Afaf Tareef

A highly secure oblivious sparse coding-based watermarking system for ownership verification

A novel sparse coding-based watermarking system is proposed to tackle security issues.The watermark is encoded as a function of its carrier using host-based dictionary.Our system improves the imperceptibility and robustness of image watermarking.The high compression and the strong encryption are some of sparse coding advantages. In the last few decades, the watermarking security issue has become one of the main challenges facing the design of watermarking techniques. In this paper, a secure oblivious watermarking system, based on Sparse Coding (SC) is proposed in order to tackle the three most critical watermarking security problems, i.e., unauthorized reading, false positive detection, and multiple claims of ownership problems, as well as optimize the fidelity, imperceptibility, and robustness characteristics. The reason for incorporating SC in the proposed system is to encode the watermark image before embedding it in the host image. This process is implemented using the well-known Stagewise Orthogonal Matching Pursuit (StOMP) method and an orthogonal dictionary that is derived from the host image itself. The watermark embedding is implemented in the transform domain of the Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) of the host image. The proposed system is oblivious, as it does not need the original host image when extracting the embedded watermark. In addition, it is suitable for both bi-level and gray-level watermarks, and can accommodate large watermarks that are up to half the size of the host image. The proposed SC-DWT-SVD based watermarking scheme is tested for various malicious and un-malicious attacks and the experimental results show that it realizes the security requirement as it tackles the false positive detection and multiple claims of ownership problems on one hand and generates an encryption form of the watermark on the other hand. In addition, the added security does not compromise the imperceptibility and robustness aspects of the proposed technique and hence can be considered to be comparable or superior to other up-to-date watermarking techniques.

Automated three-stage nucleus and cytoplasm segmentation of overlapping cells

Developing segmentation techniques for overlapping cells has become a major hurdle for automated analysis of cervical cells. In this paper, an automated three-stage segmentation approach to segment the nucleus and cytoplasm of each overlapping cell is described. First, superpixel clustering is conducted to segment the image into small coherent clusters that are used to generate a refined superpixel map. The refined superpixel map is passed to an adaptive thresholding step to initially segment the image into cellular clumps and background. Second, a linear classifier with superpixel-based features is designed to finalize the separation between nuclei and cytoplasm. Finally, edge and region based cell segmentation are performed based on edge enhancement process, gradient thresholding, morphological operations, and region properties evaluation on all detected nuclei and cytoplasm pairs. The proposed framework has been evaluated using the ISBI 2014 challenge dataset. The dataset consists of 45 synthetic cell images, yielding 270 cells in total. Compared with the state-of-the-art approaches, our approach provides more accurate nuclei boundaries, as well as successfully segments most of overlapping cells.

Automatic segmentation of overlapping cervical smear cells based on local distinctive features and guided shape deformation

Automated segmentation of cells from cervical smears poses great challenge to biomedical image analysis because of the noisy and complex background, poor cytoplasmic contrast and the presence of fuzzy and overlapping cells. In this paper, we propose an automated segmentation method for the nucleus and cytoplasm in a cluster of cervical cells based on distinctive local features and guided sparse shape deformation. Our proposed approach is performed in two stages: segmentation of nuclei and cellular clusters, and segmentation of overlapping cytoplasm. In the first stage, a set of local discriminative shape and appearance cues of image superpixels is incorporated and classified by the Support Vector Machine (SVM) to segment the image into nuclei, cellular clusters, and background. In the second stage, a robust shape deformation framework is proposed, based on Sparse Coding (SC) theory and guided by representative shape features, to construct the cytoplasmic shape of each overlapping cell. Then, the obtained shape is refined by the Distance Regularized Level Set Evolution (DRLSE) model. We evaluated our approach using the ISBI 2014 challenge dataset, which has 135 synthetic cell images for a total of 810 cells. Our results show that our approach outperformed existing approaches in segmenting overlapping cells and obtaining accurate nuclear boundaries. HighlightsA fully automated segmentation method is proposed for overlapping cervical cells.Our approach is based on superpixel-based features and guided shape deformation.Our shape initialization procedure is able to work with the different cell types.The practicality of our approach in segmenting highly overlapping cells is proved.Our approach outperformed existing approaches in nuclei and cytoplasm segmentation.

Automatic nuclei and cytoplasm segmentation of leukocytes with color and texture-based image enhancement

Designing a single automatic and accurate segmentation approach for different classes of white blood cells is a challenging task. This paper presents a fully automated segmentation framework to segment both nuclei and cytoplasm of five major classes of white blood cells in the peripheral blood smears based on color and texture enhancement. Particularly, a new gray-scale transform is generated based on three representative color channels to separate the nuclei from the cytoplasm and background by Poisson distribution based minimum error thresholding. For cytoplasm segmentation, discrete wavelet transform (DWT) and morphological filtering based enhancement procedure is utilized to highlight the cytoplasm and eliminate the small details inside the cells. Finally level set-based refinement and false candidates filtering are applied to obtain the accurate cell segmentation. The proposed approach is evaluated on two sets of peripheral blood smears, and it demonstrates improved segmentation performance when compared with existing methods.

A novel tamper detection-recovery and watermarking system for medical image authentication and EPR hiding.

