A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images

Cataloged from PDF version of article.Computer-based imaging systems are becoming important tools for quantitative assessment of peripheral blood and bone marrow samples to help experts diagnose blood disorders such as acute leukemia. These systems generally initiate a segmentation stage where white blood cells are separated from the background and other nonsalient objects. As the success of such imaging systems mainly depends on the accuracy of this stage, studies attach great importance for developing accurate segmentation algorithms. Although previous studies give promising results for segmentation of sparsely distributed normal white blood cells, only a few of them focus on segmenting touching and overlapping cell clusters, which is usually the case when leukemic cells are present. In this article, we present a new algorithm for segmentation of both normal and leukemic cells in peripheral blood and bone marrow images. In this algorithm, we propose to model color and shape characteristics of white blood cells by defining two transformations and introduce an efficient use of these transformations in a marker-controlled watershed algorithm. Particularly, these domain specific characteristics are used to identify markers and define the marking function of the watershed algorithm as well as to eliminate false white blood cells in a postprocessing step. Working on 650 white blood cells in peripheral blood and bone marrow images, our experiments reveal that the proposed algorithm improves the segmentation performance compared with its counterparts, leading to high accuracies for both sparsely distributed normal white blood cells and dense leukemic cell clusters. (C) 2014 International Society for Advancement of Cytometr

Neural Distributed Image Compression Using Common Information

We present a novel deep neural network (DNN) architecture for compressing an image when a correlated image is available as side information only at the decoder, a special case of the well-known distributed source coding (DSC) problem in information theory. In particular, we consider a pair of stereo images, which generally have high correlation with each other due to overlapping fields of view, and assume that one image of the pair is to be compressed and transmitted, while the other image is available only at the decoder. In the proposed architecture, the encoder maps the input image to a latent space, quantizes the latent representation, and compresses it using entropy coding. The decoder is trained to extract the common information between the input image and the correlated image, using only the latter. The received latent representation and the locally generated common information are passed through a decoder network to obtain an enhanced reconstruction of the input image. The common information provides a succinct representation of the relevant information at the receiver. We train and demonstrate the effectiveness of the proposed approach on the KITTI and Cityscape datasets of stereo image pairs. Our results show that the proposed architecture is capable of exploiting the decoder-only side information, and outperforms previous work on stereo image compression with decoder side information

Accuracy-awareness: A pessimistic approach to optimal control of triggered mobile communication networks

We use nonlinear model predictive control to procure a joint control of mobility and transmission to minimize total network communication energy use. The nonlinear optimization problem is solved numerically in a self-triggered framework, where the next control update time depends on the predicted state trajectory and the accuracy of the numerical solution. Solution accuracy must be accounted for in any circumstance where systems are run in open-loop for long stretches of time based on potentially inaccurate predictions. These triggering conditions allow us to place wireless nodes in low energy ‘idle' states for extended periods, saving over 70% of energy compared to a periodic policy where nodes consistently use energy to receive control updates

Additive Manufacturing and Testing of High Metal Content High Performance Ramjet Grains

NPS NRP Executive SummaryFuels with high efficiency and energy densities are needed to maximize the range and speed of future air-breathing systems such as solid fuel ramjets (SFRJ). The performance of the fuel mixtures that include large amount of metal additives suffer due to the poor combustion efficiency of the metal powders as they often do not burn completely during the short residence time in the combustor. Recent research has improved the reactivity of these fuels, but introducing them into a binder at high loading densities is a challenge due to the poor rheology. In order to develop and maximize the energy density and performance of SFJR fuel grains, advancements in additive manufacturing (AM) systems will be leveraged. This study will utilize vibration-assisted printing (VAP) and liquid metal printing (LMP) with the Xerox ElemX system to print fuel grains with metal powders and aluminum alloys, and use spray dried nanocomposite mesoparticles as additives. The research will test the physical limits of these approaches and determine optimal printing parameters for producing high quality printed fuels. The fuels will be evaluated mechanically and optimized using fly out calculations and they will be characterized with small scale combustion studies.Naval Air Warfare Center Weapons Division (NAWCWD)ASN(RDA) - Research, Development, and AcquisitionThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Additive Manufacturing and Testing of High Metal Content High Performance Ramjet Grains

NPS NRP Project PosterFuels with high efficiency and energy densities are needed to maximize the range and speed of future air-breathing systems such as solid fuel ramjets (SFRJ). The performance of the fuel mixtures that include large amount of metal additives suffer due to the poor combustion efficiency of the metal powders as they often do not burn completely during the short residence time in the combustor. Recent research has improved the reactivity of these fuels, but introducing them into a binder at high loading densities is a challenge due to the poor rheology. In order to develop and maximize the energy density and performance of SFJR fuel grains, advancements in additive manufacturing (AM) systems will be leveraged. This study will utilize vibration-assisted printing (VAP) and liquid metal printing (LMP) with the Xerox ElemX system to print fuel grains with metal powders and aluminum alloys, and use spray dried nanocomposite mesoparticles as additives. The research will test the physical limits of these approaches and determine optimal printing parameters for producing high quality printed fuels. The fuels will be evaluated mechanically and optimized using fly out calculations and they will be characterized with small scale combustion studies.Naval Air Warfare Center Weapons Division (NAWCWD)ASN(RDA) - Research, Development, and AcquisitionThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Neural distributed image compression with cross-attention feature alignment

We consider the problem of compressing an information source when a correlated one is available as side information only at the decoder side, which is a special case of the distributed source coding problem in information theory. In particular, we consider a pair of stereo images, which have overlapping fields of view, and are captured by a synchronized and calibrated pair of cameras as correlated image sources. In previously proposed methods, the encoder transforms the input image to a latent representation using a deep neural network, and compresses the quantized latent representation losslessly using entropy coding. The decoder decodes the entropy-coded quantized latent representation, and reconstructs the input image using this representation and the available side information. In the proposed method, the decoder employs a cross-attention module to align the feature maps obtained from the received latent representation of the input image and a latent representation of the side information. We argue that aligning the correlated patches in the feature maps allows better utilization of the side information. We empirically demonstrate the competitiveness of the proposed algorithm on KITTI and Cityscape datasets of stereo image pairs. Our experimental results show that the proposed architecture is able to exploit the decoder-only side information in a more efficient manner compared to previous works