This research article presents a new feature detection algorithm called centWave for high-resolution liquid chromatography-mass spectrometry (LC/MS) data. The algorithm combines density-based detection of regions of interest (ROIs) in the m/z domain with a continuous wavelet transform (CWT) for chromatographic peak resolution. It is designed to detect features in complex LC/MS data with high sensitivity and accuracy. The algorithm was evaluated against two existing feature detection algorithms, matchedFilter (from XCMS) and centroidPicker (from MZmine), using dilution series and mixtures of plant extracts. The results showed that centWave achieved the highest recall and precision values, making it more effective at detecting features, especially those that are close or partially overlapping. centWave was integrated into the Bioconductor R-package XCMS and is available for use. It processes LC/MS data by first detecting ROIs, then applying CWT to resolve chromatographic peaks. The algorithm uses local baseline and noise estimation to improve sensitivity and accurately determine peak boundaries. The study also evaluated the performance of centWave using different parameter settings and found that it outperformed the other algorithms in terms of F-score, a combined measure of precision and recall. The algorithm was tested on complex biological samples, demonstrating its ability to reliably detect features even in challenging conditions. The results indicate that centWave is a robust and sensitive method for feature detection in high-resolution LC/MS data, which is essential for metabolomics experiments. The algorithm is available for use in the R-package XCMS and can be accessed via the Bioconductor website. The study highlights the importance of accurate feature detection in metabolomics and provides a reliable tool for this purpose.This research article presents a new feature detection algorithm called centWave for high-resolution liquid chromatography-mass spectrometry (LC/MS) data. The algorithm combines density-based detection of regions of interest (ROIs) in the m/z domain with a continuous wavelet transform (CWT) for chromatographic peak resolution. It is designed to detect features in complex LC/MS data with high sensitivity and accuracy. The algorithm was evaluated against two existing feature detection algorithms, matchedFilter (from XCMS) and centroidPicker (from MZmine), using dilution series and mixtures of plant extracts. The results showed that centWave achieved the highest recall and precision values, making it more effective at detecting features, especially those that are close or partially overlapping. centWave was integrated into the Bioconductor R-package XCMS and is available for use. It processes LC/MS data by first detecting ROIs, then applying CWT to resolve chromatographic peaks. The algorithm uses local baseline and noise estimation to improve sensitivity and accurately determine peak boundaries. The study also evaluated the performance of centWave using different parameter settings and found that it outperformed the other algorithms in terms of F-score, a combined measure of precision and recall. The algorithm was tested on complex biological samples, demonstrating its ability to reliably detect features even in challenging conditions. The results indicate that centWave is a robust and sensitive method for feature detection in high-resolution LC/MS data, which is essential for metabolomics experiments. The algorithm is available for use in the R-package XCMS and can be accessed via the Bioconductor website. The study highlights the importance of accurate feature detection in metabolomics and provides a reliable tool for this purpose.