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The inclusion of multimodal inputs improves the accuracy and dependability of smart healthcare systems. A user satisfaction monitoring system that uses multimodal inputs composed of users' facial images and speech is proposed in this paper. This smart healthcare system then sends multimodal inputs to the cloud. The inputs are processed and classified as fully satisfied, partly satisfied, or unsatisfied, and the results are sent to various stakeholders in the smart healthcare environment. Multiple image and speech features are extracted during cloud processing. Moreover, directional derivatives and a weber local descriptor is used for speech and image features, respectively. The features are then combined to form a multimodal signal, which is supplied to a classifier by support vector machine. Our proposed system achieves 93% accuracy for satisfaction detection.

This paper proposes a secure binary image steganography by minimizing the flipping distortion on the statistics of local texture pattern (LTP) and constructing flexible carriers of syndrome-trellis code (STC). Firstly, the change of LTP's statistic caused by flipping one pixel is employed to measure the flipping distortion of the corresponding pixel, which can well describe the flipping distortion on both statistics and vision. Secondly, we select the non-uniform blocks flexibly and reconstruct them as STC's carriers, which can access a scalable and nearly continuous capacity upper bound to accommodate to the different secret message lengths. Our experimental results show that, for one specific message length, the security on both statistics and vision is improved significantly when the message length is closed to the capacity upper bound. The comparisons with previous steganographic schemes demonstrate the superiority of the proposed scheme. [ABSTRACT FROM AUTHOR]

Highlights • A binary image steganography with higher security and image quality is proposed. • A new distortion measurement for both security and image quality is proposed. • The whole image is used to construct a more flexible message-carrier. • The proposed method outperforms the previous state-of-the-art methods. Abstract Most state-of-the-art binary image steganography methods depend on the content of the image to determine where to embed secret messages, which is capacity-limited and indicates that their distortion measurement may be not precise enough. In this paper, we propose a kind of distortion measurement that is not only based on the discrimination effects after flipping the pixels but also depends on the visual effects of flipping corresponding pixels, which is called joint distortion measurement. Instead of selecting suitable position to embed secret messages, we then employ the syndrome-trellis code to minimize the embedding distortion and get messages embedded. And experimental results have demonstrated that the proposed distortion measurement has higher performance and the steganography scheme can achieve stronger statistical security with high capacity and image quality. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel steganalytic scheme based on local texture pattern (LTP) to detect binary image steganography. We first assess how the expanded LTPs capture embedding distortions exactly. Considering curse of dimensionality when expanding LTPs, we employ Manhattan distance to measure the pixels correlation in a 5 × 5 sized block and select the pixels with closely correlation to remove some LTPs that are not interested. Although the stego image can maintain good visual quality, steganography scheme changes the inter-pixels correlation of binary image. Therefore we utilize totally 8192 LTPs histogram to define a 8192-dimensional steganalytic feature set. Original images and stego images are classified by ensemble classifier. Experimental results show that the proposed steganalytic method can more effectively detect state-of-the-art binary image steganography schemes compared with other steganalytic schemes. [ABSTRACT FROM AUTHOR]

Digital image processing techniques are very useful in abnormality detection in digital mammogram images. Nowadays, texture-based image segmentation of digital mammogram images is very popular due to its better accuracy and precision. Local binary pattern (LBP) descriptor has attracted many researchers working in the field of texture analysis of digital images. Because of its success, many texture descriptors have been introduced as variants of LBP. In this work, we propose a novel texture descriptor called generic weighted cubicle pattern (GWCP) and we analyzed the proposed operator for texture image classification. We also performed abnormality detection through mammogram image segmentation using k-Nearest Neighbors (KNN) algorithm and compared the performance of the proposed texture descriptor with LBP and other variants of LBP namely local ternary pattern (LTPT), extended local texture pattern (ELTP) and local texture pattern (LTPS). For evaluation, we used the performance metrics such as accuracy, error rate, sensitivity, specificity, under estimation fraction and over estimation fraction. The results prove that the proposed method outperforms other descriptors in terms of abnormality detection in mammogram images. [ABSTRACT FROM AUTHOR]

Abstract: The reproductive system is a specific system of organs working together for the purpose of reproduction. As one of the most significant characteristics of human cell, subcellular localization plays a critical role for understanding specific functions of mammalian proteins. In this study, we developed a novel computational protocol for predicting protein subcellular locations from microscope images of cells in human reproductive tissues. Three major steps are contained in this protocol, i.e., protein object identification, image feature extraction, and classification. We first separated protein and DNA staining in the images with both linear and non-negative matrix factorization separation methods; then we extracted protein multi-view global and local texture features including wavelet Haralick, local binary patterns, local ternary patterns, and the local quinary patterns; finally based on the selected important feature subset, we constructed an ensemble classifier with support vector machines for classifications. Experiments are performed on a benchmark dataset consisting of seven major subcellular classes in human reproductive tissues collected from human protein atlas. Our results show that the local texture pattern features play an important complementary role to global features for enhancing the prediction performance. An overall accuracy of 85% is obtained through current system, and when only confident classifications are considered, the accuracy can reach 99%. It is the first developed image based protein subcellular location predictor specifically for human reproductive tissue. The promising results indicate that the developed protocol can be applied for accurate large-scale protein subcellular localization annotations in human reproductive system. [Copyright &y& Elsevier]