Transportation-based metrics for comparing images have long been applied to analyze images, especially where one can interpret the pixel intensities (or derived quantities) as a distribution of ‘mass’ that can be transported without strict geometric constraints. Here we describe a new transportation-based framework for analyzing sets of images. More specifically, we describe a new transportation-related distance between pairs of images, which we denote as linear optimal transportation (LOT). The LOT can be used directly on pixel intensities, and is based on a linearized version of the Kantorovich-Wasserstein metric (an optimal transportation distance, as is the earth mover’s distance). The new framework is especially well suited for computing all pairwise distances for a large database of images efficiently, and thus it can be used for pattern recognition in sets of images. In addition, the new LOT framework also allows for an isometric linear embedding, greatly facilitating the ability to visualize discriminant information in different classes of images. We demonstrate the application of the framework to several tasks such as discriminating nuclear chromatin patterns in cancer cells, decoding differences in facial expressions, galaxy morphologies, as well as sub cellular protein distributions.

Graphene-and graphene oxide-based nanomaterials have gained broad interests in research because of their unique physiochemical properties. The 2D allotropic structure allows it to be used in various biological fields. The biomedical applications of graphene and its composite include its use in gene and small molecular drug delivery. It is further used for biofunctionalization of protein, in anticancer therapy, as an antimicrobial agent for bone and teeth implantation. The biocompatibility of the newly synthesized nanomaterials allows its substantial use in medicine and biology. The current review summarizes the chemical structure and biological application of graphene in various fields.

Abstract. Rician noise introduces a bias into MRI measurements that can have a significant impact on the shapes and orientations of tensors in diffusion tensor magnetic resonance images. This is less of a problem in structural MRI, because this bias is signal dependent and it does not seriously impair tissue identification or clinical diagnoses. However, diffusion imaging is used extensively for quantitative evaluations, and the tensors used in those evaluations are biased in ways that depend on orientation and signal levels. This paper presents a strategy for filtering diffusion tensor magnetic resonance images that addresses these issues. The method is a maximum a posteriori estimation technique that operates directly on the diffusion weighted images and accounts for the biases introduced by Rician noise. We account for Rician noise through a data likelihood term that is combined with a spatial smoothing prior. The method compares favorably with several other approaches from the literature, including methods that filter diffusion weighted imagery and those that operate directly on the diffusion tensors.

Modern microscopic imaging devices are able to extract more information regarding the subcellular organization of different molecules and proteins than can be obtained by visual inspection. Predetermined numerical features (descriptors) often used to quantify cells extracted from these images have long been shown useful for discriminating cell populations (e.g., normal vs. diseased). Direct visual or biological interpretation of results obtained, however, is often not a trivial task. We describe an approach for detecting and visualizing phenotypic differences between classes of cells based on the theory of optimal mass transport. The method is completely automated, does not require the use of predefined numerical features, and at the same time allows for easily interpretable visualizations of the most significant differences. Using this method, we demonstrate that the distribution pattern of peripheral chromatin in the nuclei of cells extracted from liver and thyroid specimens is associated with malignancy. We also show the method can correctly recover biologically interpretable and statistically significant differences in translocation imaging assays in a completely automated fashion.optimal transport | cell morphometry | high content screening Q uantitative analysis of cell images is extensively used in several health sciences applications (1). Scientists wishing to quantify the effects of certain drugs, genes, and other perturbations (e.g., benign vs. malignant cancer cells) routinely make use of numerical software that are capable of evaluating statistical differences between two populations of cells captured under the microscope (2). Beyond simple automation facilitating the analysis of thousands of cells, the purpose of such software is to attempt to extract information that the human visual system is unable to cope with. A well-known drawback of existing methods is that the visual interpretation of any differences found is usually hidden from the user. The popular numerical features used to quantify and compare cells, such as form factor, Gabor and Haralick texture features, color histograms, etc. (3-5), usually do not have a direct biological interpretation. The situation is even more complicated when multiple features are needed simultaneously to characterize differences between cells, given that the physical interpretation of a combination of features with different units is a nontrivial task. Consequently, statistical tests are limited to determining whether or not two or more cell populations are different. Visual interpretation of any obtained result is usually nonintuitive and difficult.Here we describe a method, which we call transport-based morphometry (TBM), that takes as input a database of presegmented cell images and outputs a representation for the same data, which can be used for simultaneous visualization and quantitative evaluation in commonplace biological domains. An a priori set of numerical features is not needed as all calculations for comparing cells are done using the entire inform...

No abstract

No abstract

Follicular lesions of the thyroid remain significant diagnostic challenges in surgical pathology and cytology. The diagnosis often requires considerable resources and ancillary tests including immunohistochemistry, molecular studies, and expert consultation. Visual analyses of nuclear morphological features, generally speaking, have not been helpful in distinguishing this group of lesions. Here we describe a method for distinguishing between follicular lesions of the thyroid based on nuclear morphology. The method utilizes an optimal transport-based linear embedding for segmented nuclei, together with an adaptation of existing classification methods. We show the method outputs assignments (classification results) which are near perfectly correlated with the clinical diagnosis of several lesion types' lesions utilizing a database of 94 patients in total. Experimental comparisons also show the new method can significantly outperform standard numerical feature-type methods in terms of agreement with the clinical diagnosis gold standard. In addition, the new method could potentially be used to derive insights into biologically meaningful nuclear morphology differences in these lesions. Our methods could be incorporated into a tool for pathologists to aid in distinguishing between follicular lesions of the thyroid. In addition, these results could potentially provide nuclear morphological correlates of biological behavior and reduce health care costs by decreasing histotechnician and pathologist time and obviating the need for ancillary testing.

No abstract