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As image processing and analysis techniques improve, an increasing number of procedures in bio-medical analyses can be automated. This brings many benefits, e.g improved speed and accuracy, leading to more reliable diagnoses and follow-up, ultimately improving patients outcome. Many automated procedures in bio-medical imaging are well established and typically consist of detecting and counting various...
Resolution in medical images is limited by diverse physical, technological and economical considerations. In conventional medical practice, resolution enhancement is usually performed with bicubic or B-spline interpolations, strongly affecting the accuracy of subsequent processing steps such as segmentation or registration. In this paper, we propose a coupled dictionary learning approach for super...
Clinical assessment of bone marrow is limited by an inability to evaluate the marrow space comprehensively and dynamically and there is no current method for automatically assessing hematopoietic activity within the medullary space. Evaluating the hematopoietic space in its entirety could be applicable in blood disorders, malignancies, infections, and medication toxicity. In this paper, we introduce...
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Coronary artery disease is one of the major causes of death throughout the world. An effective method for diagnosing this disease is X-ray angiography. The images are usually of poor quality and low contrast. This is due to non-uniform illumination, appearance of other body organs and artifacts, low SNR values, etc. Accurate segmentation of arteries is a challenging and important task. In this paper...
In this paper, we present an automated algorithm for detection of blood vessels in 2D-thermographic images for breast cancer screening. Vessel extraction from breast thermal images help in the classification of malignancy as cancer causes increased blood flow at warmer temperatures, additional vessel formation and tortuosity of vessels feeding the cancerous growth. The proposed algorithm uses three...
A local feature descriptor for image analysis is a tool of interest for many applications. In this paper we propose an in context feature descriptor. An instance of this descriptor corresponding to a specific feature includes information from all other features in the image; it is a feature in context descriptor. This descriptor is thus unique for a feature in an ensemble of features. Many medical...
Reducing the amount of user driven input for interactive image segmentation enables faster and more precise foreground extraction of objects. A sparse collection of labeled seed points sampled over image regions can be quickly provided by the user using a few mouse clicks. Seed points are used for training an Elastic Body Spline classifier mapping function. We evaluate the efficiency and accuracy...
In recent years, advances in medical imaging have led to the emergence of massive databases, containing images from a diverse range of modalities. This has significantly heightened the need for automated annotation of the images on one side, and fast and memory-efficient content-based image retrieval systems on the other side. Binary descriptors have recently gained more attention as a potential vehicle...
This paper investigates precise pupil center localization in low-resolution images. Being an essential preprocessing step in many applications such as gaze estimation, face alignment as well as human-computer interaction, robust, precise, and efficient methods are necessary. We present a method for accurate eye center localization operating with images from simple off-the-shelf hardware such as webcams...
In photon-limited image reconstruction, the behavior of noise at the detector end is more accurately modeled as a Poisson point process than the common choice of a Gaussian distribution. As such, to recover the original signal more accurately, a penalized negative Poisson log-likelihood function — and not a least-squares function — is minimized. In many applications, including medical imaging, additional...
In this paper we explore the application of anomaly detection techniques to tumor voxels segmentation. The developed algorithms work on 3-points dynamic FDG-PET acquisitions and leverage on the peculiar anaerobic metabolism that cancer cells experience over time. A few different global or local anomaly detectors are discussed, together with an investigation over two different algorithms aiming to...
A vast amount of toxicological data can be obtained from feature analysis of cells treated in vitro. However, this requires microscopic image segmentation of cells. To this end, we propose a new strategy, namely Supervised Normalized Cut Segmentation (SNCS), to segment cells that partially overlap and have a large amount of curved edges. SNCS approach is a machine learning based method, where loosely...
In this paper, a novel label inference method encoded with local and global patch priors is introduced for the segmentation of subcortical structures in brain MR images. Due to the serious overlap of intensity profiles among different tissues in brain MR images, the conventional patch prior estimated with similarity measurement can be adversely impacted and become misleading during the final label...
Biomedical image segmentation is an active field of research where deformable models have proved to be efficient. The geometric representation of such models determines their ability to approximate the shape of interest as well as the speed of convergence of related optimization algorithms. We present a new tensor-product parameterization of surfaces that offers the possibility of local refinement...
Optical coherence tomography (OCT) is a medical imaging technology that allows for non-invasive diagnosis of diseases in the early stage. Because blood flow anomalies provide useful information for many diseases, we develop an automatic blood vessel detection algorithm based on the robust principle component analysis (RPCA) technique. Specifically, we propose a short-time RPCA method that divides...
With the unprecedented mobile technology revolution, a number of ocular biometric based personal recognition schemes have been proposed for mobile use cases. The aim of this competition is to evaluate and compare the performance of mobile ocular biometric recognition schemes in visible light on a large scale database (VISOB Dataset ICIP2016 Challenge Version) using standard evaluation methods. Four...
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