The main aim of the project was to develop a novel algorithm that enable two-dimensional feature detection in an extremely sparse environment of Ion Mobility Imaging Mass Spectrometry (IM-IMS) measurements. For this, 2D Wavelet Transform Maxima is proposed. This led to construction of wavelet chains (ridges) in the wavelet transform space. Current thresholding methods for these chains were based on global thresholding of wavelet coefficients. Therefore, a thresholding method known as effective length-based thresholding is introduced. Here, length-based thresholding is modified to take the local nature of noise into account. The design parameters of the algorithm were evaluated using a synthetic IM-IMS data sample, and the performance of the algorithm was quantified using F-Score. The parameters that were studied were (i) width of the wavelet function (along mobility dimension), (ii) effective length, (iii) penalty factor used for calculation of local noise. Fair performance was obtained by choosing effective length equal to 4 and width equal to 1% of mobility dimension. There were a significant number of false positives still being detected along dominant mobilograms . The performance of the algorithm was compared with an existing feature detection algorithm on a real-world IM-IMS data sample and was found to be better in terms of number of features being detected.