Palmprint is widely used biometric feature in biometric and forensic applications, however it is still a challenging task to process the images and extract the useful data, because the evidence, left at the crime scenes, are usually deformed and distorted by artifacts. Palmprint contains different types of visible features that can be used for identification of criminals, including big details, like crease, ridge flow, and small details, like ridges, valleys and minutiae points. In this paper we focus on ridge extraction task. We demonstrate how integration of angular preference to an existing algorithm (Non-Halo Complex Matched Filtering, NH-CMF) may significantly improve quality of extracted features. We introduce the approach where NHCMF and automated analysis of magnitude weighted angle histogram are used for ridge pattern extraction and noise reduction. Extracted information may support or even automate ridge routing that is necessary for palmprint feature extraction in forensics.