A novel hybrid region-based active contour model is presented to segment medical images with intensity inhomogeneity. The energy functional for the proposed model consists of three weighted terms: global term, local term, and regularization term. The total energy is incorporated into a level set formulation with a level set regularization term, from which a curve evolution equation is derived for energy minimization. Experiments on some synthetic and real images demonstrate that our model is more efficient compared with the localizing region-based active contours (LRBAC) method, proposed by Lankton, and more robust compared with the Chan-Vese (C-V) active contour model.

Nowadays, natural scene statistics (NSS) based blind image quality assessment (BIQA) models trained by machine learning, tend to achieve excellent performance. However, BIQA is still a very challenging research topic due to the lack of reference images. The key of further improvement lies in feature mining and pooling strategy decision. In this work, a new BIQA model is proposed to utilize local normalized multi-scale difference of Gaussian (DoG) response in distorted images as features which show a high correlation with perceptual quality. Then, a three-stepframework based deep neural network (DNN) is designed and employed as the pooling strategy. Compared with the support vector machine (SVM), the proposed three-stepframework DNN can excavate better feature representation, leading to more accurate predictions and stronger generalization ability. The proposed model achieves stateof-the-art performance on two authoritative databases and excellent generalization ability in cross database experiments.Index Terms-Blind image quality assessment, deep neural network, stacked auto-encoder, DoG.

No abstract

No abstract

No abstract