Journal articles on the topic 'Image segmentation tools'
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MEZARIS, VASILEIOS, IOANNIS KOMPATSIARIS, and MICHAEL G. STRINTZIS. "STILL IMAGE SEGMENTATION TOOLS FOR OBJECT-BASED MULTIMEDIA APPLICATIONS." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 04 (June 2004): 701–25. http://dx.doi.org/10.1142/s0218001404003393.
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In this paper, a color image segmentation algorithm and an approach to large-format image segmentation are presented, both focused on breaking down images to semantic objects for object-based multimedia applications. The proposed color image segmentation algorithm performs the segmentation in the combined intensity–texture–position feature space in order to produce connected regions that correspond to the real-life objects shown in the image. A preprocessing stage of conditional image filtering and a modified K-Means-with-connectivity-constraint pixel classification algorithm are used to allow for seamless integration of the different pixel features. Unsupervised operation of the segmentation algorithm is enabled by means of an initial clustering procedure. The large-format image segmentation scheme employs the aforementioned segmentation algorithm, providing an elegant framework for the fast segmentation of relatively large images. In this framework, the segmentation algorithm is applied to reduced versions of the original images, in order to speed-up the completion of the segmentation, resulting in a coarse-grained segmentation mask. The final fine-grained segmentation mask is produced with partial reclassification of the pixels of the original image to the already formed regions, using a Bayes classifier. As shown by experimental evaluation, this novel scheme provides fast segmentation with high perceptual segmentation quality.
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Chandra De, Utpal, Madhabananda Das, Debashis Mishra, and Debashis Mishra. "Threshold based brain tumor image segmentation." International Journal of Engineering & Technology 7, no. 3 (August 22, 2018): 1801. http://dx.doi.org/10.14419/ijet.v7i3.12425.
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Image processing is most vital area of research and application in field of medical-imaging. Especially it is a major component in medical science. Starting from radiology to ultrasound (sonography), MRI, etc. in lots of area image is the only source of diagnosis process. Now-a-days, different types of devices are being introduced to capture the internal body parts in medical science to carry the diagnosis process correctly. However, due to various reasons, the captured images need to be tuned digitally to gain the more information. These processes involve noise reduction, segmentations, thresholding etc. . Image segmentation is a process to segment the target area of image to identify the area more prominently. There are different process are evolved to perform the segmentation process, one of which is Image thresholding. Moreover there are different tools are also introduce to perform this step of image thresholding. The recent introduced tool PSO is being used here to segment the MRI scans to identify the brain lesions using image thresholding technique.
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Tippner, Aleš. "Development of segmentation algorithm based region growing for software GIS GRASS." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 58, no. 1 (2010): 207–16. http://dx.doi.org/10.11118/actaun201058010207.
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Image segmentation is fundamental prerequisite for new satellite images interpretation methods. GIS GRASS provides segmentation tools enabling global image segmentation only. We designed procedure enabling local segmentation using existing GRASS tools and segmentation algorithm based on region growing that we developed with C++. This algorithm applies mathematical morphology operators to output segments, too. Principial aim of the project is creation of useful input for differentiation of base land cover classes in panchromatic high-resolution satellite image (or historical aerial photographs for example).
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Stevens, Courtney R., Josh Berenson, Michael Sledziona, Timothy P. Moore, Lynn Dong, and Jonathan Cheetham. "Approach for semi-automated measurement of fiber diameter in murine and canine skeletal muscle." PLOS ONE 15, no. 12 (December 23, 2020): e0243163. http://dx.doi.org/10.1371/journal.pone.0243163.
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Currently available software tools for automated segmentation and analysis of muscle cross-section images often perform poorly in cases of weak or non-uniform staining conditions. To address these issues, our group has developed the MyoSAT (Myofiber Segmentation and Analysis Tool) image-processing pipeline. MyoSAT combines several unconventional approaches including advanced background leveling, Perona-Malik anisotropic diffusion filtering, and Steger’s line detection algorithm to aid in pre-processing and enhancement of the muscle image. Final segmentation is based upon marker-based watershed segmentation. Validation tests using collagen V labeled murine and canine muscle tissue demonstrate that MyoSAT can determine mean muscle fiber diameter with an average accuracy of ~92.4%. The software has been tested to work on full muscle cross-sections and works well even under non-optimal staining conditions. The MyoSAT software tool has been implemented as a macro for the freely available ImageJ software platform. This new segmentation tool allows scientists to efficiently analyze large muscle cross-sections for use in research studies and diagnostics.
