU-Net for Acupoint Localization (AL) in hand images by combining a U-shaped Network (U-Net) for pixel-level feature extraction and the Random Forest (RF) classification. Different data augmentation (DA) techniques, such as geometric transformation and intensity shifts, will be performed to create a more varied and robust training dataset that improves the model's generalization capability across different hand images. These segmented regions are then classified as an acupoint or a non-acupoint region using an RF classifier after feature extraction with U-Net. RF outperforms many other classifiers for noisy data and provides robust nonlinear classification based on the extracted features. The experimental results show that in difficult cases with a variation of hand orientation and changes in lighting conditions, the proposed U-Net + RF combination improves the localization accuracy by 30% compared to traditional methods. In that respect, the DA further augmented the model's generalisation capability, hence being highly effective when applied to a wide range of hand images.

Keywords :

Acupoint Localization, Data augmentation, U-Net, Random Forest, Feature Extraction, Hand Image Analysis.