Comprehensive two-dimensional gas chromatography (GC × GC) can separate thousands of different compounds, and is used for many important applications such as petrochemical processing and environmental monitoring, etc. GC × GC generates rich and complex information, which requires automated processing for rapid chemical identification and classification. A challenge is to remove unwanted streaks that may affect the quantification and identification of analytes. It is difficult to detect streaks because of complex backgrounds, low-contrast data, and variable shapes, scales, and orientations of streaks in GC × GC data. This paper proposes a new approach to detect streaks effectively based on image analysis techniques. By adopting a pseudo-log function and preprocessing methods to compress the original data and enhance the low-contrast data, we employ steerable Gaussian filtering to delineate streak regions based on the specific orientations of streaks. A marker-controlled watershed algorithm is then used to segment the streaks, and highly discriminating characteristics are used to identify candidate regions and reject false streaks. In the end, with a diverse data set generated from gas chromatograph, experiments are carried out and the results demonstrate that our streak detection approach is effective and robust with respect to changes in streak patterns, even in variable chromatographic conditions. The proposed object detection method effective in complex backgrounds and low-contrast conditions is also helpful for object detection in other scenes.
- Image analysis
- Marker-controlled watershed algorithm
- Steerable Gaussian filtering
- Streak detection
- Two-dimensional chromatography
ASJC Scopus subject areas
- Artificial Intelligence