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R, Asharani, and Naveen Kumar R. "Review on Brain Tumor Image Segmentation in Time-Frequency Domain." Journal of Image Processing and Artificial Intelligence 8, no. 3 (September 20, 2022): 1–6. http://dx.doi.org/10.46610/joipai.2022.v08i03.001.
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The progressive image segmentation is one of the necessary stages in image acquisition and recognition for an effective identification of brain tumor in advanced medical equipment’s, any image segmentation algorithms working effectively in distinguishing impaired and malignant information from tomographic images through various classification techniques. There is an ambiguity in segmentation for effective regeneration of disseminated information during investigation and extraction of features like shape, volume, and motions of organs from medical images is essential. Current research in medical imaging is aimed at developing automated image recognition and diagnostic systems, which require efficient image segmentation and quantification tools. This paper made an effort to realize the Time-frequency method of image segmentation and reviewing the findings of existing Medical segmentation techniques for medical images.
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He, Bing Song, Feng Zhu, and Yong Gang Shi. "Medical Image Segmentation." Advanced Materials Research 760-762 (September 2013): 1590–93. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1590.
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Medical image plays an important role in the assist doctors in the diagnosis and treatment of diseases. For the medical image, the further analysis and diagnosis of the target area is based on image segmentation. There are many different kinds of image segmentation algorithms. In this paper, image segmentation algorithms are divided into classical image segmentation algorithms and segmentation methods combined with certain mathematical tools, including threshold segmentation methods, image segmentation algorithms based on the edge, image segmentation algorithms based on the region, image segmentation algorithms based on artificial neural network technology, image segmentation algorithms based on contour model and image segmentation algorithm based on statistical major segmentation algorithm and so on. Finally, the development trend of medical image segmentation algorithms is discussed.
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Kang, Yan, Klaus Engelke, and Willi A. Kalender. "Interactive 3D editing tools for image segmentation." Medical Image Analysis 8, no. 1 (March 2004): 35–46. http://dx.doi.org/10.1016/j.media.2003.07.002.
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Atlasov, I. V., L. M. Bozhko, O. Ja Kravets, D. I. Mutin, and D. V. Popov. "Formation of a register of special technology objects based on integrating segmented images." Journal of Physics: Conference Series 2373, no. 2 (December 1, 2022): 022063. http://dx.doi.org/10.1088/1742-6596/2373/2/022063.
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Abstract The article considers aerospace technologies in terms of the details of the space survey of distributed objects. With discrete shooting of areas of interest, a set of images is formed, which are processed by segmentation tools. A set of overlapping or overlapping segments forms objects belonging to the register (areas of interest). The theoretical features of creating mathematical support for the process of forming areas of interest in which objects are present, based on a set of image segments obtained as a result of image processing by segmentation tools, are described. In particular, a mathematical model is presented for creating initial zones from multiple segments of sequential images. The process consists of determining the dynamic neighbourhood, determining the interaction weights and the rules for updating them, updating the spatial vectors of the segments of the processed images. The obtained results provide the creation of mathematical support for the process of forming areas of interest in which objects are present, based on a set of image segments obtained as a result of image processing by segmentation tools.
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Oyebode, Kazeem Oyeyemi. "Leveraging Deep Learning and Grab Cut for Automatic Segmentation of White Blood Cell Images." Journal of Biomimetics, Biomaterials and Biomedical Engineering 58 (August 19, 2022): 121–28. http://dx.doi.org/10.4028/p-oj4d78.
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White blood cell image segmentation provides the opportunity for medical experts to objectively diagnose the medical conditions of patients suffering from Leukemia, for example. Due to the rigorous nature of cell image acquisition (staining process and non-uniform illumination) efficient tools must be deployed to achieve the desired segmentation result. In this paper, a deep learning model is proposed together with a grab cut. The developed deep learning model provides an initial coarse segmentation of white blood cell images. However, the objective of this segmentation is to localize or identify regions of interest from white blood cell images. A bounding is generated from the localized cell image and then used to initiate an automatic cell image segmentation using grab cut. Results of the two publicly available datasets of white blood cell images are considered satisfactory on the proposed model.
