In the reconstruction of the quantitative photoacoustic tomography (QPAT), forward models for both the optical and acoustic problems are usually needed to predict the measurement data from a guess of the distribution of the optical parameters. The diffusion approximation (DA) is the one most often employed as the optical forward model in the QPAT. However, this model usually results in predicted data deviating far from the actual measurements especially in low scattering tissues. To tackle such a problem, we propose a reconstruction method where the modeling error of the DA is modeled and considered in the framework of Bayesian inference. Experimental results show that modeling of the approximation error and considering it in the reconstruction procedure can significantly improve the reconstructed results of the QPAT.
The purpose of this study is to investigate the role of shape and texture in the classification of hepatic fibrosis by selecting the optimal parameters for a better Computer-aided diagnosis (CAD) system. 10 surface shape features are
extracted from a standardized profile of liver; while15 texture features calculated from gray level co-occurrence matrix
(GLCM) are extracted within an ROI in liver. Each combination of these input subsets is checked by using support vector machine (SVM) with leave-one-case-out method to differentiate fibrosis into two groups: normal or abnormal.
The accurate rate value of all 10/15 types number of features is 66.83% by texture, while 85.74% by shape features,
respectively. The irregularity of liver shape can demonstrate fibrotic grade efficiently and texture feature of CT image is
not recommended to use with shape feature for interpretation of cirrhosis.
Super resolution optical microscopy allows the capture of images with a higher resolution than the diffraction limit. However, due to the lack of a standard format, the processing, visualization, transfer, and exchange of Super Resolution Optical Microscope (SROM) images are inconvenient. In this work, we present an approach to standardize the SROM images based on the Digital Imaging and Communication in Medicine (DICOM) standard. The SROM images and associated information are encapsulated and converted to DICOM images based on the Visible Light Microscopic Image Information Object Definition of DICOM. The new generated SROM images in DICOM format can be displayed, processed, transferred, and exchanged by using most medical image processing tools.
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