Novelty detection (ND) has gained attention in many applications for its effectiveness in dealing with imbalanced data. Many ND algorithms have been proposed. For example, the level set boundary description (LSBD) algorithm can accurately estimate a boundary around normal data which is subsequently used to detect novelties. However, the computational complexity and the convergence time of the LSBD algorithms increases substantially when data dimensionality increases. To solve those challenges, we propose an Integrated Autoencoder-Level Set Method (AE-LSM) for ND in this paper. The AE structure is employed to reduce the feature space with high dimensionality to a 3-dimensional (3D) space. The LSM algorithm is trained based on the compressed 3D data to identify the boundary of normal data. The AE-LSM has advantages of boundary control and good generalization performance. Experiments on 5 benchmark UCI datasets and an Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that the proposed AE-LSM present a 3%~14% significant improvement based on the average AUC (p<0.05) over the AE and LSBD algorithms across the six datasets.