scholarly journals TongueNet: A Precise and Fast Tongue Segmentation System Using U-Net with a Morphological Processing Layer

2019 ◽  
Vol 9 (15) ◽  
pp. 3128 ◽  
Author(s):  
Jianhang Zhou ◽  
Qi Zhang ◽  
Bob Zhang ◽  
Xiaojiao Chen
Keyword(s):  
State Of The Art ◽  
Automatic Segmentation ◽  
Morphological Processing ◽  
Small Scale ◽  
Tongue Diagnosis ◽  
Qualitative And Quantitative ◽  
Non Invasive ◽  
Segmentation Methods ◽  
Image Dataset ◽  
Tongue Segmentation

Automated tongue segmentation is a critical component of tongue diagnosis, especially in Traditional Chinese Medicine (TCM), where it has been practiced for thousands of years and is generally considered pain-free and non-invasive. Therefore, a more precise, fast, and robust tongue segmentation system to automatically segment tongue images from its raw format is necessary. Previous algorithms segmented the tongue in different ways, where the results are either inaccurate or time-consuming. Furthermore, none of them developed a dedicated, automatic segmentation system. In this paper, we proposed TongueNet, which is a precise and fast automatic tongue segmentation system. U-net is utilized as the segmentation backbone applying a small-scale image dataset. Besides this, a morphological layer is proposed in the latter stages of the architecture. The proposed system when applied to a tongue image dataset with 1000 images, achieved the highest Pixel Accuracy of 98.45% and consumed 0.267 s per picture on average, which outperformed conventional state-of-the-art tongue segmentation methods in both accuracy and speed. Extensive qualitative and quantitative experiments showed the robustness of the proposed system concerning different positions, poses, and shapes. The results indicate a promising step in achieving a fully automated tongue diagnosis system.

scholarly journals LSM-SEC: Tongue Segmentation by the Level Set Model with Symmetry and Edge Constraints

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Shanshan Gao ◽  
Ningning Guo ◽  
Deqian Mao
Keyword(s):  
Level Set ◽  
The Other ◽  
Surrounding Tissue ◽  
Geometric Features ◽  
Symmetry Detection ◽  
Qualitative And Quantitative ◽  
Segmentation Methods ◽  
Edge Probability ◽  
Tongue Segmentation ◽  
Level Set Model

Accurate segmentation of the tongue body is an important prerequisite for computer-aided tongue diagnosis. In general, the size and shape of the tongue are very different, the color of the tongue is similar to the surrounding tissue, the edge of the tongue is fuzzy, and some of the tongue is interfered by pathological details. The existing segmentation methods are often not ideal for tongue image processing. To solve these problems, this paper proposes a symmetry and edge-constrained level set model combined with the geometric features of the tongue for tongue segmentation. Based on the symmetry geometry of the tongue, a novel level set initialization method is proposed to improve the accuracy of subsequent model evolution. In order to increase the evolution force of the energy function, symmetry detection constraints are added to the evolution model. Combined with the latest convolution neural network, the edge probability input of the tongue image is obtained to guide the evolution of the edge stop function, so as to achieve accurate and automatic tongue segmentation. The experimental results show that the input tongue image is not subject to the external capturing facility or environment, and it is suitable for tongue segmentation under most realistic conditions. Qualitative and quantitative comparisons show that the proposed method is superior to the other methods in terms of robustness and accuracy.

Assessment of automatic segmentation accuracy with various point cloud density

2020 ◽  
Vol 961 (7) ◽  
pp. 47-55
Author(s):  
A.G. Yunusov ◽  
A.J. Jdeed ◽  
N.S. Begliarov ◽  
M.A. Elshewy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Automatic Segmentation ◽  
Geometric Accuracy ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Number Of Segments ◽  
Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

scholarly journals Distinguishing Adenocarcinomas from Granulomas in the CT scan of the chest: performance degradation evaluation in the automatic segmentation framework

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Mahsa Bank Tavakoli ◽  
Mahdi Orooji ◽  
Mehdi Teimouri ◽  
Ramita Shahabifar
Keyword(s):  
State Of The Art ◽  
Automatic Segmentation ◽  
Texture Features ◽  
Standards Of Care ◽  
Shape Features ◽  
Selection Methods ◽  
Segmentation Framework ◽  
Vessel Tortuosity ◽  
Benign Nodules ◽  
Degradation Evaluation

