Medical and Computational Imaging

For the Cure



Research Highlights of
Information Processing & Algorithms Laboratory

ABOUT

The Information Processing and Algorithms Laboratory (iPAL) is directed by Prof. Vishal Monga. Graduate research in iPAL broadly encompasses signal and image processing theory and applications with a particular focus on capturing practical real-world constraints via convex optimization theory and algorithms.

RECENT PROJECTS

Simultaneous Denoising and Localization Network for Photoacoustic Target Localization

We develop a novel deep learning method designed to explicitly exhibit robustness to noise present in photoacoustic radio-frequency (RF) data.We verify the merits of the proposed architecture by outperforming the state of the art on both simulated and experimental datasets.

Histopathological Image Classification using Discriminative Feature-Oriented Dictionary Learning

We propose an automatic feature discovery framework for extracting discriminative class-specific features and present a low-complexity method for classification and disease grading in histopathology.Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific features which are suitable for representing samples from the same class while are poorly capable of representing samples from other classes.

Learning Based Segmentation of CT Brain Images: Application to Post-Operative Hydrocephalic Scans

Most intensity and feature based segmentation methods fail to separate subdurals from brain and CSF as subdural geometry varies greatly across different patients and their intensity varies with time. We combat this problem by a learning approach that treats segmentation as supervised classification at the pixel level, i.e. a training set of CT scans with labeled pixel identities is employed. We also demonstrate that the proposed algorithm is computationally less burdensome and exhibits a graceful degradation against number of training samples, enhancing its deployment potential.

Deep MR Image Super-Resolution Using Structural Priors

Unlike regular optical imagery, for MR image super-resolution generous training is often unavailable. We therefore propose the use of image priors, namely a low-rank structure and a sharpness prior to enhance deep MR image superresolution. Experiments performed on two publicly available MR brain image databases exhibit promising results particularly when training imagery is limited.

Deep Networks With Shape Priors For Nucleus Detection

Nuclei detection has been a topic of enduring interest with promising recent success shown by deep learning methods. These methods train for example convolutional neural networks (CNNs) with a training set of input images and known, labeled nuclei locations. Many of these methods are supplemented by spatial or morphological processing. We develop a new approach that we call Shape Priors with Convolutional Neural Networks (SP-CNN) to perform significantly enhanced nuclei detection.

ALSF - Analysis-Synthesis learning with shared features for histopathological image classification

The diversity of tissue structure in histopathological images makes feature extraction for classification a challenging task. We introduce the learning of a low rank shared dictionary and a shared analysis operator, which more accurately represents both similarities and differences in histopathological images from distinct classes.We also develop an extension of ALSF with a sparsity constraint, whose presence or absence facilitates a cost-performance trade-off.

Deep Retinal Image Segmentation Under Geometrical Priors

Vessel segmentation of retinal images is a key diagnostic capability in ophthalmology. This problem faces several challenges including low contrast, variable vessel size and thickness, and presence of interfering pathology such as micro-aneurysms and hemorrhages. Early approaches addressing this problem employed hand-crafted filters to capture vessel structures, accompanied by morphological post-processing. More recently, deep learning techniques have been employed with significantly enhanced segmentation accuracy. We propose a novel domain enriched deep network that consists of two components: 1) a representation network that learns geometric features specific to retinal images, and 2) a custom designed computationally efficient residual task network that utilizes the features obtained from the representation layer to perform pixel-level segmentation. The representation and task networks are {\em jointly learned} for any given training set. To obtain physically meaningful and practically effective representation filters, we propose two new constraints that are inspired by expected prior structure on these filters: 1) orientation constraint that promotes geometric diversity of curvilinear features, and 2) a data adaptive noise regularizer that penalizes false positives. Multi-scale extensions are developed to enable accurate detection of thin vessels.

Efficient And Interpretable Deep Blind Image Deblurring Via Algorithm Unrolling

Blind image deblurring remains a topic of enduring interest. Learning based approaches, especially those that employ neural networks have emerged to complement traditional model based methods and in many cases achieve vastly enhanced performance. That said, neural network approaches are generally empirically designed and the underlying structures are difficult to interpret. In recent years, a promising technique called algorithm unrolling has been developed that has helped connect iterative algorithms such as those for sparse coding to neural network architectures. In this paper, we propose a neural network architecture based on this idea to solve the blind image unrolling problem.

Ghost-Free High Dynamic Range Imaging

We propose a ghost-free high dynamic range (HDR) image synthesis algorithm using a low-rank matrix completion framework, which we call RM-HDR. Based on the assumption that irradiance maps are linearly related to low dynamic range (LDR) image exposures, we formulate ghost region detection as a rank minimization problem. We incorporate constraints on moving objects, i.e., sparsity, connectivity, and priors on under and over exposed regions into the framework.

A MAP Estimation Framework for HDR Video Synthesis

High dynamic range (HDR) image synthesis from multiple low dynamic range (LDR) exposures continues to be a topic of great interest. Its extension to HDR video is also a topic of significant research interest due to its increasing demand and economic costs. However, limited work has been done in this area because of its major challenge in accurate correspondence estimation; in particular, loss of data resulting from poor exposures and varying intensity make conventional optical flow methods highly inaccurate.

Bundle Robust Alignment for Panoramic Stitching

Here we study the problem of image alignment for panoramic stitching. Unlike most existing approaches that are feature-based, our algorithm works on pixels directly, and accounts for errors across the whole images globally. Technically, we formulate the alignment problem as rank-1 and sparse matrix decomposition over transformed images, and develop an efficient algorithm for solving this challenging non-convex optimization problem. The algorithm reduces to a sequence of subproblems, where we analytically establish exact recovery conditions, convergence and optimality, together with convergence rate and complexity.

Sparsity-based Color Image Super Resolution via Exploiting Cross Channel Constraints

Sparsity constrained single image super-resolution (SR) has been of much recent interest. A typical approach involves sparsely representing patches in a low-resolution (LR) input image via a dictionary of example LR patches, and then using the coefficients of this representation to generate the highresolution (HR) output via an analogous HR dictionary. However, most existing sparse representation methods for super resolution focus on the luminance channel information and do not capture interactions between color channels. In this work, we extend sparsity based super-resolution to multiple color channels by taking color information into account.

Blind Image Deblurring Using Row-Column Sparse Representations

Blind image deblurring is a particularly challenging inverse problem where the blur kernel is unknown and must be estimated en route to recovering the de-blurred image. The problem is of strong practical relevance since many imaging devices such as cellphone cameras, must rely on deblurring algorithms to yield satisfactory image quality. Despite significant research effort, handling large motions remains an open problem. In this paper, we develop a new method called Blind Image Deblurring Using Row-Column Sparse Representations (BD-RCS) to address this issue. Specifically, we model the outer product of kernel and image coefficients in certain transformation domains as a rank-one matrix, and recover it via a rank minimization problem.

Email
ipal.psu@gmail.com

Address
104 Electrical Engineering East,
University Park, PA 16802, USA

Lab Phone:
814-863-7810
814-867-4564