X-RAY ENHANCEMENT BASED ON COMPONENT ATTENUATION, CONTRAST ADJUSTMENT, AND IMAGE FUSION

Abstract

Inspecting X-ray images is an essential aspect of medical diagnosis. However, due to an X-ray’s low contrast and low dynamic range, important aspects such as organs, bones, and nodules become difficult to identify. Hence, contrast adjustment is critical, especially in view of its ability to enhance the details in both bright and dark regions. For X-ray image enhancement, we therefore propose a new concept based on component attenuation. Notably, we assumed an X-ray image could be decomposed into tissue components and important details. Since tissues may not be the major primary of an X-ray, we proposed enhancing the visual contrast by adaptive tissue attenuation and dynamic range stretching. Via component decomposition and tissue attenuation, a parametric adjustment model was deduced to generate many enhanced images at once. Finally, an ensemble framework was proposed for fusing these enhanced images and producing a high contrast output in both bright and dark regions. We have used measurement metrics to evaluate our system and achieved promising scores in each. An online testing system was also built for subjective evaluation. Moreover, we applied our system to an X-ray dataset provided by the Japanese Society of Radiological Technology to help with nodule detection. The experimental results of which demonstrated the effectiveness of our method.

Existing System

Contextual and variational contrast enhancement algorithm (CVC), spatial entropy-based contrast enhancement algorithm in DCT (SECEDCT)

Proposed System

The proposed method enhancing the visual contrast by adaptive tissue attenuation and dynamic range stretching. Via component decomposition and tissue attenuation, a parametric adjustment model was deduced to generate many enhanced images at once. Finally, an ensemble framework was proposed for fusing these enhanced images and producing a high contrast output in both bright and dark regions. This method used measurement metrics to evaluate our system and achieved promising scores in each. An online testing system was also built for subjective evaluation.

Architecture

BLOCK DIAGRAM