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Ta, Vinh-Thong, Olivier Lézoray, Abderrahim Elmoataz, and Sophie Schüpp. "Graph-based tools for microscopic cellular image segmentation." Pattern Recognition 42, no. 6 (June 2009): 1113–25. http://dx.doi.org/10.1016/j.patcog.2008.10.029.
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Qasim, Iqraa. "A Review on Image Segmentation Techniques and Its Recent Applications." International Journal of Computer Science and Mobile Computing 10, no. 9 (September 30, 2021): 98–106. http://dx.doi.org/10.47760/ijcsmc.2021.v10i09.010.
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The technique of attaching a name to each component in a picture so that elements with much the same label have certain visual qualities (pixels, colours, values, patterns, and other features of the image) is known as image segmentation. The output of image segmentation is a series of fragments or patterns taken from the image that collectively cover the full picture. After segmenting an image, classification detects classes in an image on the basic of similarities and differences between detected segments. These two tools together are doing wonders in better object detection in images. Big world has big data, and this big data need robust algorithms for understanding images without a human. This paper summarises the techniques of image segmentation and classification based on their efficiency. Finally, a comparison table shows who performed it better.
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T Zalizam, T. Muda, Abdul Salam Rosalina, and Ismail Suzilah. "Adaptive Hybrid Blood Cell Image Segmentation." MATEC Web of Conferences 255 (2019): 01001. http://dx.doi.org/10.1051/matecconf/201925501001.
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Image segmentation is an important phase in the image recognition system. In medical imaging such as blood cell analysis, it becomes a crucial step in quantitative cytophotometry. Currently, blood cell images become predominantly valuable in medical diagnostics tools. In this paper, we present an adaptive hybrid analysis based on selected segmentation algorithms. Three designates common approaches, that are Fuzzy c-means, K-means and Mean-shift are adapted. Blood cell images that are infected with malaria parasites at various stages were tested. The most suitable method will be selected based on the lowest number of regions. The selected approach will be enhanced by applying Median-cut algorithm to further expand the segmentation process. The proposed adaptive hybrid method has shown a significant improvement in the number of regions.
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CHENG, JIN-CHANG, and HON-SON DON. "SEGMENTATION OF BILEVEL IMAGES USING MATHEMATICAL MORPHOLOGY." International Journal of Pattern Recognition and Artificial Intelligence 06, no. 04 (October 1992): 595–628. http://dx.doi.org/10.1142/s0218001492000321.
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This paper presents the results of a study on the use of morphological skeleton transformation to segment gray-scale images into bilevel images. When a bilevel image (such as printed texts and machine tools) is digitized, the result is a gray-scale image due to the point spread function of digitizer, non-uniform illumination and noise. Our method can recover the original bilevel image from the gray-scale image. The theoretical basis of the algorithm is the physical structure of the skeleton set. A connectivity property of the gray-scale skeleton transformation is used to separate and remove the background terrain. The object pixels can then be obtained by applying a global threshold. Experimental results are given.
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Ge Tu, Wang He Xi, and Bolormaa D. "Research on color image segmentation." Mongolian Journal of Agricultural Sciences 25, no. 03 (December 28, 2018): 138–43. http://dx.doi.org/10.5564/mjas.v25i03.1183.
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The basic foundation for the development of the image processing is image segments. Primary analysis, such as analysis of images and visualization of images, begins with segmentation. Image segmentation is one of the important parts of digital image processing. Depending on the accuracy and accuracy of the segmentation, the results of the image analysis, including the size of the object, the size of the object, and so on. In the first section of this study, briefly describe the types of image segments. Also use Mathlab language's powerful modern programming tools to explore the image segmentation methods and compare the results. As a result of the experiment, it is more accurate to accurately measure the trajectory of the image segmentation of the image as a result of the Otsu-based method of B space. This will apply to further research.
