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[IEEE 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) - Stuttgart, Germany (2018.11.20-2018.11.22)] 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) - Plant Cell Segmentation with Adaptive Thresholding
摘要： There are many approaches to plant cell segmentation, but there is no established method to segment plant cell for a portable, USB-powered optical microscope. Existing methods leverage on sophisticated microscope such as confocal laser scanning microscope or electron microscope may not be applicable for a portable setup. Staining of plant cell specimens, in order to improve visibility of boundaries, might affect the plant cell and also requires additional preparation work prior to acquisition which could be infeasible for on-the-fly applications. Conventional plant cell segmentation using watershed transform often results in over-segmentation, hindering the effectiveness of the method. Hence, we propose a thresholding method based on Otsu's method, to retain majority of the image information to improve the success rate of the cell segmentation. The method is implemented on a leaf cellular image acquired from freshwater weed elodea. The region identified by the improved watershed transform can be further processed to locate the centroids of the cells. We experimented our method on images filled fully with plant cells and filled partially with plant cells. We also studied the impact of boundary definition of the image to our method.
关键词： cell segmentation,watershed transform,image processing
更新于2025-09-23 15:22:29
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Cone Photoreceptor Cell Segmentation and Diameter Measurement on Adaptive Optics Images Using Circularly Constrained Active Contour Model
摘要： PURPOSE. Cone photoreceptor cells can be noninvasively imaged in the living human eye by using nonconfocal adaptive optics scanning ophthalmoscopy split detection. Existing metrics, such as cone density and spacing, are based on simplifying cone photoreceptors to single points. The purposes of this study were to introduce a computer-aided approach for segmentation of cone photoreceptors, to apply this technique to create a normal database of cone diameters, and to demonstrate its use in the context of existing metrics. METHODS. Cone photoreceptor segmentation is achieved through a circularly constrained active contour model (CCACM). Circular templates and image gradients attract active contours toward cone photoreceptor boundaries. Automated segmentation from in vivo human subject data was compared to ground truth established by manual segmentation. Cone diameters computed from curated data (automated segmentation followed by manual removal of errors) were compared with histology and published data. RESULTS. Overall, there was good agreement between automated and manual segmentations and between diameter measurements (n ? 5191 cones) and published histologic data across retinal eccentricities ranging from 1.35 to 6.35 mm (temporal). Interestingly, cone diameter was correlated to both cone density and cone spacing (negatively and positively, respectively; P < 0.01 for both). Application of the proposed automated segmentation to images from a patient with late-onset retinal degeneration revealed the presence of enlarged cones above individual reticular pseudodrusen (average 23.0% increase, P < 0.05). CONCLUSIONS. CCACM can accurately segment cone photoreceptors on split detection images across a range of eccentricities. Metrics derived from this automated segmentation of adaptive optics retinal images can provide new insights into retinal diseases.
关键词： nonconfocal split detection,reticular pseudodrusen,normal database,active contour model,cell segmentation
更新于2025-09-19 17:15:36
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Automated cell segmentation in FIJI? using the DRAQ5 nuclear dye
摘要： Background: Image segmentation and quantification are essential steps in quantitative cellular analysis. In this work, we present a fast, customizable, and unsupervised cell segmentation method that is based solely on Fiji (is just ImageJ)?, one of the most commonly used open-source software packages for microscopy analysis. In our method, the “leaky” fluorescence from the DNA stain DRAQ5 is used for automated nucleus detection and 2D cell segmentation. Results: Based on an evaluation with HeLa cells compared to human counting, our algorithm reached accuracy levels above 92% and sensitivity levels of 94%. 86% of the evaluated cells were segmented correctly, and the average intersection over union score of detected segmentation frames to manually segmented cells was above 0.83. Using this approach, we quantified changes in the projected cell area, circularity, and aspect ratio of THP-1 cells differentiating from monocytes to macrophages, observing significant cell growth and a transition from circular to elongated form. In a second application, we quantified changes in the projected cell area of CHO cells upon lowering the incubation temperature, a common stimulus to increase protein production in biotechnology applications, and found a stark decrease in cell area. Conclusions: Our method is straightforward and easily applicable using our staining protocol. We believe this method will help other non-image processing specialists use microscopy for quantitative image analysis.
关键词： Batch processing,Fiji,Cell segmentation,DRAQ5,Image processing,ImageJ
更新于2025-09-19 17:15:36
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[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Low Resolution Cell Image Edge Segmentation Based on Convolutional Neural Network
摘要： An low-resolution cell images captured by a lens-free imaging system is presented in this paper. The resolution of this cell images is impacted by the low and experimental cell segmentation methods to solve the original cell images is not robust and sensitive to noise. So based on the convolutional neural network, an optimized CSnet method is proposed in this paper for automatically segmenting cell. In the proposed method, the produced data set will be sent into the convolutional neural network firstly for training to obtain an optimized convolution neural network segmentation model. And then, the pre-divided images acquired by the lens-free imaging system are loaded into the segmentation model to get the segmentation images. Finally, our proposed method in this paper is tested in a neural network framework built in keras. The experimental results show that the accuracy of our proposed method can reach about 96%. At the same time, it also can implement batch segmentation automatically and make the problem of heavy task for segmentation better.
关键词： convolutional neural network,cell segmentation,Lensfree imaging,microfluidic chip
更新于2025-09-10 09:29:36
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[IEEE 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) - Washington, DC (2018.4.4-2018.4.7)] 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) - Segmentation of cell nuclei using intensity-based model fitting and sequential convex programming
摘要： We introduce a convex model-based approach for the segmentation of cell nuclei, which exploits both shape and intensity information. The model is directly fitted to the image intensities. Previous shape-based approaches either are not globally optimal or require prior binarization of an image. Our approach relies on a fast second-order optimization scheme to solve a sequence of convex programs and estimate the globally optimal solution based on the image intensities. Model fitting is performed within image regions which are determined by exploiting the local image structure. We evaluated our approach using fluorescence microscopy images of two different cell types and performed a quantitative comparison with previous methods.
关键词： model fitting,Fluorescence microscopy,convex optimization,cell segmentation
更新于2025-09-09 09:28:46