VOLOOM Volumizing Hair Straighteners Iron for Woman (UK Edition) - 1 inch Revolutionary Hair Crimpers - Wide Plates Lifter Add Lasting Volume & Body to Hair - Patented Checkerboard Volumiser Design

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VOLOOM is an absolute miracle for aging hair, which gets finer and thinner, the older you get. Thin hair is very aging, because it accentuates the sagging of the skin and face that comes with age. By lifting the hair up and away from the scalp and face, you can take years off your appearance. We say, “Don’t get a facelift; get a hair-lift with VOLOOM!” All methods benefited from parameter tuning on both image resolutions based on most of the metrics, using either set of landmarks for evaluation (see Table 1 and Supplementary Results). Of the top three methods, MIM and RVSS obtained better accuracy using high resolution images and ESA worked better on the low resolution images. ESA and MIM reached similar mean TRE values, slightly better than RVSS and approaching or exceeding the accuracy of LS. In terms of maximum TRE and ATRE, the three methods were comparable, but RVSS reached slightly lower ATRE than ESA or MIM. Among all tools, ESA and MIM also obtained the highest Jaccard index values. The RMSE and f 2 metrics do not allow comparison across different image resolutions and one should note that MIM’s output was always stored at the lower resolution for technical reasons. Considering these limitations, we can observe that ESA performed best in terms of these metrics on both image resolutions ahead of RVSS. Changes in tissue area introduced by ESA, MIM and RVSS were moderate. Behind the top three, most other tools reached accuracy comparable to each other. The worst results were obtained using default parameters and for some methods, most notably ESA and RVSS, they were even comparable to the unregistered original images. Based on this study, methods utilizing locally varying transformations (ESA, MIM, RVSS, Voloom) were superior to those constrained to global affine models (OPT, SIFT, HSR). ESA was the only method to consistently outperform or match the other approaches on two datasets based on the majority of metrics. In the case of the higher quality prostate dataset, differences in accuracy between the tools were rather subtle. All three top-performing methods on this dataset incorporate an elastic transformation model: MIM and RVSS use a B-spline grid and ESA is based on a piecewise linear mesh. While methods relying on a global transformation model also performed reasonably well, the additional accuracy offered by elastic transformations could be crucial when microstructure at the cellular scale is of interest. In the case of the liver sample, more profound differences between the methods were observed, likely due to the more challenging tissue content and the presence of deformations, which cannot be compensated for using a global model. ESA, MIM and Voloom stood out from the other methods. While Voloom appeared to be less accurate on average compared to ESA and MIM based on mean TRE, it demonstrated the lowest maximum and accumulated errors of all automated methods, indicating capability to avoid propagation of errors even in the presence of considerable deformations. The ability of the algorithms to tolerate such deformations is a significant benefit. Due to the mostly manual nature of histological sectioning and brittleness of the thin tissue sections, deformations in the form of folds and tears often occur. This challenge is especially encountered in 3D histology, when uninterrupted sequences of sections are desired.

Repeat this process as you move VOLOOM down the hair shaft, two to three times, stopping at about eye or cheekbone level. You can experiment with more or less, depending on the length of your hair.This work was supported by Academy of Finland [269474]; Tekes [269/31/2015]; Cancer Society of Finland; Emil Aaltonen Foundation; Finnish Foundation for Technology Promotion; KAUTE Foundation; and Orion Research Foundation.

Take a thin section of hair alongside your face – ½ to 1 inch wide -- and clip it off to the side with the top layer of untreated hair. It will remain untreated and smooth. For each section pair, we evaluated the similarity of corresponding pixels. After conversion to grayscale we computed the following measures: root mean squared error (RMSE), normalized cross correlation (NCC), mutual information (MI) and normalized mutual information (NMI) ( Studholme et al., 1999). Only the set of overlapping tissue pixels A∩ B was considered. These indirect metrics provide information from the entire tissue area and complement the TRE evaluation. 2.2.5 Reconstruction smoothnessMIM: Medical Image Manager, trial v. 0.94, was applied using images subsampled by a factor of 4 (magnification of 5×) as input. Sections 130 and 24 were used as references for the prostate and liver, respectively. We varied the initial magnification (0.3125×, 0.625×, 1.25× or 2.5×) and the number of non-rigid levels (1, 2, 3 or 4), thus modifying the image resolution used.