Өнгөний мэдээлэлд суурилсан дүрс сегментчлэх аргын судалгаа
Хураангуй: Дүрс боловсруулах судалгааны ажлын үндсэн суурь нь дүрс сегментчлэл юм. Дүрсэнд анализ хийх, дүрсийг ойлгох зэрэг анхан шатны боловсруулалт нь дүрс сегментчлэхээс эхэлдэг. Дүрс сегментчлэл нь дижитал дүрс боловсруулалтын чухал хэсгүүдийн нэг юм. Сегментчлэлийг хэр зэрэг үнэн зөв, нарийвчлал сайтай хийснээс шалтгаалан, дараагийн дүрс таних, обьектын хэмжээ зэрэг дүрс шинжлэлийн алхамын үр дүн ихээхэн хамаардаг. Энэхүү судалгааны ажлын эхний хэсэгт дүрс сегментчлэх арга төрлүүдийн талаар товч танилцуулна. Мөн орчин үеийн програмчлалын хүчтэй хэрэгсэл болох Mathlab хэлний функцуудыг ашиглан дүрс сегментчилж гарсан үр дүнгийн харьцуулалтыг танилцууллаа. Туршилтын үр дүнд RGB өнгөний орон зайн B бүрэлдэхүүнд суурилсан Otsu-ийн аргийг ашиглан дүрсийг сементчилэх нь уламжлалт дүрс сегментчилэх аргаас нэн сайн үр дүнтай илүү нарийвчлалтай байна. Үүнийг цаашдын судалгааны ажилдаа хэрэглэх болно.
Түлхүүр үг: RGB дүрс, босго (Threshold) утга, гистограм, Otsu-ийн арга, дүрс боловсруулалт
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Jiménez-Lao, R., M. A. Aguilar, C. Ladisa, F. J. Aguilar, and A. Nemmaoui. "MULTIRESOLUTION SEGMENTATION FOR EXTRACTING PLASTIC GREENHOUSES FROM DEIMOS-2 IMAGERY." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2022 (May 17, 2022): 251–58. http://dx.doi.org/10.5194/isprs-annals-v-2-2022-251-2022.
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Abstract. Accurate greenhouse mapping can support environment monitoring and resource management. In an object-based image analysis (OBIA) approach focused on plastic covered greenhouses (PCG) classification, the segmentation is a crucial step for the goodness of the final results. Multiresolution segmentation (MRS) is one of the most used algorithms in OBIA approaches, being greatly enabled by the advent of the commercial software eCognition. Therefore, in addition to the segmentation algorithm used, it is very important to count on tools to assess the quality of segmentation results from digital images in order to obtain the most similar segments to the real PCG objects. In this work, several factors affecting MRS such as the type of input image and the best MRS parameters (i.e., scale, compactness and shape), have been analysed. In this regard, more than 2800 segmentations focused on PCG land cover were conducted from four pre-processed Deimos-2 very high-resolution (VHR) satellite orthoimages taken in the Southeast of Spain (Almería). Specifically, one multispectral and one pansharpened Deimos-2 orthoimages, both with and without atmospheric correction were tested in this work. The free access AssesSeg command line tool, based on a modified version of the supervised discrepancy measure named Euclidean Distance 2 (ED2), was used to determine the best MRS parameters for all the VHR satellite images. According to both the supervised discrepancy measure ED2 and visual perception, the best segmentation on PCG was obtained over the atmospherically corrected pansharpened Deimos-2 orthoimage, achieving very good results.
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Cao, Liying, Hongda Li, Helong Yu, Guifen Chen, and Heshu Wang. "Plant Leaf Segmentation and Phenotypic Analysis Based on Fully Convolutional Neural Network." Applied Engineering in Agriculture 37, no. 5 (2021): 929–40. http://dx.doi.org/10.13031/aea.14495.
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HighlightsModify the U-Net segmentation network to reduce the loss of segmentation accuracy.Reducing the number of layers U-net network, modifying the loss function, and the increase in the output layer dropout.It can be well extracted after splitting blade morphological model and color feature.Abstract. From the perspective of computer vision, the shortcut to extract phenotypic information from a single crop in the field is image segmentation. Plant segmentation is affected by the background environment and illumination. Using deep learning technology to combine depth maps with multi-view images can achieve high-throughput image processing. This article proposes an improved U-Net segmentation network, based on small sample data enhancement, and reconstructs the U-Net model by optimizing the model framework, activation function and loss function. It is used to realize automatic segmentation of plant leaf images and extract relevant feature parameters. Experimental results show that the improved model can provide reliable segmentation results under different leaf sizes, different lighting conditions, different backgrounds, and different plant leaves. The pixel-by-pixel segmentation accuracy reaches 0.94. Compared with traditional methods, this network achieves robust and high-throughput image segmentation. This method is expected to provide key technical support and practical tools for top-view image processing, Unmanned Aerial Vehicle phenotype extraction, and phenotype field platforms. Keywords: Deep learning, Full convolution neural network, Image segmentation, Phenotype analysis, U-Net.