Recently, the literature has witnessed an increasing interest in the study of medical image watermarking and recovery techniques. In this article, a novel image tamper localization and recovery technique for medical image authentication is proposed. The sparse coding of the Electronic Patient Record (EPR) and the reshaped region of Interest (ROI) is embedded in the transform domain of the Region of Non-Interest (RONI). The first part of the sparse coded watermark is use for saving the patient information along with the image, whereas the second part is used for authentication purpose. When the watermarked image is tampered during transmission between hospitals and medical clinics, the embedded sparse coded ROI can be extracted to recover the tampered image. The experimental results demonstrate the efficiency of the proposed technique in term of tamper correction capability, robustness to attacks, and imperceptibility.

Morphological Filtering and Hierarchical Deformation for Partially Overlapping Cell Segmentation

Accurate cell segmentation is an important and long-standing challenge in biomedical image analysis due to large variations in shape and boundary ambiguity. In this paper, we present a segmentation framework for partially overlapping cervical cells. The proposed method starts by cellular clump estimation with morphological reconstruction. Subsequently, the nuclei inside the cellular clumps are located by H-maxima transformation and thresholding. The cytoplasm of each detected nucleus is finally delineated with hierarchical deformation based on landmarks and shape dictionaries. The proposed approach is tested on a cervical smear image dataset containing single and partially overlapping cells. The results demonstrate that our approach can achieve more accurate and stable cytoplasmic segmentation, better nuclear segmentation, and lower time complexity, compared to a state-of-the-art approach.

Automated multi-stage segmentation of white blood cells via optimizing color processing

Segmentation of white blood cells (i.e. leukocytes) is a crucial step toward the development of haematological images analysis of peripheral blood smears. This step, however, is complicated by the complex nature of the different types of white blood cells and their large variations in shape, texture, color, and density. This study addresses this issue and presents a single fully automatic segmentation framework for both nuclei and cytoplasm of the five classes of leukocytes in a microscope blood smear. The proposed framework integrates a priori information of enhanced nuclei color with Gram-Schmidt orthogonalization, and multi-scale morphological enhancement to localize the nuclei, whereas clustering-based seed extraction and watershed are utilized to segment the cytoplasm. The experimental results on two different datasets show that the proposed method works successfully for both nuclei and cytoplasm segmentation, and achieves more accurate segmentation results compared to six leukocytes segmentation methods in the literature.

A hybrid encryption/hiding method for secure transmission of biometric data in multimodal authentication system

Biometric security is a fast growing area that gains an increasing interest in the last decades. Digital encryption and hiding techniques provide an efficient solution to protect biometric data from accidental or intentional attacks. In this paper, a highly secure encryption/hiding scheme is proposed to ensure secure transmission of biometric data in multimodal biometric identification/authentication system. The secret fingerprint and iris vectors are sparsely approximated using accelerated iterative hard thresholding technique and then embedded in the host Slantlet-SVD domain of face image. Experiments demonstrate the efficiency of our technique for both encryption and hiding purpose, where the secret biometric information is well encrypted and still extractable with high fidelity even though the carrier image is seriously corrupted. Our experimental results show the efficiency of the proposed technique in term of robustness to attacks, Invisibility, and security.

Learning Shape-Driven Segmentation Based on Neural Network and Sparse Reconstruction Toward Automated Cell Analysis of Cervical Smears

The development of an automatic and accurate segmentation approach for both nuclei and cytoplasm remains an open problem due to the complexities of cell structures resulting from inconsistent staining, poor contrast, and the presence of mucus, blood, inflammatory cells, and highly overlapping cells. This paper introduces a computer vision slide analysis technique of two stages: the 3-class cellular component classification, and individual cytoplasm segmentation. Feed forward neural network along with discriminative shape and texture features is applied to classify the cervical cell images in the cellular components. Then, a learned shape prior incorporated with variational framework is applied for accurate localization and delineation of overlapping cells. The shape prior is dynamically modelled during the segmentation process as a weighted linear combination of shape templates from an over-complete shape repository. The proposed approach is evaluated and compared to the state-of-the-art methods on a dataset of synthetically generated overlapping cervical cell images, with competitive results in both nuclear and cytoplasmic segmentation accuracy.

Optimizing the cervix cytological examination based on deep learning and dynamic shape modeling

The task of segmenting nuclei and cytoplasm in Papanicolau smear images is one of the most challenging tasks in automated cervix cytological analysis owing to the high degree of overlapping, the multiform shape of the cells and their complex structures resulting from inconsistent staining, poor contrast, and the presence of inflammatory cells. This article presents a robust variational segmentation framework based on superpixelwise convolutional neutral network and a learned shape prior enabling an accurate analysis of overlapping cervical mass. The cellular components of Pap image are first classified by automatic feature learning and classification model. Then, a learning shape prior model is employed to delineate the actual contour of each individual cytoplasm inside the overlapping mass. The shape prior is dynamically modeled during the segmentation process as a weighted linear combination of shape templates from an over-complete shape dictionary under sparsity constraints. We provide quantitative and qualitative assessment of the proposed method using two databases of 153 cervical cytology images, with 870 cells in total, synthesised by accumulating real isolated cervical cells to generate overlapping cellular masses with a varying number of cells and degree of overlap. The experimental results have demonstrated that our methodology can successfully segment nuclei and cytoplasm from highly overlapping mass. Our segmentation is also competitive when compared to the state-of-the-art methods.