Nuclei segmentation is an important step for quantitative analysis of fluorescence microscopy images. A large volume generally has many different regions containing nuclei with varying spatial characteristics. Automatically identifying nuclei that are challenging to segment can speed up the analysis of biological tissues. Here we show a segmentation technique that provides a metric of segmentation “confidence” for each segmented object in an image volume. This confidence metric can be used either to generate a “confidence map” for visual distinction of reliable from unreliable regions, or in the data space to identify questionable measurements that can be analyzed separately or eliminated from analysis. In an analysis of nuclei in a 3-dimensional image volume, we show that the confidence map correlates well with visual evaluations of segmentation quality, and that the confidence metric correlates well with F1 scores within subregions of the image volume. In addition, we also describe three visualization methods that can visualize the segmentation differences between a segmented volume and a reference volume.

Matrix low rank approximation is an effective method to reduce or eliminate the statistical redundancy of its components. Compared with the traditional global low rank methods such as singular value decomposition (SVD), local low rank approximation methods are more advantageous to uncover interpretable data structures when clear duality exists between the rows and columns of the matrix. Local low rank approximation is equivalent to low rank submatrix detection. Unfortunately, existing local low rank approximation methods can detect only submatrices of specific mean structure, which may miss a substantial amount of true and interesting patterns. In this work, we develop a novel matrix computational framework called RPSP (Random Probing based submatrix Propagation) that provides an effective solution for the general matrix local low rank representation problem. RPSP detects local low rank patterns that grow from small submatrices of low rank property, which are determined by a random projection approach. RPSP is supported by theories of random projection. Experiments on synthetic data demonstrate that RPSP outperforms all state-of-the-art methods, with the capacity to robustly and correctly identify the low rank matrices when the pattern has a similar mean as the background, background noise is heteroscedastic and multiple patterns present in the data. On real-world datasets, RPSP also demonstrates its effectiveness in identifying interpretable local low rank matrices.

Automated microscope systems are increasingly used to collect large-scale 3D image volumes of biological tissues. Since cell boundaries are seldom delineated in these images, detection of nuclei is a critical step for identifying and analyzing individual cells. Due to the large intra-class variability in nuclei morphology and the difficulty of generating ground truth annotations, accurate nuclei detection remains a challenging task. We propose a 3D nuclei centroid detection method by estimating the "vector flow" volume where each voxel represents a 3D vector pointing to its nearest nuclei centroid in the corresponding microscopy volume. We then use a voting mechanism to estimate the 3D nuclei centroids from the "vector flow" volume. Our system is trained on synthetic microscopy volumes and tested on real microscopy volumes. The evaluation results indicate our method outperforms other methods both visually and quantitatively.

A large number of genetic variations have been identified to be associated with Alzheimer's disease (AD) and related quantitative traits. However, majority of existing studies focused on single types of omics data, lacking the power of generating a community including multi-omic markers and their functional connections. Because of this, the immense value of multi-omics data on AD has attracted much attention. Leveraging genomic, transcriptomic and proteomic data, and their backbone network through functional relations, we proposed a modularity-constrained logistic regression model to mine the association between disease status and a group of functionally connected multi-omic features, i.e. single-nucleotide polymorphisms (SNPs), genes and proteins. This new model was applied to the real data collected from the frontal cortex tissue in the Religious Orders Study and Memory and Aging Project cohort. Compared with other state-of-art methods, it provided overall the best prediction performance during cross-validation. This new method helped identify a group of densely connected SNPs, genes and proteins predictive of AD status. These SNPs are mostly expression quantitative trait loci in the frontal region. Brain-wide gene expression profile of these genes and proteins were highly correlated with the brain activation map of 'vision', a brain function partly controlled by frontal cortex. These genes and proteins were also found to be associated with the amyloid deposition, cortical volume and average thickness of frontal regions. Taken together, these results suggested a potential pathway underlying the development of AD from SNPs to gene expression, protein expression and ultimately brain functional and structural changes.

Three dimensional image volumes collected using optical microscopy exhibit many characteristics that cause difficulty in segmentation. These include decreasing image contrast with increasing tissue depth, poor edge detail with regard to cellular structures, and limited spatial resolution. This paper describes a three dimensional segmentation method utilizing active surfaces to segment microscopy image volumes. We demonstrate this method on a three dimensional sequence of images acquired from stationary/stabilized kidney tissue of a rat. Results from this method are compared against a prior pseudo-three dimensional segmentation method that analyzes slices on a single image-by-image basis, as well as against another native three dimensional segmentation method.

Inter-integrated circuit mode (I2C) software is used to communicate between sensors and on embedded control system utilising PIC182585 MPLAB hardware. These sensors were built as part of a system on-board that includes sensors, a Q2 microcontroller and interface circuitry. The hardware includes a PIC18 processor, a field programmable gate array (FPGA) chip and peripherals. The FPGA chip is used to interface the processor with the peripherals operating at the same clock speed. This hardware design features high-level integration, reliability, high precision and high-speed communications. The system was demonstrated using temperature and CO2 sensors. The overall system was implemented and successfully tested. The response of the multi-sensor system was agreeable with the design parameters. The system is expandable to include other sensors such as light and pressure sensors. Monitoring threshold values should add to the security features of the integrated communication system. The use of a signature detection algorithm to determine threatening situation and/or intrusion within the sittings are discussed as a potential application for home land security. The integrated system features low-power consumption (utilising the sleeping mode of the processors), high-speed communications, security and flexibility to expansion.

