ReTouchImg: Fusioning from-local-to-global context detection and graph data structures for fully-automatic specular reflection removal for endoscopic images
| Autor: | Angelica I. Aviles-Rivero, James K. Hahn, Samar M. Alsaleh |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Diagnostic Imaging
Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Inpainting Health Informatics 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Robustness (computer science) Image Interpretation Computer-Assisted Minimally Invasive Surgical Procedures Radiology Nuclear Medicine and imaging Computer vision Specular reflection Image restoration Endoscopes Radiological and Ultrasound Technology business.industry Image Enhancement Data structure Computer Graphics and Computer-Aided Design Fully automatic Graph (abstract data type) Computer Vision and Pattern Recognition Artificial intelligence business Algorithms 030217 neurology & neurosurgery Endoscopic image |
| Zdroj: | Computerized Medical Imaging and Graphics. 73:39-48 |
| ISSN: | 0895-6111 |
| DOI: | 10.1016/j.compmedimag.2019.02.002 |
| Popis: | Minimally invasive surgical and diagnostic systems are commonly used in clinical practices. However, the accuracy and robustness of these systems depend heavily on computer based processes such as tracking, detecting or segmenting clinically meaningful regions of interest, which are significantly affected by the inherent specular reflections that appear on the organs' surfaces. Restoration of the acquired data for clinical purposes still presents challenges because of the high texture and color variations across the image. In this work, we propose a novel fully-automated solution for endoscopic image restoration, which we call ReTouchImg. Our approach is designed as a two-step scheme. The first is a detection step that is based on the synergy of a set of color variations and gradient information conditions. For the second step, we introduce an inpainting process which is based on graph data structures for recovering the missing information. We exhaustively evaluate our approach on real endoscopic datasets and compare it against some works from the body of literature. We also demonstrate that our solution deals with complex cases such as strong illumination variation and large affected areas through a careful quantitative evaluation of a range of numerical results. |
| Databáze: | OpenAIRE |
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