Visual Quality Enhancement in Optoacoustic Tomography using Active Contour Segmentation Priors

Segmentation of biomedical images is essential for studying and characterizing anatomical structures, detection and evaluation of pathological tissues. Segmentation has been further shown to enhance the reconstruction performance in many tomographic imaging modalities by accounting for heterogeneities of the excitation field and tissue properties in the imaged region. This is particularly relevant in optoacoustic tomography, where discontinuities in the optical and acoustic tissue properties, if not properly accounted for, may result in deterioration of the imaging performance. Efficient segmentation of optoacoustic images is often hampered by the relatively low intrinsic contrast of large anatomical structures, which is further impaired by the limited angular coverage of some commonly employed tomographic imaging configurations. Herein, we analyze the performance of active contour models for boundary segmentation in cross-sectional optoacoustic tomography. The segmented mask is employed to construct a two compartment model for the acoustic and optical parameters of the imaged tissues, which is subsequently used to improve accuracy of the image reconstruction routines. The performance of the suggested segmentation and modeling approach are showcased in tissue-mimicking phantoms and small animal imaging experiments.Comment: Accepted for publication in IEEE Transactions on Medical Imagin

Deep Learning based Skin-layer Segmentation for Characterizing Cutaneous Wounds from Optical Coherence Tomography Images

Optical coherence tomography (OCT) is a medical imaging modality that allows us to probe deeper substructures of skin. The state-of-the-art wound care prediction and monitoring methods are based on visual evaluation and focus on surface information. However, research studies have shown that sub-surface information of the wound is critical for understanding the wound healing progression. This work demonstrated the use of OCT as an effective imaging tool for objective and non-invasive assessments of wound severity, the potential for healing, and healing progress by measuring the optical characteristics of skin components. We have demonstrated the efficacy of OCT in studying wound healing progress in vivo small animal models. Automated analysis of OCT datasets poses multiple challenges, such as limitations in the training dataset size, variation in data distribution induced by uncertainties in sample quality and experiment conditions. We have employed a U-Net-based model for the segmentation of skin layers based on OCT images and to study epithelial and regenerated tissue thickness wound closure dynamics and thus quantify the progression of wound healing. In the experimental evaluation of the OCT skin image datasets, we achieved the objective of skin layer segmentation with an average intersection over union (IOU) of 0.9234. The results have been corroborated using gold-standard histology images and co-validated using inputs from pathologists. Clinical Relevance: To monitor wound healing progression without disrupting the healing procedure by superficial, noninvasive means via the identification of pixel characteristics of individual layers.Comment: Accepte

Further Cryptographic Properties of the Multiplicative Inverse Function

Differential analysis is an important cryptanalytic technique on block ciphers. In one form, this measures the probability of occurrence of the differences between certain inputs vectors and the corresponding outputs vectors. For this analysis, the constituent S-boxes of Block cipher need to be studied carefully. In this direction, we derive further cryptographic properties of inverse function, especially higher-order differential properties here. This improves certain results of Boukerrou et al [ToSC 2020(1)]. We prove that inverse function defined over $\mathbb F_{2^n}$ has an error (bias) in its second-oder differential spectrum with probability $\frac{1}{2^{n-2}}$, and that error occurs in more than one places. To the best of our knowledge, this result was not known earlier. Further, for the first time, we analyze the Gowers uniformity norm of S-boxes which is also a measure of resistance to higher order approximations. Finally, the bounds related to the nonlinearity profile of multiplicative inverse function are derived using both Gowers $U_3$ norm and Walsh--Hadamard spectrum. Some of our findings provide slightly improved bounds over the work of Carlet [IEEE-IT, 2008]. All our results might have implications towards non-randomness of a block cipher where the inverse function is used as a primitive

The connection between quadratic bent-negabent functions and the Kerdock code

In this paper we prove that all bent functions in the Kerdock code, except for the coset of the symmetric quadratic bent function, are bent–negabent. In this direction, we characterize the set of quadratic bent–negabent functions and show some results connecting quadratic bent–negabent functions and the Kerdock code. Further, we note that there are bent–negabent preserving nonsingular transformations outside the well known class of orthogonal ones that might provide additional functions in the bent– negabent set. This is the first time we could identify non-orthogonal (nonsingular) linear transformations that preserve bent–negabent property for a special subset

Quantum noise limited microwave amplification using a graphene Josephson junction

Josephson junctions (JJ) and their tunable properties, including their nonlinearities, form the core of superconducting circuit quantum electrodynamics (cQED). In quantum circuits, low-noise amplification of feeble microwave signals is essential and the Josephson parametric amplifiers (JPA) are the widely used devices. The existing JPAs are based on Al-AlOx-Al tunnel junctions realized in a superconducting quantum interference device geometry, where magnetic flux is the knob for tuning the frequency. Recent experimental realizations of 2D van der Waals JJs provide an opportunity to implement various cQED devices with the added advantage of tuning the junction properties and the operating point using a gate potential. While other components of a possible 2D van der Waals cQED architecture have been demonstrated -- quantum noise limited amplifier, an essential component, has not been realized. Here we implement a quantum noise limited JPA, using a graphene JJ, that has linear resonance gate tunability of 3.5 GHz. We report 24 dB amplification with 10 MHz bandwidth and -130 dBm saturation power; performance on par with the best single-junction JPAs. Importantly, our gate tunable JPA works in the quantum-limited noise regime which makes it an attractive option for highly sensitive signal processing. Our work has implications for novel bolometers -- the low heat capacity of graphene together with JJ nonlinearity can result in an extremely sensitive microwave bolometer embedded inside a quantum noise-limited amplifier. In general, our work will open up exploration of scalable device architecture of 2D van der Waals materials by integrating a sensor with the quantum amplifier.Comment: 15 pages, 4 figures, and supplementary informatio