A Shape-Aware Total Body Photography System for In-focus Surface Coverage Optimization

TL;DR

This paper introduces a shape-aware total body photography system that optimizes image resolution and sharpness over the entire body surface, enhancing skin lesion detection for skin cancer screening.

Contribution

It presents a novel system using depth and RGB cameras with 3D shape estimation to automatically optimize focus for full-body imaging, improving image quality over existing methods.

Findings

Achieves high resolution of 0.068 mm/pixel and 0.0566 mm/pixel.

Attains 85% and 95% in-focus surface coverage on simulation and real data.

Outperforms existing auto-focus protocols in focus quality.

Abstract

Total Body Photography (TBP) is becoming a useful screening tool for patients at high risk for skin cancer. While much progress has been made, existing TBP systems can be further improved for automatic detection and analysis of suspicious skin lesions, which is in part related to the resolution and sharpness of acquired images. This paper proposes a novel shape-aware TBP system automatically capturing full-body images while optimizing image quality in terms of resolution and sharpness over the body surface. The system uses depth and RGB cameras mounted on a 360-degree rotary beam, along with 3D body shape estimation and an in-focus surface optimization method to select the optimal focus distance for each camera pose. This allows for optimizing the focused coverage over the complex 3D geometry of the human body given the calibrated camera poses. We evaluate the effectiveness of the system in capturing high-fidelity body images. The proposed system achieves an average resolution of 0.068 mm/pixel and 0.0566 mm/pixel with approximately 85% and 95% of surface area in-focus, evaluated on simulation data of diverse body shapes and poses as well as a real scan of a mannequin respectively. Furthermore, the proposed shape-aware focus method outperforms existing focus protocols (e.g. auto-focus). We believe the high-fidelity imaging enabled by the proposed system will improve automated skin lesion analysis for skin cancer screening.