Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing

<p>Abstract</p> <p>Background</p> <p>In recent years, magnetic resonance imaging (MRI) has become important in brain tumor diagnosis. Using this modality, physicians can locate specific pathologies by analyzing differences in tissue character presented in different types of MR images.</p> <p>This paper uses an algorithm integrating fuzzy-c-mean (FCM) and region growing techniques for automated tumor image segmentation from patients with menigioma. Only non-contrasted T1 and T2 -weighted MR images are included in the analysis. The study's aims are to correctly locate tumors in the images, and to detect those situated in the midline position of the brain.</p> <p>Methods</p> <p>The study used non-contrasted T1- and T2-weighted MR images from 29 patients with menigioma. After FCM clustering, 32 groups of images from each patient group were put through the region-growing procedure for pixels aggregation. Later, using knowledge-based information, the system selected tumor-containing images from these groups and merged them into one tumor image. An alternative semi-supervised method was added at this stage for comparison with the automatic method. Finally, the tumor image was optimized by a morphology operator. Results from automatic segmentation were compared to the "ground truth" (GT) on a pixel level. Overall data were then evaluated using a quantified system.</p> <p>Results</p> <p>The quantified parameters, including the "percent match" (PM) and "correlation ratio" (CR), suggested a high match between GT and the present study's system, as well as a fair level of correspondence. The results were compatible with those from other related studies. The system successfully detected all of the tumors situated at the midline of brain.</p> <p>Six cases failed in the automatic group. One also failed in the semi-supervised alternative. The remaining five cases presented noticeable edema inside the brain. In the 23 successful cases, the PM and CR values in the two groups were highly related.</p> <p>Conclusions</p> <p>Results indicated that, even when using only two sets of non-contrasted MR images, the system is a reliable and efficient method of brain-tumor detection. With further development the system demonstrates high potential for practical clinical use.</p

Automatic extraction of shorelines from Landsat TM and ETM+ multi-temporal images with subpixel precision

A high precision geometric method for automated shoreline detection from Landsat TM and ETM+ imagery is presented. The methodology is based on the application of an algorithm that ensures accurate image geometric registration and the use of a new algorithm for sub-pixel shoreline extraction, both at the sub-pixel level. The analysis of the initial errors shows the influence that differences in reflectance of land cover types have over shoreline detection, allowing us to create a model to substantially reduce these errors. Three correction models were defined according to the type of gain used in the acquisition of the original Landsat images. Error assessment tests were applied on three artificially stabilised coastal segments that have a constant and well-defined land-water boundary. A testing set of 45 images (28 TM, 10 ETM high-gain and 7 ETM low-gain) was used. The mean error obtained in shoreline location ranges from 1.22 to 1.63. m, and the RMSE from 4.69 to 5.47. m. Since the errors follow a normal distribution, then the maximum error at a given probability can be estimated. The results confirm that the use of Landsat imagery for detection of instantaneous coastlines yields accuracy comparable to high-resolution techniques, showing the potential of Landsat TM and ETM images in those applications where the instantaneous lines are a good geomorphological descriptor. © 2012 Elsevier Inc.The authors appreciate the financial support provided by the Spanish Ministerio de Ciencia e Innovacion and the Spanish Plan E in the framework of the Projects CGL2009-14220-C02-01 and CGL2010-19591.Pardo Pascual, JE.; Almonacid Caballer, J.; Ruiz Fernández, LÁ.; Palomar-Vázquez, J. (2012). Automatic extraction of shorelines from Landsat TM and ETM+ multi-temporal images with subpixel precision. Remote Sensing of Environment. 123:1-11. doi:10.1016/j.rse.2012.02.024S11112

Study of e+e−→ppˉe^+e^- \rightarrow p\bar{p} in the vicinity of ψ(3770)\psi(3770)

Using 2917 $\rm{pb}^{-1}$ of data accumulated at 3.773~$\rm{GeV}$, 44.5~$\rm{pb}^{-1}$ of data accumulated at 3.65~$\rm{GeV}$ and data accumulated during a $\psi(3770)$ line-shape scan with the BESIII detector, the reaction $e^+e^-\rightarrow p\bar{p}$ is studied considering a possible interference between resonant and continuum amplitudes. The cross section of $e^+e^-\rightarrow\psi(3770)\rightarrow p\bar{p}$, $\sigma(e^+e^-\rightarrow\psi(3770)\rightarrow p\bar{p})$, is found to have two solutions, determined to be ($0.059\pm0.032\pm0.012$) pb with the phase angle $\phi = (255.8\pm37.9\pm4.8)^\circ$ ($<$0.11 pb at the 90% confidence level), or $\sigma(e^+e^-\rightarrow\psi(3770)\rightarrow p\bar{p}) = (2.57\pm0.12\pm0.12$) pb with $\phi = (266.9\pm6.1\pm0.9)^\circ$ both of which agree with a destructive interference. Using the obtained cross section of $\psi(3770)\rightarrow p\bar{p}$, the cross section of $p\bar{p}\rightarrow \psi(3770)$, which is useful information for the future PANDA experiment, is estimated to be either ($9.8\pm5.7$) nb ($<17.2$ nb at 90% C.L.) or $(425.6\pm42.9)$ nb

Light absorption of black carbon and brown carbon in winter in North China Plain: comparisons between urban and rural sites

The light absorption black carbon (BC) and brown carbon (BrC) are two important sources of uncertainties in radiative forcing estimate. Here we investigated the light absorption enhancement (Eabs) of BC due to coated materials at an urban (Beijing) and a rural site (Gucheng) in North China Plain (NCP) in winter 2019 by using a photoacoustic extinctiometer coupled with a thermodenuder. Our results showed that the average (±1σ) Eabs was 1.32 (±0.15) at the rural site, which was slightly higher than that at the urban site (1.24 ± 0.15). The dependence of Eabs on coating materials was found to be relatively limited at both sites. However, Eabs presented considerable increases as a function of relative humidity below 70%. Further analysis showed that Eabs during non-heating period in Beijing was mainly caused by secondary components, while it was dominantly contributed by enhanced primary emissions in heating season at both sites. In particular, aerosol particles mixed with coal combustion emissions had a large impact on Eabs (>1.40), while the fresh traffic emissions and freshly oxidized secondary OA (SOA) had limited Eabs (1.00–1.23). Although highly aged or aqueous-phase processed SOA coated on BC showed the largest Eabs, their contributions to the bulk absorption enhancement were generally small. We also quantified the absorption of BrC and source contributions. The results showed the BrC absorption at the rural site was nearly twice that of urban site, yet absorption Ångström exponents were similar. Multiple linear regression analysis highlighted the major sources of BrC being coal combustion emissions and photochemical SOA at both sites with additional biomass burning at the rural site. Overall, our results demonstrated the relatively limited winter light absorption enhancement of BC in different chemical environments in NCP, which needs be considered in regional climate models to improve BC radiative forcing estimates