10 research outputs found
A robust technique based on VLM and Frangi filter for retinal vessel extraction and denoising
<div><p>The exploration of retinal vessel structure is colossally important on account of numerous diseases including stroke, Diabetic Retinopathy (DR) and coronary heart diseases, which can damage the retinal vessel structure. The retinal vascular network is very hard to be extracted due to its spreading and diminishing geometry and contrast variation in an image. The proposed technique consists of unique parallel processes for denoising and extraction of blood vessels in retinal images. In the preprocessing section, an adaptive histogram equalization enhances dissimilarity between the vessels and the background and morphological top-hat filters are employed to eliminate macula and optic disc, etc. To remove local noise, the difference of images is computed from the top-hat filtered image and the high-boost filtered image. Frangi filter is applied at multi scale for the enhancement of vessels possessing diverse widths. Segmentation is performed by using improved Otsu thresholding on the high-boost filtered image and Frangi’s enhanced image, separately. In the postprocessing steps, a Vessel Location Map (VLM) is extracted by using raster to vector transformation. Postprocessing steps are employed in a novel way to reject misclassified vessel pixels. The final segmented image is obtained by using pixel-by-pixel AND operation between VLM and Frangi output image. The method has been rigorously analyzed on the STARE, DRIVE and HRF datasets.</p></div
Accuracy (Acc), Sensitivity (Sn) and Specificity (Sp) statistics of the proposed system on the DRIVE, STARE and HRF databases.
<p>Accuracy (Acc), Sensitivity (Sn) and Specificity (Sp) statistics of the proposed system on the DRIVE, STARE and HRF databases.</p
Performance metrics for evaluation of the proposed method.
<p>Performance metrics for evaluation of the proposed method.</p
Analysis of Frangi filtering enhancement using DRIVE dataset.
<p>(a) Thin vessel enhanced image (b) Thin binary image (c) Thick vessel enhanced image (d) Thick binary image.</p
Analysis of Frangi filtering enhancement using STARE dataset.
<p><b>A</b> (a) Thin vessel enhanced image (b) Thin binary image (c) Thick vessel enhanced image (d) Thick binary image.</p
Visual presentation of the Proposed system major processing stages.
<p>(a) Input RGB photograph from <b>HRF</b> database (b) Green channel (c) CLAHE applied result (d) Difference image (e) Otsu threshold resultant image (f) Postprocessed dilated image (g) Frangi filter enhanced image (h) Final image using AND Operation.</p
Performance evaluations of various retinal vascular extraction algorithms.
<p>Performance evaluations of various retinal vascular extraction algorithms.</p
Visual appearance of the proposed technique utilizing STARE dataset.
<p>(a) RGB photograph (b) Manual segmentation (c) Proposed technique segmented image.</p
Pictorial representation for unhealthy retinal image from the STARE dataset.
<p>(a) RGB image (b) Manual segmentation (c) Proposed scheme final result.</p
Refining Defect States in W<sub>18</sub>O<sub>49</sub> by Mo Doping: A Strategy for Tuning N<sub>2</sub> Activation towards Solar-Driven Nitrogen Fixation
Photocatalysis
may provide an intriguing approach to nitrogen fixation,
which relies on the transfer of photoexcited electrons to the ultrastable
Nî—¼N bond. Upon N<sub>2</sub> chemisorption at active sites
(e.g., surface defects), the N<sub>2</sub> molecules have yet to receive
energetic electrons toward efficient activation and dissociation,
often forming a bottleneck. Herein, we report that the bottleneck
can be well tackled by refining the defect states in photocatalysts
via doping. As a proof of concept, W<sub>18</sub>O<sub>49</sub> ultrathin
nanowires are employed as a model material for subtle Mo doping, in
which the coordinatively unsaturated (CUS) metal atoms with oxygen
defects serve as the sites for N<sub>2</sub> chemisorption and electron
transfer. The doped low-valence Mo species play multiple roles in
facilitating N<sub>2</sub> activation and dissociation by refining
the defect states of W<sub>18</sub>O<sub>49</sub>: (1) polarizing
the chemisorbed N<sub>2</sub> molecules and facilitating the electron
transfer from CUS sites to N<sub>2</sub> adsorbates, which enables
the Nî—¼N bond to be more feasible for dissociation through proton
coupling; (2) elevating defect-band center toward the Fermi level,
which preserves the energy of photoexcited electrons for N<sub>2</sub> reduction. As a result, the 1 mol % Mo-doped W<sub>18</sub>O<sub>49</sub> sample achieves an ammonia production rate of 195.5 μmol
g<sub>cat</sub><sup>–1</sup> h<sup>–1</sup>, 7-fold
higher than that of pristine W<sub>18</sub>O<sub>49</sub>. In pure
water, the catalyst demonstrates an apparent quantum efficiency of
0.33% at 400 nm and a solar-to-ammonia efficiency of 0.028% under
simulated AM 1.5 G light irradiation. This work provides fresh insights
into the design of photocatalyst lattice for N<sub>2</sub> fixation
and reaffirms the versatility of subtle electronic structure modulation
in tuning catalytic activity