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BALAFAR, M. A., A. B. D. RAHMAN RAMLI, M. IQBAL SARIPAN, SYAMSIAH MASHOHOR, and ROZI MAHMUD. "MEDICAL IMAGE SEGMENTATION USING FUZZY C-MEAN (FCM) AND USER SPECIFIED DATA." Journal of Circuits, Systems and Computers 19, no. 01 (February 2010): 1–14. http://dx.doi.org/10.1142/s0218126610005913.
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Image segmentation is one of the most important parts of clinical diagnostic tools. Medical images mostly contain noise and inhomogeneity. Therefore, accurate segmentation of medical images is a very difficult task. However, the process of accurate segmentation of these images is very important and crucial for a correct diagnosis by clinical tools. We proposed a new clustering method based on Fuzzy C-Mean (FCM) and user specified data. In the postulated method, the color image is converted to grey level image and anisotropic filter is applied to decrease noise; User selects training data for each target class, afterwards, the image is clustered using ordinary FCM. Due to inhomogeneity and unknown noise some clusters contain training data for more than one target class. These clusters are partitioned again. This process continues until there are no such clusters. Then, the clusters contain training data for a target class assigned to that target class; mean of intensity in each class is considered as feature for that class, afterwards, feature distance of each unsigned cluster from different class is found then unsigned clusters are signed to target class with least distance from. Experimental result is demonstrated to show effectiveness of new method.
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Lucas, Alice M., Pearl V. Ryder, Bin Li, Beth A. Cimini, Kevin W. Eliceiri, and Anne E. Carpenter. "Open-source deep-learning software for bioimage segmentation." Molecular Biology of the Cell 32, no. 9 (April 19, 2021): 823–29. http://dx.doi.org/10.1091/mbc.e20-10-0660.
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Microscopy images are rich in information about the dynamic relationships among biological structures. However, extracting this complex information can be challenging, especially when biological structures are closely packed, distinguished by texture rather than intensity, and/or low intensity relative to the background. By learning from large amounts of annotated data, deep learning can accomplish several previously intractable bioimage analysis tasks. Until the past few years, however, most deep-learning workflows required significant computational expertise to be applied. Here, we survey several new open-source software tools that aim to make deep-learning–based image segmentation accessible to biologists with limited computational experience. These tools take many different forms, such as web apps, plug-ins for existing imaging analysis software, and preconfigured interactive notebooks and pipelines. In addition to surveying these tools, we overview several challenges that remain in the field. We hope to expand awareness of the powerful deep-learning tools available to biologists for image analysis.
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Woodward-Greene, M. Jennifer, Jason M. Kinser, Tad S. Sonstegard, Johann Sölkner, Iosif I. Vaisman, and Curtis P. Van Tassell. "PreciseEdge raster RGB image segmentation algorithm reduces user input for livestock digital body measurements highly correlated to real-world measurements." PLOS ONE 17, no. 10 (October 13, 2022): e0275821. http://dx.doi.org/10.1371/journal.pone.0275821.
