X-ray microtomography reveals a lattice-like network within aortic elastic lamellae.

The arterial wall consists of three concentric layers: intima, media, and adventitia. Beyond their resident cells, these layers are characterized by an extracellular matrix (ECM), which provides both biochemical and mechanical support. Elastin, the major component of arterial ECM, is present in the medial layer and organized in concentric elastic lamellae that confer resilience to the wall. We explored the arterial wall structures from C57Bl6 (control), db/db (diabetic), and ApoE-/- (atherogenic) mice aged 3 months using synchrotron X-ray computed microtomography on fixed and unstained tissues with a large image field (8 mm3 ). This approach combined a good resolution (0.83 µm/voxel), large 3D imaging field. and an excellent signal to noise ratio conferred by phase-contrast imaging. We determined from 2D virtual slices that the thickness of intramural ECM structures was comparable between strains but automated image analysis of the 3D arterial volumes revealed a lattice-like network within concentric elastic lamellae. We hypothesize that this network could play a role in arterial mechanics. This work demonstrates that phase-contrast synchrotron X-ray computed microtomography is a powerful technique which to characterize unstained soft tissues.

Statistical model of quantized DCT coefficients: application in the steganalysis of Jsteg algorithm.

The goal of this paper is to propose a statistical model of quantized discrete cosine transform (DCT) coefficients. It relies on a mathematical framework of studying the image processing pipeline of a typical digital camera instead of fitting empirical data with a variety of popular models proposed in this paper. To highlight the accuracy of the proposed model, this paper exploits it for the detection of hidden information in JPEG images. By formulating the hidden data detection as a hypothesis testing, this paper studies the most powerful likelihood ratio test for the steganalysis of Jsteg algorithm and establishes theoretically its statistical performance. Based on the proposed model of DCT coefficients, a maximum likelihood estimator for embedding rate is also designed. Numerical results on simulated and real images emphasize the accuracy of the proposed model and the performance of the proposed test.

Camera model identification based on the heteroscedastic noise model.

The goal of this paper is to design a statistical test for the camera model identification problem. The approach is based on the heteroscedastic noise model, which more accurately describes a natural raw image. This model is characterized by only two parameters, which are considered as unique fingerprint to identify camera models. The camera model identification problem is cast in the framework of hypothesis testing theory. In an ideal context where all model parameters are perfectly known, the likelihood ratio test (LRT) is presented and its performances are theoretically established. For a practical use, two generalized LRTs are designed to deal with unknown model parameters so that they can meet a prescribed false alarm probability while ensuring a high detection performance. Numerical results on simulated images and real natural raw images highlight the relevance of the proposed approach.