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

Opto-Electronic Advances 2023年第4期6卷 6-14页

作者： Sitong Wu Zhichao Yang Chenguang Ma Xun Zhang Chao Mi Jiajia Zhou Zhiyong Guo Dayong Jin UTS-SUSTech Joint Research Centre for Biomedical Materials&Devices Department of Biomedical EngineeringSouthern University of Science and TechnologyShenzhen 518055China Institute for Biomedical Materials&Devices Faculty of ScienceUniversity of Technology SydneyUltimoNew South Wales 2007Australia Guangdong Provincial Key Laboratory of Advanced Biomaterials Southern University of Science and TechnologyShenzhen 518055China

Fluorescence imaging through the second near-infrared window(NIR-Ⅱ,1000–1700 nm) allows in-depth ***, 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.

关键词： NIR-Ⅱfluorescence time-gated light sheet illumination deep learning vessel enhancement 3D imaging

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

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Journal of Computer and Communications 2024年第2期12卷 116-141页

作者： Marios Vlachos Evangelos Dermatas Institute of Communication and Computer Systems Athens Greece Department of Computer Engineering and Informatics University of Patras Patras Greece

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.

关键词： Finger Vein vessel enhancement vessel Network Extraction Non-Uniform Images Binarization Morphological Post-Processing

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