We propose an automatic detection of the oropharyngeal area in PET-CT images. This detection can be used to preprocess images for efficient segmentation of Head and Neck (H&N) tumors in the cropped regions by a Convolutional Neural Network (CNN) for treatment planning and large-scale radiomics studies (e.g. prognosis prediction). The developed method is based on simple image processing steps to segment the brain on the PET image and retrieve a fixed size bounding box of the extended oropharyngeal region. We evaluate the results by measuring whether the primary Gross Tumor Volume (GTV) is fully contained in the bounding box. 194 out of 201 regions (96.5%) are correctly detected. The code is available on our GitHub repository.