Optical microscopy exhibits many challenges for digital image analysis. In general, microscopy volumes are inherently anisotropic, suffer from decreasing contrast with tissue depth, and characteristically have low signal levels. This paper describes a method to segment multiphoton fluorescent microscopy images via a combination of segmentation and registration methods. In particular, the proposed method utilizes image enhancement and spatial filtering along with registration and temporal filtering. Experimental results demonstrate the efficacy of the proposed method.

In prior work, we proposed an unequal error protection algorithm based on Wyner-Ziv coding for error resilient video transmission. In subsequentwork it was demonstrated that using either of content adaptive unequal error protection or feedback aided unequal error protection individually improved error resilience performance. In the current paper we propose to combine the use of a content adaptive function, implemented at the encoder, with the channel loss feedback provided by the decoder. The experimental results demonstrate improved rate distortion performance.

Compressed video is very sensitive to channel errors. A few bit losses can derail the entire decoding process. Thus, protecting compressed video is imperative to enable visual communications. Since different elements in a compressed video stream vary in their impact on the quality of the decoded video, unequal error protection can be used to provide efficient protection. This paper describes an unequal error protection method for protecting data elements in a video stream, via a Wyner--Ziv encoder that consists of a coarse quantizer and a Turbo coder based lossless Slepian--wolf encoder. Data elements that significantly impact the visual quality of decoded video, such as modes and motion vectors as used by H.264, are provided more parity bits than coarsely quantized transform coefficients. This results in an improvement in the quality of the decoded video when the transmitted sequence is corrupted by transmission errors, than obtained by the use of equal error protection.

In this paper, we focus on leaky prediction layered video coding (LPLC). LPLC includes a scaled version of the enhancement layer within the motion compensation (MC) loop to improve the coding efficiency while maintaining graceful recovery in the presence of error drift. However, there exists a deficiency inherent in the LPLC structure, namely that the reconstructed video quality from both the enhancement layer and the base layer cannot be guaranteed to be always superior to that of using the base layer alone, even when no drift occurs. In this paper, we: 1) highlight this deficiency using a formulation that describes LPL; 2) propose a general framework that applies to both LPLC and a multiple description coding scheme using MC and we use this framework to further confirm the existence of the deficiency in LPLC; and 3) furthermore, we propose an enhanced LPLC based on maximum-likelihood estimation to address the previously specified deficiency in LPLC. We then show how our new method performs compared to LPLC.

In asynchronus transfer mode networks, cell loss or channel errors can cause data to be dropped in the channel. When digital images/videos are transmitted over these networks, one must be able to reconstruct the missing data so that the impact of the errors is minimized. In this paper, we present an error-concealment technique for shape in MPEG-4 object-based video coding. This method, which is based on using global motion estimation and compensation techniques for boundary recovery, consists of three steps: (1) boundary extraction from shape; (2) boundary patching using global motion compensation; and (3) boundary filling to reconstruct the shape of the damaged video object planes. Global motion parameters are inserted as part of the USER_DATA field in the compressed stream and are utilized in reconstructing the damaged boundaries of compressed video object planes.

Unlike conventional layered scalable video coding, leaky prediction layered video coding (LPLC) introduces a leaky factor /spl alpha/, which takes on values in the range between 0 and 1, to partially include the enhancement layer in the motion compensation loop, hence obtaining a trade-off between coding efficiency and error resilience performance. In this paper, we use quantization noise modeling to theoretically analyze the rate distortion performance of LPLC. An alternative block diagram of LPLC is first developed, which significantly simplifies the theoretical analysis. Closed form expressions, as a function of the leaky factor, are derived for two scenarios, where drift error occurs in the enhancement layer and no drift occurs within the motion compensation loop. Theoretical results are evaluated with respect to the leaky factor, showing that a leaky factor of 0.4-0.6 is a good choice in terms of the overall rate distortion performance of LPLC.

Leaky prediction layered video coding (LPLC) partially includes the enhancement layer in the motion compensated prediction loop, by using a leaky factor between 0 and 1, to balance the coding efficiency and error resilience performance. In this paper, rate distortion functions are derived for LPLC from rate distortion theory. Closed form expressions are obtained for two scenarios of LPLC, one where the enhancement layer stays intact and the other where the enhancement layer suffers from data rate truncation. The rate distortion performance of LPLC is then evaluated with respect to different choices of the leaky factor, demonstrating that the theoretical analysis well conforms with the operational results.