Abstract Objective The most common histopathologic malignant and benign nodules are Adenocarcinoma and Granuloma, respectively, which have different standards of care. In this paper, we propose an automatic framework for the diagnosis of the Adenocarcinomas and the Granulomas in the CT scans of the chest from a private dataset. We use the radiomic features of the nodules and the attached vessel tortuosity for the diagnosis. The private dataset includes 22 CTs for each nodule type, i.e., adenocarcinoma and granuloma. The dataset contains the CTs of the non-smoker patients who are between 30 and 60 years old. To automatically segment the delineated nodule area and the attached vessels area, we apply a morphological-based approach. For distinguishing the malignancy of the segmented nodule, two texture features of the nodule, the curvature Mean and the number of the attached vessels are extracted. Results We compare our framework with the state-of-the-art feature selection methods for differentiating Adenocarcinomas from Granulomas. These methods employ only the shape features of the nodule, the texture features of the nodule, or the torsion features of the attached vessels along with the radiomic features of the nodule. The accuracy of our framework is improved by considering the four selected features.

scholarly journals Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy

2021 ◽  
Author(s):  
Jorge F. Lazo ◽  
Aldo Marzullo ◽  
Sara Moccia ◽  
Michele Catellani ◽  
Benoit Rosa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
State Of The Art ◽  
Automatic Segmentation ◽  
Temporal Information ◽  
Invasive Technique ◽  
Dice Similarity Coefficient ◽  
Specular Reflections ◽  
Lumen Segmentation ◽  
Previous State

Abstract Purpose Ureteroscopy is an efficient endoscopic minimally invasive technique for the diagnosis and treatment of upper tract urothelial carcinoma. During ureteroscopy, the automatic segmentation of the hollow lumen is of primary importance, since it indicates the path that the endoscope should follow. In order to obtain an accurate segmentation of the hollow lumen, this paper presents an automatic method based on convolutional neural networks (CNNs). Methods The proposed method is based on an ensemble of 4 parallel CNNs to simultaneously process single and multi-frame information. Of these, two architectures are taken as core-models, namely U-Net based in residual blocks ($$m_1$$ m 1 ) and Mask-RCNN ($$m_2$$ m 2 ), which are fed with single still-frames I(t). The other two models ($$M_1$$ M 1 , $$M_2$$ M 2 ) are modifications of the former ones consisting on the addition of a stage which makes use of 3D convolutions to process temporal information. $$M_1$$ M 1 , $$M_2$$ M 2 are fed with triplets of frames ($$I(t-1)$$ I ( t - 1 ) , I(t), $$I(t+1)$$ I ( t + 1 ) ) to produce the segmentation for I(t). Results The proposed method was evaluated using a custom dataset of 11 videos (2673 frames) which were collected and manually annotated from 6 patients. We obtain a Dice similarity coefficient of 0.80, outperforming previous state-of-the-art methods. Conclusion The obtained results show that spatial-temporal information can be effectively exploited by the ensemble model to improve hollow lumen segmentation in ureteroscopic images. The method is effective also in the presence of poor visibility, occasional bleeding, or specular reflections.

scholarly journals Photovoltaics Enabling Sustainable Energy Communities: Technological Drivers and Emerging Markets

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1862
Author(s):  
Alexandros-Georgios Chronis ◽  
Foivos Palaiogiannis ◽  
Iasonas Kouveliotis-Lysikatos ◽  
Panos Kotsampopoulos ◽  
Nikos Hatziargyriou
Keyword(s):  
State Of The Art ◽  
Research Question ◽  
Economic Benefits ◽  
Energy Market ◽  
Small Scale ◽  
Local Energy ◽  
Community Members ◽  
Open Research ◽  
Energy Community

In this paper, we investigate the economic benefits of an energy community investing in small-scale photovoltaics (PVs) when local energy trading is operated amongst the community members. The motivation stems from the open research question on whether a community-operated local energy market can enhance the investment feasibility of behind-the-meter small-scale PVs installed by energy community members. Firstly, a review of the models, mechanisms and concepts required for framing the relevant concepts is conducted, while a clarification of nuances at important terms is attempted. Next, a tool for the investigation of the economic benefits of operating a local energy market in the context of an energy community is developed. We design the local energy market using state-of-the-art formulations, modified according to the requirements of the case study. The model is applied to an energy community that is currently under formation in a Greek municipality. From the various simulations that were conducted, a series of generalizable conclusions are extracted.

scholarly journals Non-Invasive Technical Investigation of English Portrait Miniatures Attributed to Nicholas Hilliard and Isaac Oliver

Heritage ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 1165-1181
Author(s):  
Flavia Fiorillo ◽  
Lucia Burgio ◽  
Christine Slottved Kimbriel ◽  
Paola Ricciardi
Keyword(s):  
Fluorescence Spectroscopy ◽  
Scientific Methodology ◽  
Small Scale ◽  
Artistic Practice ◽  
X Ray ◽  
Non Invasive ◽  
Miniature Painting ◽  
Entire Career ◽  
Similarities And Differences ◽  
Victoria And Albert Museum

