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Image processing is the main topic of discussion in the field of computer vision technology. With the increase in the number of images used over time, the types of images with different resolution qualities are becoming more diverse. Low image resolution leads to uncertainty in the task of image processing. Therefore, a method with high performance is needed for image processing. In image processing, there is a Convolutional Neural Networks (CNN) architecture for semantic segmentation of pixels called U-Net. U-Net is formed by an encoder network and decoder network that will later produce segmented images. In this paper, researchers applied the U-Net architecture to the lung CT image dataset, which has different resolutions in each image, to segment the image that produces a segmented lung image. In this study, we conducted experiments for many training and testing data ratios while also comparing the model performances between the single resolution dataset and the multiresolution dataset. The results showed that the segmentation accuracy using a single resolution dataset is as follows: 5 to 5 ratio is 66.00%, 8 to 2 ratio is 88.96%, and 9 to 1 ratio is 94.47%. For the multiresolution dataset, the application is: 5 to 5 ratio is 82.42%, 8 to 2 ratio is 90.12%, and 9 to 1 ratio is 93.66%. And for the result, the training time using single resolution dataset are: 5 to 5 ratio is 59.94 seconds, 8 to 2 ratio is 87.16 seconds, and 9 to 1 ratio is 195.34 seconds, as for multiresolution data application are: 5 to 5 ratio is 49.60 seconds, 8 to 2 ratio is 102.08 seconds, and 9 to 1 ratio is 199.79 seconds. Based on those results, we obtained the best accuracy for single resolution at a 9:1 ratio and the best training time for multiresolution at a 5:5 ratio. Doi: 10.28991/ESJ-2023-07-02-014 Full Text: PDF

The determination of infection rate parameter from in vitro macrophages infected by Leishmania amastigotes is fundamental in the study of vaccine candidates and new drugs for the treatment of leishmaniasis. The conventional method that consists in the amastigotes count inside macrophages, normally is done by a trained microscope technician, which is liable to misinterpretation and sampling. The objective of this work is to develop a method for the segmentation of images to enable the automatic calculation of the infection rate by amastigotes. Segmentation is based on mathematical morphology in the context of a computer vision system. The results obtained by computer vision system presents a 95% accuracy in comparison to the conventional method. Therefore, the proposed method can contribute to the speed and accuracy of analysis of infection rate, minimizing errors from the traditional methods, especially in situations where exhaustive repetitions of the procedure are required from the technician.

Analysing biological images coming from the microscope is challenging; not only is it complex to acquire the images, but also the three-dimensional shapes found on them. Thus, using automatic approaches that could learn and embrace that variance would be highly interesting for the field. Here, we use an evolutionary algorithm to obtain the 3D cell shape of curve epithelial tissues. Our approach is based on the application of a 3D segmentation algorithm called LimeSeg, which is a segmentation software that uses a particle-based active contour method. This program needs the fine-tuning of some hyperparameters that could present a long number of combinations, with the selection of the best parametrisation being highly time-consuming. Our evolutionary algorithm automatically selects the best possible parametrisation with which it can perform an accurate and non-supervised segmentation of 3D curved epithelial tissues. This way, we combine the segmentation potential of LimeSeg and optimise the parameters selection by adding automatisation. This methodology has been applied to three datasets of confocal images from Drosophila melanogaster, where a good convergence has been observed in the evaluation of the solutions. Our experimental results confirm the proper performing of the algorithm, whose segmented images have been compared to those manually obtained for the same tissues. ; B.P. research is funded by FEDER/Ministerio de Ciencia e Innovación—Agencia Estatal de Investigación/Proyecto TIN2017-88209-C2 and by the Andalusian Regional Government under the projects: US-1263341 and P18-RT-2778. P.V.-M. and P.G.-G. work is funded by the Ministry of Economy, Industry and Competitiveness grant BFU2016-74975-P co-funded by FEDER funds, and by the Spanish Ministry of Science and Innovation Ministry of Science through grant PID2019-103900GB-I00.

[EN] In this work, we propose a new Bayesian model for unsupervised image segmentation based on a combination of the spatially varying finite mixture models (SVFMMs) and the non-local means (NLM) framework. The probabilistic NLM weighting function is successfully integrated into a varying Gauss¿Markov random field, yielding a prior density that adaptively imposes a local regularization to simultaneously preserve edges and enforce smooth constraints in homogeneous regions of the image. Two versions of our model are proposed: a pixel-based model and a patch-based model, depending on the design of the probabilistic NLM weighting function. Contrary to previous methods proposed in the literature, our approximation does not introduce new parameters to be estimated into the model, because the NLM weighting function is completely known once the neighborhood of a pixel is fixed. The proposed model can be estimated in closed-form solution via a maximum a posteriori (MAP) estimation in an expectation¿maximization scheme. We have compared our model with previously proposed SVFMMs using two public datasets: the Berkeley Segmentation dataset and the BRATS 2013 dataset. The proposed model performs favorably to previous approaches in the literature, achieving better results in terms of Rand Index and Dice metrics in our experiments. ; This study is partially supported by Secretaria de Estado de Investigacion, Desarrollo e Innovacion (DPI2016-80054-R, TIN2013-43457-R) and Agencia Valenciana de la Innovacion (INNVAL10/18/048). E.F.G was supported by the European Union's Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement (No. 844646) and also acknowledges the support of NVIDIA GPU Grant Program. ; Juan -Albarracín, J.; Fuster García, E.; Juan, A.; Garcia-Gomez, JM. (2021). Non-local spatially varying finite mixture models for image segmentation. Statistics and Computing. 31(1):1-10. https://doi.org/10.1007/s11222-020-09988-w ; S ; 1 ; 10 ; 31 ; 1