In this paper, we focus on the leaky prediction based scalable coding (LPSC) structure and present a general framework for LPSC. We demonstrate the similarity between LPSC and motion compensation based multiple description coding scheme. We show that since the information contained in the enhancement layer in LPSC is actually a mismatch between two descriptions for each frame, it cannot be guaranteed that the enhancement layer always achieves superior reconstruction quality beyond that achieved by the base layer. We derive three reconstructions for each frame under the LPSC framework, and propose a maximum-likelihood (ML) estimation scheme for LPSC video reconstruction at the decoder. This generally achieves superior decoded video quality than both the enhancement layer and the base layer.

In ATM networks cell loss or channel errors can cause data to be dropped in the channel. When digital images/video are transmitted over these networks one must be able to reconstruct the missing data so that the impact of the errors is minimized. We present an error concealment technique for object-coded sequences using the MPEG-4 video coding standard. This method, which is based on using global motion estimation for boundary recovery, consists of boundary extraction, boundary patching using global motion compensation, and object filling to reconstruct the damaged video object.

We propose new schemes to introduce error resilience into the compressed video bitstreams for transmission over packet networks. First, we develop an adaptive packetization scheme that prohibits any dependency across packets, for error resilience purposes, while exploiting the dependency within each packet to improve the source coding performance. Secondly, we address a two-layer rate-distortion optimization scheme to serve our packetization method. We also use forward error correction (FEC) coding across packets to provide further error protection. Finally, we present a simplified version of our schemes to make it fully compliant with the current ITU video coding standard - H.263+.

In ATM networks cell loss or channel errors can cause data to be dropped in the channel. When digital images/video are transmitted over these networks one must be able to reconstruct the missing data so that the impact of the errors is minimized. We overview the problem of using EZW encoders in channels where data-loss is possible. We also describe an error resilience scheme based on unequal error protection and data interleaving that addresses the problem of using rate scalable encoders over ATM networks.

We develop a framework for efficiently encoding predictive error frames (PEF) as part of a rate scalable, wavelet-based video compression algorithm. We investigate the use of rate-distortion analysis to determine the significance of coefficients in the wavelet decomposition. Based on this analysis, we allocate the bit budget assigned to a PEF to the coefficients that yield the largest reduction in distortion, while maintaining the embedded and rate scalable properties of our video compression algorithm.

When transmitting compressed video over a data network, one has to deal with how channel errors affect the decoding process. This is particularly a problem with data loss or erasures. In this paper we describe techniques to address this problem in the context of asynchronous transfer mode (ATM) networks. Our techniques can be extended to other types of data networks such as wireless networks. In ATM networks channel errors or congestion cause data to be dropped, which results in the loss of entire macroblocks when MPEG video is transmitted. In order to reconstruct the missing data, the location of these macroblocks must be known. We describe a technique for packing ATM cells with compressed data, whereby the location of missing macroblocks in the encoded video stream can be found. This technique also permits the proper decoding of correctly received macroblocks, and thus prevents the loss of ATM cells from affecting the decoding process. The packing strategy can also be used for wireless or other types of data networks. We also describe spatial and temporal techniques for the recovery of lost macroblocks. In particular, we develop several optimal estimation techniques for the reconstruction of missing macroblocks that contain both spatial and temporal information using a Markov random field model. We further describe a sub-optimal estimation technique that can be implemented in real time.

Rate scalable video compression is appealing for low bit rate applications, such as video telephony and wireless communication, where bandwidth available to an application cannot be guaranteed. In this paper, we investigate a set of strategies to increase the performance of SAMCoW, a rate scalable encoder. These techniques are based on based on wavelet decomposition, spatial orientation trees, and motion compensation.

In ATM networks cell loss or channel errors can cause data to be dropped in the channel. When digital video is transmitted over these networks one must be able to reconstruct the missing data so that the impact of these errors is minimized. In this paper we describe a Bayesian approach to concealing these errors by post-processing the received data. In a previous paper (see IEEE Proc. Int. Conf. on Image Processing p.49-52, 1996), each frame in the sequence was modeled as a Markov random field, and maximum a posteriori estimates of the missing macroblocks were obtained. However, the maximum a posteriori estimate is not unique, and the algorithm is also computationally intensive. In this paper we demonstrate, that by using median filtering we arrive at a suboptimal estimate. This will allow real-time nearly optimal reconstruction of the missing data.

In ATM networks cell loss causes data to be dropped in the channel. When digital video is transmitted over these networks one must be able to reconstruct the missing data so that the impact of these errors is minimized. In this paper we describe a Bayesian approach to conceal these errors. Assuming that the digital video has been encoded using the MPEG1 or MPEG2 compression scheme, each frame is modeled as a Markov random field. A maximum a posteriori estimate of the missing macroblocks and motion vectors is described based on the model.

In this paper we describe two error-recovery approaches for MPEG encoded video over ATM networks. The first approach aims at reconstructing each lost pixel by spatial interpolation from the nearest undamaged pixels. The second approach recovers lost macroblocks by minimizing intersample variations within each block and across its boundaries. Moreover, a new technique for packing ATM cells with compressed data is also proposed.