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In this paper, we propose a sparse and low-rank decomposition of annihilating filter-based Hankel matrix for MRI artifact removal. Based on the observation that some MR artifacts are originated from k-space outliers, we employ the recently proposed image modeling method using annihilating filter-based low-rank Hankel matrix approach (ALOHA) to decompose the sparse outliers from the low-rank component...
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We investigate glandular structure segmentation in colon histology images as a window-based classification problem. We compare and combine methods based on fine-tuned convolutional neural networks (CNN) and hand-crafted features with support vector machines (HC-SVM). On 85 images of H&E-stained tissue, we find that fine-tuned CNN outperforms HC-SVM in gland segmentation measured by pixel-wise...
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Segmentation of nuclei on breast cancer histopathological images is considered a basic and essential step for diagnosis in a computer-aided diagnosis framework. Nuclear segmentation remains a challenging problem due to the inherent diversity of cancer biology and the variability of the tissue appearance. We present an automatic nuclear segmentation method using an improved hybrid active contour (AC)...
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We propose an exhaustive extension to graph cut-based coronary artery reconstruction from multiple views of a rotational angiography sequence. The reconstruction is formulated as an energy minimization problem that is solved using the a-expansion algorithm. We enforce reprojection-based data consistency and completeness conditions on the reconstructed centerline. The proposed strategy omits the need...
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Being able to efficiently segment a developing embryo from background clutter constitutes an important step in automated monitoring of human embryonic cells. State-of-the-art automatic segmentation methods remain ill-suited to handle the complex behavior and morphological variance of non-stained embryos. By contrast, while effective, manual approaches are impractically time-consuming. In this paper,...
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Time-lapse microscopy imaging has advanced rapidly in last few decades and is producing large volume of data in cell and developmental biology. This has increased the importance of automated analyses, which depend heavily on cell segmentation and tracking as these are the initial stages when computing most biologically important cell properties. In this paper, we propose a novel joint cell segmentation...
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A sparse coding method is proposed for the representation and segmentation of multi-subject white matter fiber tracts. Instead of representing bundles as a single centroid, this method learns a compact dictionary of training fibers, capable of describing the whole dataset, and encodes bundles as a sparse combination of dictionary prototypes. This provides an efficient and accurate way to segment new...
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Computer-based analysis is highly effective in information retrieval from Dynamic Contrast Enhance Magnetic Resonance Images (DCE-MRI) for prostate cancer recognition. Quantification and modelling of perfusion curves to extract higher order informative features for use in classification algorithms, is a major step in DCE-MRI analysis where inter-scan independent semi-quantitative models are highly...
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Breast tissue segmentation is a fundamental task in digital mammography. Commonly, this segmentation is applied prior to breast density estimation. However, observations show a strong correlation between the segmentation parameters and the breast density, resulting in a chicken and egg problem. This paper presents a new method for breast segmentation, based on training with weakly labeled data, namely...
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Breast ultrasound image segmentation is challenging task due to the low quality of ultrasound images and the complex breast structure. An accurate and automatic algorithm is presented to segment breast ultrasound images by combining image boundary and region information. The algorithm decomposes the image into a set of superpixels using the Normalized Cuts method along with texture analysis. An SVM...
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In this paper we address the problem of differentiating between malignant and benign tumors based on their appearance in the CC and MLO mammography views. Classification of clustered breast microcalcifications into benign and malignant categories is an extremely challenging task for computerized algorithms and expert radiologists alike. We describe a deep-learning classification method that is based...
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Dictionary based regularization has shown its potential to improve the low-dose computed tomography (CT) imaging quality. In this paper, we developed a 3D dictionary learning (3D-DL) regularization approach for low-dose cone-beam computed tomography (CBCT) reconstruction. A 3D dictionary learned from standard-dose CT volumes database is used to build the prior term, and an alternating minimization...
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Microwave Induced Thermo-Acoustic Tomography (MITAT) is a new biological imaging technology, which combines the advantage of high contrast and high resolution. MITAT can image tumor tissues and is helpful for the diagnosis of breast cancer, etc. In this paper, we propose a new MITAT system design which uses Stepped-Frequency Continuous-Wave (S-FCW) waveforms to reduce the irradiating microwave peak...
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Here we present a novel iterative approach for tomographic image reconstruction which improves image quality for undersampled and limited view projection measurements. Recently, the Total Generalized Variation (TGV) penalty has been proposed to establish a desirable balance between smooth and piecewise-constant solutions. Piecewise-smooth reconstructions are particularly important for biomedical applications,...
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X-ray Tensor Tomography (XTT) is a recently developed imaging modality that provides reconstruction of X-ray scattering tensors from dark-field projections obtained in a grating interferometry setup. In this work we present a novel component-based total variation (TV) regularized reconstruction technique for XTT data. First results show promising qualitative improvements of the reconstructed tensors...
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In functional MRI (fMRI), the noise is often considered to have a fixed model of temporal autocorrelation. However, the correlation structure of brain voxels is generally unknown and varies due to the regional effects of neurogenic noises and pulsatile effects. In this paper, we apply mixed spectrum analysis to separate the discrete (task-related) spectrum and continuous (noise-related) spectrum of...
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Respiratory motion limits the use of focused ultrasound surgery and conformal radiation therapies for livers. Population motion models have shown their ability to predict unobservable motion from tracking results. These models require establishment of inter-subject correspondences, which is non-trivial. In this study we compare the use of a landmark-based method with a shape-based approach for this...
