New SVD based initialization strategy for Non-negative Matrix Factorization

There are two problems need to be dealt with for Non-negative Matrix Factorization (NMF): choose a suitable rank of the factorization and provide a good initialization method for NMF algorithms. This paper aims to solve these two problems using Singular Value Decomposition (SVD). At first we extract the number of main components as the rank, actually this method is inspired from [1, 2]. Second, we use the singular value and its vectors to initialize NMF algorithm. In 2008, Boutsidis and Gollopoulos [3] provided the method titled NNDSVD to enhance initialization of NMF algorithms. They extracted the positive section and respective singular triplet information of the unit matrices {C(j)}k j=1 which were obtained from singular vector pairs. This strategy aims to use positive section to cope with negative elements of the singular vectors, but in experiments we found that even replacing negative elements by their absolute values could get better results than NNDSVD. Hence, we give another method based SVD to fulfil initialization for NMF algorithms (SVD-NMF). Numerical experiments on two face databases ORL and YALE [16, 17] show that our method is better than NNDSVD

Numerical Methods for Pulmonary Image Registration

Due to complexity and invisibility of human organs, diagnosticians need to analyze medical images to determine where the lesion region is, and which kind of disease is, in order to make precise diagnoses. For satisfying clinical purposes through analyzing medical images, registration plays an essential role. For instance, in Image-Guided Interventions (IGI) and computer-aided surgeries, patient anatomy is registered to preoperative images to guide surgeons complete procedures. Medical image registration is also very useful in surgical planning, monitoring disease progression and for atlas construction. Due to the significance, the theories, methods, and implementation method of image registration constitute fundamental knowledge in educational training for medical specialists. In this chapter, we focus on image registration of a specific human organ, i.e. the lung, which is prone to be lesioned. For pulmonary image registration, the improvement of the accuracy and how to obtain it in order to achieve clinical purposes represents an important problem which should seriously be addressed. In this chapter, we provide a survey which focuses on the role of image registration in educational training together with the state-of-the-art of pulmonary image registration. In the first part, we describe clinical applications of image registration introducing artificial organs in Simulation-based Education. In the second part, we summarize the common methods used in pulmonary image registration and analyze popular papers to obtain a survey of pulmonary image registration

Polymorphism of the 86th amino acid in CX26 protein and hereditary deafness

Objective: To investigate the membrane localization function of the CX26 protein when its 86th amino acid is Thr, Ser or Arg, and its relations to deafness. Methods: CX26-GFP protein with either Thr, Ser or Arg as the 86th amino acid was expressed in mouse SGN cells via the GFP fusion type lenti-virus expression system. The membrane localization of the fusion protein was observed under a fluorescence microscope. Results: The mutated protein of CX26 T86S was localized to cell membrane and form gap conjunction structures, showing no difference to the wild type CX26 protein (with Thr as the 86th amino acid). However, the gap conjunction structure disappeared when the mutation was CX26 T86A. Conclusion: These results indicate that the CX26 T86R mutation may be a cause of hearing loss, but CX26 T86S as a non-pathogenic polymorphism mutation does not affect functions of the CX26 protein. The results are in accordance with the results of clinical screening