An MRI brain disease classification system using PDFB-CT and GLCM with kernel-SVM for medical decision support

Multimedia Tools and Applications - The automatic binary classification of normal and abnormal subjects using magnetic resonance (MR) brain images has made remarkable progress in recent years. This...     

Advanced partial encryption using watermarking and scrambling in MP3

In this paper, we propose an advanced partial encryption of watermarking and scrambling using the magnitude information of Modifed Discrete Cosine Transform (MDCT). In MPEG-1/Audio Layer III (MP3), the magnitude and phase information of modified discrete cosine transform (MDCT) coefficients is encrypted. The proposed method uses both watermarking and scrambling, and aims at protecting the contents against eavesdropping and moreover against illegal mass distribution after descrambled. Experimental results show that the proposed method can achieve higher security and less computational complexity by reusing the MDCT coefficients obtained in MP3.     

Comparative analysis of Alzheimer's disease classification by CDR level using CNN,feature selection,and machine-learning techniques

Magnetic resonance imaging (MRI) of brain needs an impeccable analysis to investigate all its structure and pattern. This analysis may be a sharp visual analysis by an experienced medical professional or by a computer aided diagnosis system that can help to predict, what may be the recent condition. Similarly, on the basis of various information and technique, a system can be designed to detect whether a patient is prone to Alzheimer's disease or not. And this task of detection of abnormalities at an initial stage from brain MRI is a major challenge in the field of neurosciences. The main idea behind our research is to utilize the deep layers feature extraction benefited from deep neural network architecture, without extensive hardware resource training, and classifying the image on a basis of simple machine-learning algorithm with selected best features in order to reduce work load, classification error and hardware utilization time. We have utilized convolution neural network (CNN) layer using similar architecture like that of Alexnet with some parametric change, for the automatic extraction of features of images obtained from slice extraction of whole brain MRI whereas 13 manual features based on gray level co-occurrence matrix were also extracted to test the impact of this features on ranking. If we had only classified using CNN network, the misclassification rate was much higher. So, feature selection is achieved with feature ranking algorithms like Mutinffs, ReliefF, Laplacian and UDFS and so on and also tested with different machine-learning techniques like Support Vector Machine, K-Nearest Neighbor and Subspace Ensemble under different testing condition. The performance of the result is satisfactory with classification accuracy around 98% to 99% with 7:3 ratio of random holdout partition of training to testing image sets and also with fivefolds of cross-validation on the same set using a standardized template.     

SVD based improved secret fragment visible mosaic image generation for information hiding

This paper proposes a new method for information hiding based on secret fragment visible mosaic image. This algorithm incorporates the application of color transfer for the mosaic image generation to hide the secret information. Since the RGB color space has strong correlation between three color channels, the modification process of any color is complicated in this color space. The large size of bit stream of recovery information of secret image block sequence creates an additional problem. We present a new way to solve these problems using the color transfer technique in correlated color space. The pixel value is considered as a three dimensional stochastic variable and image as a set of samples. Color transformation is achieved by the geometrical transformations such as translation, scaling and rotation. The correlation between the color spaces is measured and further used for the color transfer process by exploring the covariance. In order to get the robust encryption and lower bit rate requirement, the combination of logistic map and Chebyshev map is used to generate the encryption sequence of the secret image.     

Erratum to： An advanced segmentation using area and boundary tracing technique in extraction of lungs region

Erratum to：J.Cent.South Univ.（2014）21：3811-3820DOI：10.1007/s11771-014-2366-9The original version of this article unfortunately contained three mistakes.The mistakes are corrected as follows：1）The spelling of third author is incorrect.The correct name is Jae-Young PYUN.2）The information of corresponding author is incorrect.The correct information should be Goo-Rak KWON,Professor,PhD;Tel/Fax：＋98-711-7264102;E-mail：grkwon@chosun.ac.kr     

Double random phase encoding with a Poisson-multinomial distribution for efficient colorful image authentication

Multimedia Tools and Applications - In this paper, a new integrated approach is proposed for authenticating digital color image. Here, a new technique, Poisson-Multinomial Distribution (PMD) is...