Line Patterns Segmentation in Blurred Images Using Contrast Enhancement and Local Entropy Thresholding

Finger vein extraction and recognition hold significance in various applications due to the unique and reliable nature of finger vein patterns. While recently finger vein recognition has gained popularity, there are still challenges associated with extracting and processing finger vein patterns related to image quality, positioning and alignment, skin conditions, security concerns and processing techniques applied. In this paper, a method for robust segmentation of line patterns in strongly blurred images is presented and evaluated in vessel network extraction from infrared images of human fingers. In a four-step process: local normalization of brightness, image enhancement, segmentation and cleaning were involved. A novel image enhancement method was used to re-establish the line patterns from the brightness sum of the independent close-form solutions of the adopted optimization criterion derived in small windows. In the proposed method, the computational resources were reduced significantly compared to the solution derived when the whole image was processed. In the enhanced image, where the concave structures have been sufficiently emphasized, accurate detection of line patterns was obtained by local entropy thresholding. Typical segmentation errors appearing in the binary image were removed using morphological dilation with a line structuring element and morphological filtering with a majority filter to eliminate isolated blobs. The proposed method performs accurate detection of the vessel network in human finger infrared images, as the experimental results show, applied both in real and artificial images and can readily be applied in many image enhancement and segmentation applications.

Deep learning enhanced NIR-Ⅱ volumetric imaging of whole mice vasculature

Fluorescence imaging through the second near-infrared window(NIR-Ⅱ,1000–1700 nm) allows in-depth imaging.However, current imaging systems use wide-field illumination and can only provide low-contrast 2D information, without depth resolution. Here, we systematically apply a light-sheet illumination, a time-gated detection, and a deep-learning algorithm to yield high-contrast high-resolution volumetric images. To achieve a large Fo V(field of view) and minimize the scattering effect, we generate a light sheet as thin as 100.5 μm with a Rayleigh length of 8 mm to yield an axial resolution of 220 μm. To further suppress the background, we time-gate to only detect long lifetime luminescence achieving a high contrast of up to 0.45 Icontrast. To enhance the resolution, we develop an algorithm based on profile protrusions detection and a deep neural network and distinguish vasculature from a low-contrast area of 0.07 Icontrast to resolve the 100μm small vessels. The system can rapidly scan a volume of view of 75 × 55 × 20 mm3and collect 750 images within 6mins. By adding a scattering-based modality to acquire the 3D surface profile of the mice skin, we reveal the whole volumetric vasculature network with clear depth resolution within more than 1 mm from the skin. High-contrast large-scale 3D animal imaging helps us expand a new dimension in NIR-Ⅱ imaging.

Effect of arteriosclerotic intracranial arterial vessel wall enhancement on downstream collateral flow

Background:The effect of arteriosclerotic intracranial arterial vessel wall enhancement(IAVWE)on downstream collateral flow found in vessel wall imaging(VWI)is not clear.Regardless of the mechanism underlying IAVWE on VWI,damage to the patient’s nervous system caused by IAVWE is likely achieved by affecting downstream cerebral blood flow.The present study aimed to investigate the effect of arteriosclerotic IAVWE on downstream collateral flow.Methods:The present study recruited 63 consecutive patients at the Second Hospital of Hebei Medical University from January 2021 to November 2021 with underlying atherosclerotic diseases and unilateral middle cerebral artery(MCA)M1-segment stenosis who underwent an magnetic resonance scan within 3 days of symptom onset.The patients were divided into 4 groups according to IAVWE and the stenosis ratio(Group 1,n=17;Group 2,n=19;Group 3,n=13;Group 4,n=14),and downstream collateral flow was analyzed using three-dimensional pseudocontinuous arterial spin labeling(3D-pCASL)and RAPID software.The National Institutes of Health Stroke Scale(NIHSS)scores of the patients were also recorded.Two-factor multivariate analysis of variance using Pillai’s trace was used as the main statistical method.Results:No statistically significant difference was found in baseline demographic characteristics among the groups.IAVWE,but not the stenosis ratio,had a statistically significant significance on the late-arriving retrograde flow proportion(LARFP),hypoperfusion intensity ratio(HIR),and NIHSS scores(F=20.941,P<0.001,Pillai’s trace statistic=0.567).The between-subject effects test showed that IAVWE had a significant effect on the three dependent variables:LARFP(R^(2)=0.088,F=10.899,P=0.002),HIR(R^(2)=0.234,F=29.354,P<0.001),and NIHSS(R^(2)=114.339,F=33.338,P<0.001).Conclusions:Arteriosclerotic IAVWE significantly reduced downstream collateral flow and affected relevant neurological deficits.It was an independent factor affecting downstream collateral flow and NIHSS scores,which should be a focus of future studies.Trial Registration:ChiCTR.org.cn,ChiCTR2100053661.