This study presents the results of the technical investigation carried out on several English portrait miniatures painted in the 16th and 17th century by Nicholas Hilliard and Isaac Oliver, two of the most famous limners working at the Tudor and Stuart courts. The 23 objects chosen for the analysis, spanning almost the entire career of the two artists, belong to the collections of the Victoria and Albert Museum (London) and the Fitzwilliam Museum (Cambridge). A non-invasive scientific methodology, comprising of stereo and optical microscopies, Raman microscopy, and X-ray fluorescence spectroscopy, was required for the investigation of these small-scale and fragile objects. The palettes and working techniques of the two artists were characterised, focusing in particular on the examination of flesh tones, mouths, and eyes. These findings were also compared to the information written in the treatises on miniature painting circulating during the artists’ lifetime. By identifying the materials and techniques most widely employed by the two artists, this study provides information about similarities and differences in their working methods, which can help to understand their artistic practice as well as contribute to matters of attribution.

Pseudo-3D Physical Design Flow for Monolithic 3D ICs: Comparisons and Enhancements

2021 ◽  
Vol 26 (5) ◽  
pp. 1-25
Author(s):  
Heechun Park ◽  
Bon Woong Ku ◽  
Kyungwook Chang ◽  
Da Eun Shim ◽  
Sung Kyu Lim
Keyword(s):  
State Of The Art ◽  
Physical Design ◽  
Design Flow ◽  
Design Tools ◽  
Mixed Design ◽  
Power Performance ◽  
3D Design ◽  
Qualitative And Quantitative ◽  
3D Ics ◽  
Power Delay Product

Studies have shown that monolithic 3D ( M3D ) ICs outperform the existing through-silicon-via ( TSV ) -based 3D ICs in terms of power, performance, and area ( PPA ) metrics, primarily due to the orders of magnitude denser vertical interconnections offered by the nano-scale monolithic inter-tier vias. In order to facilitate faster industry adoption of the M3D technologies, physical design tools and methodologies are essential. Recent academic efforts in developing an EDA algorithm for 3D ICs, mainly targeting placement using TSVs, are inadequate to provide commercial-quality GDS layouts. Lately, pseudo-3D approaches have been devised, which utilize commercial 2D IC EDA engines with tricks that help them operate as an efficient 3D IC CAD tool. In this article, we provide thorough discussions and fair comparisons (both qualitative and quantitative) of the state-of-the-art pseudo-3D design flows, with analysis of limitations in each design flow and solutions to improve their PPA metrics. Moreover, we suggest a hybrid pseudo-3D design flow that achieves both benefits. Our enhancements and the inter-mixed design flow, provide up to an additional 26% wirelength, 10% power consumption, and 23% of power-delay-product improvements.

scholarly journals PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Changyong Li ◽  
Yongxian Fan ◽  
Xiaodong Cai
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
State Of The Art ◽  
Experimental Results ◽  
Actual Situation ◽  
Controlled Experiments ◽  
Biomedical Image ◽  
Segmentation Methods ◽  
Art Performance

Abstract Background With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. Results A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. Conclusions Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.

scholarly journals Nucleation in continuous flow cooling sonocrystallization for coiled capillary crystallizers

2021 ◽  
Author(s):  
Mira Schmalenberg ◽  
Lena K. Weick ◽  
Norbert Kockmann
Keyword(s):  
Time Distribution ◽  
Residence Time Distribution ◽  
Flow Cell ◽  
Deionized Water ◽  
Small Scale ◽  
Non Invasive ◽  
Ultrasonic Bath ◽  
Continuous Crystallization ◽  
Wide Range ◽  
Inner Diameter

AbstractNucleation in continuously operated capillary coiled cooling crystallizers is experimentally investigated under the influence of ultrasound. It was found that there is no sharp boundary but rather a transition zone for nucleation under sonication. For this purpose, a tube with an inner diameter of 1.6 mm and a length of 6 m was winded in a coiled flow inverter (CFI) design and immersed into a cooled ultrasonic bath (37 kHz). The CFI design was chosen for improved radial mixing and narrow residence time distribution, which is also investigated. Amino acid l-alanine dissolved in deionized water is employed in a supersaturation range of 1.10 to 1.46 under quiet and sonicated conditions. Nucleation is non-invasive detected using a flow cell equipped with a microscope and camera. Graphical abstract Since the interest and demand for small-scale, continuous crystallization increases, seed crystals were generated in a coiled tube via sonication and optically investigated and characterized. No distinct threshold for nucleation could be determined in a wide range of supersaturations of l-alanine in water

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