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In multi-atlas based segmentation propagation, segmentations from multiple atlases are propagated to the target image and combined to produce the segmentation result. Local weighted voting (LWV) method is a classifier fusion method which combines the propagated atlases weighted by local image similarity. We demonstrate that the segmentation accuracy using LWV improves as the number of atlases increases...
In this paper we introduce a new algorithm for reconstruction of low-dose CT images. The approach, called multi-resolution feature fusion (MRFF), combines the textural qualities of conventional filtered-back projection images, with the noise suppression ability of non-quadratic regularized iterative reconstructions, to form a fast image reconstruction with good noise texture properties. Low-dose abdominal...
We describe a novel method to segment the bladder wall in magnetic resonance imaging (MRI) to support the detection of disease, such as endometriosis, and for surgical planning. We segment the inner and outer wall boundary using T2- and T1-weighted MRI images, respectively. A new coupling technique for level sets is formulated and tested on 54 T2- and T1-weighted image pairs. A local phase based dimensionless...
Features extracted from cell networks have become popular tools in histological image analysis. However, existing features do not take sufficient advantage of the cycle structure present within the cell networks. We introduce a new class of network cycle features that take advantage of such structures. We demonstrate the utility of these features for automated prostate cancer scoring using histological...
Large multimodal datasets such as The Cancer Genome Atlas present an opportunity to perform correlative studies of tissue morphology and genomics to explore the morphological phenotypes associated with gene expression and genetic alterations. In this paper we present an investigation of Cancer Genome Atlas data that correlates morphology with recently discovered molecular subtypes of glioblastoma...
Populations of healthy older individuals are often highly heterogeneous, as prevalence of various underlying pathologies increases with age. Finding coherent groups of normal older adults may allow to identify subpopulations that are at risk of developing Alzheimer's disease (AD). In this paper, we propose an approach that utilizes longitudinal magnetic resonance imaging (MRI) data to obtain natural...
This paper presents a feature-selection-based data fusion method to follow up the evolution of brain tumors under therapeutic treatments with multi-spectral MRI data sequences. The fusion of MRI data is proposed to use a feature selection method to choose the most important features to classify tumor tissues and non-tumor tissues. Our system consists of three steps for each MRI examination (one examination...
Prostate segmentation is an essential step in developing any non-invasive Computer-Assisted Diagnostic (CAD) system for the early diagnosis of prostate cancer using Magnetic Resonance Images (MRI). In this paper, a novel framework for 3D segmentation of the prostate region from Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is proposed. The framework is based on a Maximum the Posteriori (MAP)...
The proper segmentation of the vascular system of the retina currently attracts wide interest. As a precious outcome, a successful segmentation may lead to the improvement of automatic screening systems. Namely, the detection of the vessels helps the localization of other anatomical parts and lesions besides the vascular disorders. In this paper, we recommend a novel approach for the segmentation...
In this paper we propose an automated articulated atlas-based approach for bone segmentation in whole-body μSPECT data of mice, obtained by injecting the 99mTc-methylene diphosphonate (99mTc - MDP). This is a difficult task, since SPECT data is usually noisy and low resolution, and the skeleton image is incomplete with several portions missing (e.g.: in limbs and skull). For this purpose the articulated...
This paper examines the effectiveness of geometric feature descriptors, common in computer vision, for false positive reduction and for classification of lung nodules in low dose CT (LDCT) scans. A data-driven lung nodule modeling approach creates templates for common nodule types, using active appearance models (AAM); which are then used to detect candidate nodules based on optimum similarity measured...
In this work, we conducted an imaging study to make a direct, quantitative comparison of image features measured by film and full-field digital mammography (FFDM). We acquired images of cadaver breast specimens containing simulated microcalcifications using both a GE digital mammography system and a screen-film system. To quantify the image features, we calculated and compared a set of 12 texture...
In this paper, we show that domain-optimized text detection in biomedical images is important for boosting text extraction recall via off-the-shelf OCR engines. Methodologically, we contrast OCR performance when processing raw biomedical images, compared to preprocessing those images, and performing OCR on detected image text regions only. To quantify OCR extraction results, we rely on a gold standard...
In this paper, we propose a new segmentation algorithm that combines a graph-based shape model with image cues based on boosted features. The landmark-based shape model encodes prior constraints through the normalized Euclidean distances between pairs of control points, alleviating the need of a large database for the training. Moreover, the graph topology is deduced from the dataset using manifold...
In this paper we compare different approaches to combine color and statistical texture descriptors. Previous studies on this topic were conducted on natural images only. We focus on the particular case of histological datasets where color plays an important role due to the staining process of the biological samples. We also introduce two new variants of the well-known Local Binary Patterns (LBP) operator...
High-throughput microscopy allows fast imaging of large tissue samples, producing an unprecedented amount of sub-cellular information. The size and complexity of these data sets often out-scale current reconstruction algorithms. Overcoming this computational bottleneck requires extensive parallel processing and scalable algorithms. As high-throughput imaging techniques move into main stream research,...
Segmentation is the process of partitioning digital images into meaningful regions. The analysis of biological high content images often requires segmentation as a first step. We propose ilastik as an easy-to-use tool which allows the user without expertise in image processing to perform segmentation and classification in a unified way. ilastik learns from labels provided by the user through a convenient...
Structural and functional brain connectivity has been extensively studied via diffusion tensor imaging (DTI) and functional MRI (fMRI) in recent years. An important aspect that has not been adequately addressed before is the connectivity state change in structurally-connected brain regions. In this paper, we present an intuitive approach that extracts feature vectors describing the functional connectivity...
The descriptive power of low-level image features for describing the high-level semantic concepts is limited for content-based image retrieval (CBIR). To reduce this semantic gap and improve retrieval performance of CBIR, a distance metric learning method is proposed which can learn a linear projection to define a distance metric for maximizing mean average precision (MAP). The smooth approximation...
In machine learning, non-linear dimensionality reduction (NLDR) is commonly used to embed high-dimensional data into a low-dimensional space while preserving local object adjacencies. However, the majority of NLDR methods define object adjacencies using distance metrics that do not account for the quality of the features in the high-dimensional space. In this paper we present Boosted Spectral Embedding...
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