Browsing by Author "Wang, X."

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Enhancement and bias removal of optical coherence tomography images: An iterative approach with adaptive bilateral filtering
(2016) Sudeep, P.V.; Issac, Niwas, S.; Palanisamy, P.; Rajan, J.; Xiaojun, Y.; Wang, X.; Luo, Y.; Liu, L.
Optical coherence tomography (OCT) has continually evolved and expanded as one of the most valuable routine tests in ophthalmology. However, noise (speckle) in the acquired images causes quality degradation of OCT images and makes it difficult to analyze the acquired images. In this paper, an iterative approach based on bilateral filtering is proposed for speckle reduction in multiframe OCT data. Gamma noise model is assumed for the observed OCT image. First, the adaptive version of the conventional bilateral filter is applied to enhance the multiframe OCT data and then the bias due to noise is reduced from each of the filtered frames. These unbiased filtered frames are then refined using an iterative approach. Finally, these refined frames are averaged to produce the denoised OCT image. Experimental results on phantom images and real OCT retinal images demonstrate the effectiveness of the proposed filter. 2016 Elsevier Ltd.
Enhancement and bias removal of optical coherence tomography images: An iterative approach with adaptive bilateral filtering
(Elsevier Ltd, 2016) Sudeep, P.V.; Issac Niwas, S.; Ponnusamy, P.; Rajan, J.; Xiaojun, Y.; Wang, X.; Luo, Y.; Liu, L.
Optical coherence tomography (OCT) has continually evolved and expanded as one of the most valuable routine tests in ophthalmology. However, noise (speckle) in the acquired images causes quality degradation of OCT images and makes it difficult to analyze the acquired images. In this paper, an iterative approach based on bilateral filtering is proposed for speckle reduction in multiframe OCT data. Gamma noise model is assumed for the observed OCT image. First, the adaptive version of the conventional bilateral filter is applied to enhance the multiframe OCT data and then the bias due to noise is reduced from each of the filtered frames. These unbiased filtered frames are then refined using an iterative approach. Finally, these refined frames are averaged to produce the denoised OCT image. Experimental results on phantom images and real OCT retinal images demonstrate the effectiveness of the proposed filter. © 2016 Elsevier Ltd.
NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter Scale
(Springer Science and Business Media Deutschland GmbH, 2021) Lin, Z.; Wei, D.; Petkova, M.D.; Wu, Y.; Ahmed, Z.; K, K.S.; Zou, S.; Wendt, N.; Boulanger-Weill, J.; Wang, X.; Dhanyasi, N.; Arganda-Carreras, I.; Engert, F.; Lichtman, J.; Pfister, H.
Segmenting 3D cell nuclei from microscopy image volumes is critical for biological and clinical analysis, enabling the study of cellular expression patterns and cell lineages. However, current datasets for neuronal nuclei usually contain volumes smaller than 10- 3 mm3 with fewer than 500 instances per volume, unable to reveal the complexity in large brain regions and restrict the investigation of neuronal structures. In this paper, we have pushed the task forward to the sub-cubic millimeter scale and curated the NucMM dataset with two fully annotated volumes: one 0.1Â mm3 electron microscopy (EM) volume containing nearly the entire zebrafish brain with around 170,000 nuclei; and one 0.25Â mm3 micro-CT (uCT) volume containing part of a mouse visual cortex with about 7,000 nuclei. With two imaging modalities and significantly increased volume size and instance numbers, we discover a great diversity of neuronal nuclei in appearance and density, introducing new challenges to the field. We also perform a statistical analysis to illustrate those challenges quantitatively. To tackle the challenges, we propose a novel hybrid-representation learning model that combines the merits of foreground mask, contour map, and signed distance transform to produce high-quality 3D masks. The benchmark comparisons on the NucMM dataset show that our proposed method significantly outperforms state-of-the-art nuclei segmentation approaches. Code and data are available at https://connectomics-bazaar.github.io/proj/nucMM/index.html. Â© 2021, Springer Nature Switzerland AG.
Placed-based interpretation of the sustainable development goals for the land-river interface
(Springer, 2022) Vercruysse, K.; Grabowski, R.C.; Holman, I.; Azhoni, A.; Bala, B.; Meersmans, J.; Peng, J.; Shankar, V.; Mukate, S.; Poddar, A.; Wang, X.; Zhang, Z.
The land–river interface (LRI) is important for sustainable development. The environmental processes that define the LRI support the natural capital and ecosystem services that are linked directly to multiple Sustainable Development Goals (SDGs). However, existing approaches to scale up or down SDG targets and link them to natural capital are insufficient for the two-way human–environment interactions that exist in the LRI. Therefore, this study proposes a place-based approach to interpret the SDG framework to support sustainable land/water management, by (i) identifying key priorities for sustainable development through a normative content analysis of the SDG targets, and (ii) illustrating these priorities and associated challenges within the LRI, based on a literature review and case-studies on human–environment interactions. The content analysis identifies three overarching sustainable development priorities: (i) ensuring improved access to resources and services provided by the LRI, (ii) strengthening the resilience of the LRI to deal with social and natural shocks, and (iii) increasing resource efficiency. The review of the current state of LRIs across the world confirms that these are indeed priority areas for sustainable development. Yet, the challenges of attaining the sustainable development priorities in the LRI are also illustrated with three examples of development-related processes. Urbanisation, dam construction, and aggregate mining occur within specific zones of the LRI (land, land–river, river, respectively), but their impacts can compromise sustainable development across the entire LRI and beyond. The existence of these unintended impacts highlights the need to consider the geomorphic, hydrological, and ecological processes within the LRI and how they interact with human activity. Identifying the place-based priorities and challenges for sustainable development will help achieve the SDGs without compromising the functions and services of the LRI. © 2022, The Author(s).