Image textures in computed tomography colonography (CTC) have great potential for differentiating non-neoplastic from neoplastic polyps and thus can advance the current CTC detection-only paradigm to a new level toward optimal polyp management to prevent the deadly colorectal cancer. However, image textures are frequently compromised due to noise smoothing and other error-correction operations in most CT image reconstructions. Furthermore, because of polyp orientation variation in patient space, texture features extracted in that space can vary accordingly, resulting in variable results. To address these issues, this study proposes an adaptive approach to extract and analyze the texture features for polyp differentiation. Firstly, derivative operations are performed on the CT intensity image to amplify the textures, e.g. in the 1st order derivative (gradient) and 2nd order derivative (curvature) images, with adequate noise control. Then the Haralick co-occurrence matrix (CM) is used to calculate texture measures along each of the 13 directions (defined by the 1st and 2nd order image voxel neighbors) through the polyp volume in the intensity, gradient and curvature images. Instead of taking the mean and range of each CM measure over the 13 directions as the so-called Haralick texture features, the Karhunen-Loeve transform is performed to map the 13 directions into an orthogonal coordinate system where all the CM measures are projected onto the new coordinates so that the resulted texture features are less dependent on the polyp spatial orientation variation. While the ideas for amplifying textures and stabilizing spatial variation are simple, their impacts are significant for the task of differentiating non-neoplastic from neoplastic polyps as demonstrated by experiments using 384 polyp datasets, of which 52 are non-neoplastic polyps and the rest are neoplastic polyps. By the merit of area under the curve of receiver operating characteristic, the innovative ideas achieved differentiation capability of 0.8016, indicating the feasibility of advancing CTC toward personal healthcare for preventing colorectal cancer.
Recently many attempts have been made for extracting the structural information of myofibrils as indicators for diseases of skeletal muscle. In this paper we adopt wide-field illumination and take the backscattering Mueller matrix images of bovine skeletal muscle tissues during the 24-hour experimental time after the animal's death. The 2D images of Mueller matrix elements and their frequency distribution histograms (FDHs) reveal rich qualitative information on the changes in the microstructures of the skeletal muscle. The temporal variations of the sample are quantitatively analyzed using two Mueller matrix transformation (MMT) parameters. The characteristic features of the temporal plots are attributed to the rigor mortis and proteolysis processes. For a deeper insight on the relationship between the features of the MMT parameters and the microstructures during the rigor mortis and proteolysis processes, Monte Carlo (MC) simulations are carried out based on sphere-cylinder birefringence model (SCBM). The good agreement between the experimental and MC simulated results show that the FDHs and MMT parameters can describe more clearly the characteristic microstructural features of skeletal muscle tissues. The techniques are useful for the characterization of physiological status of tissues, or quantitative assessment of meat qualities in food industry.
In this paper, we propose a coupled level set (LS) framework for segmentation of bladder wall using T1-weighted magnetic resonance (MR) images with clinical applications to virtual cystoscopy (i.e., MR cystography). The framework uses two collaborative LS functions and a regional adaptive clustering algorithm to delineate the bladder wall for the wall thickness measurement on a voxel-by-voxel basis. It is significantly different from most of the pre-existing bladder segmentation work in four aspects. First of all, while most previous work only segments the inner border of the wall or at most manually segments the outer border, our framework extracts both the inner and outer borders automatically except that the initial seed point is given by manual selection. Secondly, it is adaptive to T1-weighted images with decreased intensities in urine, as opposed to enhanced intensities in T2-weighted scenario and computed tomography. Thirdly, by considering the image global intensity distribution and local intensity contrast, the defined image energy function in the framework is more immune to inhomogeneity effect, motion artifacts and image noise. Finally, the bladder wall thickness is measured by the length of integral path between the two borders which mimic the electric field line between two iso-potential surfaces. The framework was tested on six datasets with comparison to the well-known Chan-Vese (C-V) LS model. Five experts blindly scored the segmented inner and outer borders of the presented framework and the C-V model. The scores demonstrated statistically the improvement in detecting the inner and outer borders.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with đź’™ for researchers
Part of the Research Solutions Family.