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Computer vision is a tool that could provide livestock producers with digital body measures and records that are important for animal health and production, namely body height and length, and chest girth. However, to build these tools, the scarcity of labeled training data sets with uniform images (pose, lighting) that also represent real-world livestock can be a challenge. Collecting images in a standard way, with manual image labeling is the gold standard to create such training data, but the time and cost can be prohibitive. We introduce the PreciseEdge image segmentation algorithm to address these issues by employing a standard image collection protocol with a semi-automated image labeling method, and a highly precise image segmentation for automated body measurement extraction directly from each image. These elements, from image collection to extraction are designed to work together to yield values highly correlated to real-world body measurements. PreciseEdge adds a brief preprocessing step inspired by chromakey to a modified GrabCut procedure to generate image masks for data extraction (body measurements) directly from the images. Three hundred RGB (red, green, blue) image samples were collected uniformly per the African Goat Improvement Network Image Collection Protocol (AGIN-ICP), which prescribes camera distance, poses, a blue backdrop, and a custom AGIN-ICP calibration sign. Images were taken in natural settings outdoors and in barns under high and low light, using a Ricoh digital camera producing JPG images (converted to PNG prior to processing). The rear and side AGIN-ICP poses were used for this study. PreciseEdge and GrabCut image segmentation methods were compared for differences in user input required to segment the images. The initial bounding box image output was captured for visual comparison. Automated digital body measurements extracted were compared to manual measures for each method. Both methods allow additional optional refinement (mouse strokes) to aid the segmentation algorithm. These optional mouse strokes were captured automatically and compared. Stroke count distributions for both methods were not normally distributed per Kolmogorov-Smirnov tests. Non-parametric Wilcoxon tests showed the distributions were different (p< 0.001) and the GrabCut stroke count was significantly higher (p = 5.115 e-49), with a mean of 577.08 (std 248.45) versus 221.57 (std 149.45) with PreciseEdge. Digital body measures were highly correlated to manual height, length, and girth measures, (0.931, 0.943, 0.893) for PreciseEdge and (0.936, 0. 944, 0.869) for GrabCut (Pearson correlation coefficient). PreciseEdge image segmentation allowed for masks yielding accurate digital body measurements highly correlated to manual, real-world measurements with over 38% less user input for an efficient, reliable, non-invasive alternative to livestock hand-held direct measuring tools.
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Paul, Tuhin Utsab, Samir Kumar Bandhyopadhyay, and Sayantan Chakraborty. "A New Discrete Wavelet Transform Algorithm Based on Frame Theory and Its Application to Brain MRI Segmentation." Journal of Medical Imaging and Health Informatics 9, no. 2 (February 1, 2019): 223–34. http://dx.doi.org/10.1166/jmihi.2019.2589.
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Automated brain tumor detection from MRI images is a very challenging job in today's modern medical imaging research. MRI is used to take image of soft tissues of human body. It is very helpful for analyzing human organs without surgical intervention. For automatic detection of tumor, segmentation of brain image is required. Segmentation partitions the image into distinct regions based on various parameters. It is the most important and challenging area of computer aided clinical diagnostic tools. Although Conventional segmentation approaches are computationally efficient, but have low quality of edge and feature detection. Here, we propose an algorithm on frame theoretic methods and the discrete wavelet transform and apply it to brain MRIs. Significant gains in performance are observed over conventional segmentation algorithms.
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Barton, David, Felix Hess, Patrick Männle, Sven Odendahl, Marc Stautner, and Jürgen Fleischer. "Image segmentation and robust edge detection for collision avoidance in machine tools." tm - Technisches Messen 88, no. 6 (May 14, 2021): 374–85. http://dx.doi.org/10.1515/teme-2021-0028.
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Abstract Collisions are a major cause of unplanned downtime in small series manufacturing with machine tools. Existing solutions based on geometric simulation do not cover collisions due to setup errors. Therefore a solution is developed to compare camera images of the setup with the simulation, thus detecting discrepancies. The comparison focuses on the product being manufactured (workpiece) and the fixture holding the workpiece, thus the first step consists in segmenting the corresponding region of interest in the image. Subsequently edge detection is applied to the image to extract the relevant contours. Additional processing steps in the spatial and frequency domain are used to alleviate effects of the harsh conditions in the machine, including swarf, fluids and sub-optimal illumination. The comparison of the processed images with the simulation will be presented in a future publication.
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Henke, Michael, Kerstin Neumann, Thomas Altmann, and Evgeny Gladilin. "Semi-Automated Ground Truth Segmentation and Phenotyping of Plant Structures Using k-Means Clustering of Eigen-Colors (kmSeg)." Agriculture 11, no. 11 (November 4, 2021): 1098. http://dx.doi.org/10.3390/agriculture11111098.
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Background. Efficient analysis of large image data produced in greenhouse phenotyping experiments is often challenged by a large variability of optical plant and background appearance which requires advanced classification model methods and reliable ground truth data for their training. In the absence of appropriate computational tools, generation of ground truth data has to be performed manually, which represents a time-consuming task. Methods. Here, we present a efficient GUI-based software solution which reduces the task of plant image segmentation to manual annotation of a small number of image regions automatically pre-segmented using k-means clustering of Eigen-colors (kmSeg). Results. Our experimental results show that in contrast to other supervised clustering techniques k-means enables a computationally efficient pre-segmentation of large plant images in their original resolution. Thereby, the binary segmentation of plant images in fore- and background regions is performed within a few minutes with the average accuracy of 96–99% validated by a direct comparison with ground truth data. Conclusions. Primarily developed for efficient ground truth segmentation and phenotyping of greenhouse-grown plants, the kmSeg tool can be applied for efficient labeling and quantitative analysis of arbitrary images exhibiting distinctive differences between colors of fore- and background structures.