Background Medical Image segmentation is an important image processing step. Comparing images to evaluate the quality of segmentation is an essential part of measuring progress in this research area. Some of the challenges in evaluating medical segmentation are: metric selection, the use in the literature of multiple definitions for certain metrics, inefficiency of the metric calculation implementations leading to difficulties with large volumes, and lack of support for fuzzy segmentation by existing metrics. Result First we present an overview of 20 evaluation metrics selected based on a comprehensive literature review. For fuzzy segmentation, which shows the level of membership of each voxel to multiple classes, fuzzy definitions of all metrics are provided. We present a discussion about metric properties to provide a guide for selecting evaluation metrics. Finally, we propose an efficient evaluation tool implementing the 20 selected metrics. The tool is optimized to perform efficiently in terms of speed and required memory, also if the image size is extremely large as in the case of whole body MRI or CT volume segmentation. An implementation of this tool is available as an open source project. Conclusion We propose an efficient evaluation tool for 3D medical image segmentation using 20 evaluation metrics and provide guidelines for selecting a subset of these metrics that is suitable for the data and the segmentation task. ; European Union Seventh Framework Programme (FP7/2007-2013) ; 1

High-resolution remote sensing image segmentation is a mature application in many industrial-level image applications and it also has military and civil applications. The scene analysis needs to be automated as much as possible with high-resolution remote sensing images. This plays a significant role in environmental disaster monitoring, forestry industry, agricultural farming, urban planning, and road analysis. This study proposes a multi-level feature fusion network (MFNet) that can integrate the multi-level features in the backbone to obtain different types of image information. Finally, the experiments in this study demonstrate that the proposed network can achieve good segmentation results in the Vaihingen and Potsdam datasets. By aiming to achieve a large difference in the scale of the target objects in remote sensing images and achieving a poor recognition result for small objects, a multi-level feature fusion solution is proposed in this study. This investigation improves the recognition results of the remote sensing image segmentation to a certain extent.

Image segmentation is a necessary step in automated quantitative imaging. ImageSURF is a macro-compatible ImageJ2/FIJI plugin for pixel-based image segmentation that considers a range of image derivatives to train pixel classifiers which are then applied to image sets of any size to produce segmentations without bias in a consistent, transparent and reproducible manner. The plugin is available from ImageJ update site http://sites.imagej.net/ImageSURF/ and source code from https://github.com/omaraa/ImageSURF. Funding statement: This research was supported by an Australian Government Research Training Program Scholarship.

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis that produces pulmonary damage. Radiological imaging is the preferred technique for the assessment of TB longitudinal course. Computer-assisted identification of biomarkers eases the work of the radiologist by providing a quantitative assessment of disease. Lung segmentation is the step before biomarker extraction. In this study, we present an automatic procedure that enables robust segmentation of damaged lungs that have lesions attached to the parenchyma and are affected by respiratory movement artifacts in a Mycobacterium Tuberculosis infection model. Its main steps are the extraction of the healthy lung tissue and the airway tree followed by elimination of the fuzzy boundaries. Its performance was compared with respect to a segmentation obtained using: (1) a semi-automatic tool and (2) an approach based on fuzzy connectedness. A consensus segmentation resulting from the majority voting of three experts' annotations was considered our ground truth. The proposed approach improves the overlap indicators (Dice similarity coefficient, 94% ± 4%) and the surface similarity coefficients (Hausdorff distance, 8.64 mm ± 7.36 mm) in the majority of the most difficult-to-segment slices. Results indicate that the refined lung segmentations generated could facilitate the extraction of meaningful quantitative data on disease burden. ; The research leading to these results received funding from the Innovative Medicines Initiative (www.imi.europa.eu) Joint Undertaking under grant agreement no. 115337, whose resources comprise funding from the European Union's Seventh Framework Programme (FP7/2007–2013) and EFPIA companies' in kind contribution. This work was partially funded by projects TEC2013-48552-C2-1-R, RTC-2015-3772-1, TEC2015-73064-EXP and TEC2016-78052-R from the Spanish Ministerio de Economía, Industria y Competitividad, TOPUS S2013/MIT-3024 project from the regional government of Madrid and by the Department of Health, UK.