An Improved Medical Image Watermarking Technique Based on Weber’s Law Descriptors

In distributed m-health communication, it is a major challenge to develop an efficient blind watermarking method to protect the confidential medical data of patients. This paper proposes an efficient blind watermarking for medical images, which boasts a very high embedding capacity, a good robustness, and a strong imperceptibility. Three techniques, namely, discrete cosine transform (DCT), Weber’s descriptors (WDs), and Arnold chaotic map, were integrated to our method. Specifically, the Arnold chaotic map was used to scramble the watermark image. Then, the medical image was partitioned into non-over lapping blocks, and each block was subjected to DCT. After that, the scrambled watermark image data were embedded in the middle-band DCT coefficients of each block, such that two bits were embedded in each block. Simulation results show that the proposed watermarking method provides better imperceptibility, robustness, and computational complexity results with higher embedding capacity than the contrastive method.

DCT based Fusion of Variable Exposure Images for HDRI

Combining images with different exposure settings are of prime importance in the field of computational photography. Both transform domain approach and filtering based approaches are possible for fusing multiple exposure images, to obtain the well-exposed image. We propose a Discrete Cosine Trans- form (DCT-based) approach for fusing multiple exposure images. The input image stack is processed in the transform domain by an averaging operation and the inverse transform is performed on the averaged image obtained to generate the fusion of multiple exposure image. The experimental observation leads us to the conjecture that the obtained DCT coefficients are indicators of parameters to measure well-exposedness, contrast and saturation as specified in the traditional exposure fusion based approach and the averaging performed indicates equal weights assigned to the DCT coefficients in this non- parametric and non pyramidal approach to fuse the multiple exposure stack.

Online handwriting Arabic recognition system using k-nearest neighbors classifier and DCT features

With advances in machine learning techniques, handwriting recognition systems have gained a great deal of importance. Lately, the increasing popularity of handheld computers, digital notebooks, and smartphones give the field of online handwriting recognition more interest. In this paper, we propose an enhanced method for the recognition of Arabic handwriting words using a directions-based segmentation technique and discrete cosine transform (DCT) coefficients as structural features. The main contribution of this research was combining a total of 18 structural features which were extracted by DCT coefficients and using the k-nearest neighbors (KNN) classifier to classify the segmented characters based on the extracted features. A dataset is used to validate the proposed method consisting of 2500 words in total. The obtained average 99.10% accuracy in recognition of handwritten characters shows that the proposed approach, through its multiple phases, is efficient in separating, distinguishing, and classifying Arabic handwritten characters using the KNN classifier. The availability of an online dataset of Arabic handwriting words is the main issue in this field. However, the dataset used will be available for research via the website.

Multi-Regression Fusion Based Blood Pressure Estimation

Using the traditional cuff based instruments to estimate the blood pressure (BP) values is inconvenient and difficult to have a continuous measurement. On the other hand, using the intelligent watch based instruments to perform the continuous BP estimation for monitoring the human health conditions is convenient and easy. This paper proposes a method for estimating the BP values continuously only using the photoplethysmogram (PPG). First, the PPGs are denoised by the discrete cosine transform (DCT). It is worth noting that the conventional DCT denoising approach only takes the low frequency DCT coefficients. On the other hand, this paper proposes a training based method for selecting the DCT AC coefficients. Here, the DCT AC coefficients refer to those non-DC DCT coefficients. Then, the features based on some specific points in the PPGs are extracted and the feature vectors are categriozed into two classes of the BP values using the random forest (RF) classifier. Third, for each class and each type of the BP values, three popular regressions including the support vector regression (SVR), the RF regression and the L1 norm criterion based linear regression are used to estimate the BP values. These three estimated BP values are fused together via an L2 norm based regression. It is worth noting that different classes and different types of the BP values are considered separately. Hence, each class and each type of the BP values can be estimated more accurately. Moreover, since the multi-model fusion is employed for combining these three regressions, the overall estimation results are more accurate. The computer numerical simulation results show that the average root mean squares errors (RMSEs) of the SBP and the DBP estimated by our proposed method are 9.12 and 8.19, respectively. In fact, our proposed multi-model fusion based BP estimation approach achieves a higher accuracy compared to the individual regressions.

A robust and high capacity data hiding method for JPEG compressed images with SVD-based block selection and advanced error correcting techniques

In this paper, we propose a frequency domain data hiding method for the JPEG compressed images. The proposed method embeds data in the DCT coefficients of the selected 8 × 8 blocks. According to the theories of Human Visual Systems (HVS), human vision is less sensitive to perturbation of pixel values in the uneven areas of the image. In this paper we propose a Singular Value Decomposition based image roughness measure (SVD-IRM) using which we select the coarse 8 × 8 blocks as data embedding destinations. Moreover, to make the embedded data more robust against re-compression attack and error due to transmission over noisy channels, we employ Turbo error correcting codes. The actual data embedding is done using a proposed variant of matrix encoding that is capable of embedding three bits by modifying only one bit in block of seven carrier features. We have carried out experiments to validate the performance and it is found that the proposed method achieves better payload capacity and visual quality and is more robust than some of the recent state-of-the-art methods proposed in the literature.