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Gehan, Malia A., Noah Fahlgren, Arash Abbasi, Jeffrey C. Berry, Steven T. Callen, Leonardo Chavez, Andrew N. Doust, et al. "PlantCV v2: Image analysis software for high-throughput plant phenotyping." PeerJ 5 (December 1, 2017): e4088. http://dx.doi.org/10.7717/peerj.4088.
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Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.
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Johnson, Brian Alan, and Lei Ma. "Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities." Remote Sensing 12, no. 11 (June 1, 2020): 1772. http://dx.doi.org/10.3390/rs12111772.
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Image segmentation and geographic object-based image analysis (GEOBIA) were proposed around the turn of the century as a means to analyze high-spatial-resolution remote sensing images. Since then, object-based approaches have been used to analyze a wide range of images for numerous applications. In this Editorial, we present some highlights of image segmentation and GEOBIA research from the last two years (2018–2019), including a Special Issue published in the journal Remote Sensing. As a final contribution of this special issue, we have shared the views of 45 other researchers (corresponding authors of published papers on GEOBIA in 2018–2019) on the current state and future priorities of this field, gathered through an online survey. Most researchers surveyed acknowledged that image segmentation/GEOBIA approaches have achieved a high level of maturity, although the need for more free user-friendly software and tools, further automation, better integration with new machine-learning approaches (including deep learning), and more suitable accuracy assessment methods was frequently pointed out.
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Caicedo, Juan C., Allen Goodman, Kyle W. Karhohs, Beth A. Cimini, Jeanelle Ackerman, Marzieh Haghighi, CherKeng Heng, et al. "Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl." Nature Methods 16, no. 12 (October 21, 2019): 1247–53. http://dx.doi.org/10.1038/s41592-019-0612-7.
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Abstract Segmenting the nuclei of cells in microscopy images is often the first step in the quantitative analysis of imaging data for biological and biomedical applications. Many bioimage analysis tools can segment nuclei in images but need to be selected and configured for every experiment. The 2018 Data Science Bowl attracted 3,891 teams worldwide to make the first attempt to build a segmentation method that could be applied to any two-dimensional light microscopy image of stained nuclei across experiments, with no human interaction. Top participants in the challenge succeeded in this task, developing deep-learning-based models that identified cell nuclei across many image types and experimental conditions without the need to manually adjust segmentation parameters. This represents an important step toward configuration-free bioimage analysis software tools.
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SPINA, THIAGO V., PAULO A. V. DE MIRANDA, and ALEXANDRE X. FALCÃO. "INTELLIGENT UNDERSTANDING OF USER INTERACTION IN IMAGE SEGMENTATION." International Journal of Pattern Recognition and Artificial Intelligence 26, no. 02 (March 2012): 1265001. http://dx.doi.org/10.1142/s0218001412650016.
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We have developed interactive tools for graph-based segmentation of natural images, in which the user guides object delineation by drawing strokes (markers) inside and outside the object. A suitable arc-weight estimation is paramount to minimize user time and maximize segmentation accuracy in these tools. However, it depends on discriminative image properties for object and background. These properties can be obtained from some marker pixels, but their identification is a hard problem during delineation. Careless arc-weight re-estimation reduces user control and drops performance, while interactive arc-weight estimation in a step before interactive object extraction is the best option so far, albeit it is not intuitive for nonexpert users. We present an effective solution using the unified framework of the image foresting transform (IFT) with three operators: clustering for interpreting user interaction and determining when and where arc weights need to be re-estimated; fuzzy classification for arc-weight estimation; and marker competition based on optimum connectivity for object extraction. For validation, we compared the proposed approach with another interactive IFT-based method, which computes arc weights before extraction. Evaluation involved multiple users (experts and nonexperts), a dataset with several natural images, and measurements to quantify accuracy, precision, efficiency (user time and computation time), and user control, being some of them novel measurements, proposed in this work.
