Chronic wounds including diabetic foot ulcers, venous leg ulcers, and pressure ulcers are challenging to wound care professionals and takes up a significant amount of medical resources. Fully automatic segmentation of wound area in natural images is an important part of the diagnosis and prognosis since it is crucial to measure the area of the wound and provide quantitative parameters in the treatment. Traditionally hand-crafted features are derived based on image intensities and shape prior to wound area segmentation. With the advance of deep learning, various neural network models have gained great success in image analysis including semantic segmentation. Particularly, MobileNetV2 stands out among other approaches due to its lightweight architecture and uncompromised performance. This paper proposes a novel convolutional framework based on MobileNetV2 to segment wound regions from natural images. We also build an annotated wound image dataset consist of 1109 Foot Ulcer images taken from 889 patients to train and test the deep learning models. We demonstrate the effectiveness and mobility of our method by conducting comprehensive experiments and analysis on various segmentation neural networks, which are compared in terms of Dice coefficient, Precision and Recall. The full implementation is available at https://github.com/Pele324/ChronicWoundSeg.