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Saifullah, Shoffan, Rabbimov Ilyos Mehriddinovich, and Lean Karlo Tolentino. "Chicken Egg Detection Based-on Image Processing Concept: A Review." Computing and Information Processing Letters 1, no. 1 (November 18, 2021): 31. http://dx.doi.org/10.31315/cip.v1i1.6129.
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The concept of image processing has been implemented and developed in various fields, including the poultry industry. The focus in development is on egg detection. Detection is not only in the concept of object detection but also in other things such as weight prediction, egg physical characteristics, to embryo detection. This staged process starts from the image acquisition process, preprocessing, segmentation up to identifying or detecting eggs. This article provides details about the concept of image processing in detecting chicken eggs based on a review of previous studies. The studies discussed the basic concepts of image processing in detecting chicken eggs and their technical application. Based on image processing’s basic concept, there are four main parts: image acquisition, preprocessing, segmentation, and identification or classification. The acquisition process is carried out with a variety of tools that can capture images to be processed. The result of the acquisition is preprocessed by one or more methods that can improve image quality. After that, the image segmentation process is used to determine the object to be detected. Image segmentation can be used as a reference for objects processed by feature extraction. The feature extraction aims to provide certain fertile (embryonic) characteristics and unfertile (non-embryonic) egg images. The identification process is precise which objects are detected and not. The concept of segmentation and identification/classification can be implemented in computer-based applied applications. Besides, these methods are still developing and improving their accuracy and implementation in the poultry industry.
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BALAFAR, M. A., A. B. D. RAHMAN RAMLI, M. IQBAL SARIPAN, SYAMSIAH MASHOHOR, and ROZI MAHMUD. "IMPROVED FAST FUZZY C-MEAN AND ITS APPLICATION IN MEDICAL IMAGE SEGMENTATION." Journal of Circuits, Systems and Computers 19, no. 01 (February 2010): 203–14. http://dx.doi.org/10.1142/s0218126610006001.
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Image segmentation is a preliminary stage in diagnosis tools and the accurate segmentation of medical images is crucial for a correct diagnosis by these tools. Sometimes, due to inhomogeneity, low contrast, noise and inequality of content with semantic, automatic methods fail to segment image correctly. Therefore, for these images, it is necessary to use user help to correct method's error. We proposed to upgrade FAST FCM method to use training data to have more accurate results. In this paper, instead of using pixels as training data which is usual, we used different gray levels as training data and that is why we have used FAST FCM, because the input of FAST FCM is gray levels exist in image (histogram of the image). We named the new clustering method improved fast fuzzy C-mean (FCM). We use two facts to improve fast FCM. First, training data for each class are the member of the class. Second, the relevance distance of each input data from the training data of a class show the distance of the input data from the class. To cluster an image, first, the color image is converted to gray level image; then, from histogram of image, user selects training data for each target class, afterwards, the image is clustered using postulated clustering method. Experimental result is demonstrated to show effectiveness of the new method.
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Scarinci, I. E., P. Pérez, and M. Valente. "HEURISTIC ALGORITHM FOR PET IMAGES’ SEGMENTATION USING ARTIFICIAL INTELIGENCE TECHNIQUES." Anales AFA 31, no. 4 (January 15, 2021): 165–71. http://dx.doi.org/10.31527/analesafa.2020.31.4.165.
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The overall quantity of nuclear medicine procedures has increased remarkably in recent years, making them a daily tool capable of reaching wide sectors of the population. Regarding the nuclear medicine therapeutic applications, it is worth noting that there is an increasing demand of novel techniques and greater variety of radioisotopes requiring accurate patient-specific dosimetry aimed at evaluating lethal damage to the tumor while maintaining acceptable dose levels in healthy tissues. Image-guided internal dosimetry appears as particularly suitable for theranostics procedures, which allow the joint implementation of diagnose and treatment. In this case, the correct segmentation of the images is critical for the identification of different tissues and organs. On the other hand, modern tools based on data science and artificial intelligence have spread in several fields, particularly in the digital image processing. The use of machine learning models for digital image processing appears as a promising opportunity to complement clinical analysis by experts. This paper reports about an unsupervised segmentation heuristic algorithm using clustering and machine learning techniques together, based on the use of two algorithms: K-Means and HDBSCAN. The results obtained highlight the capacity of automatic segmentation by means of clustering algorithms, becoming a useful tool to assist clinician experts and shorten the segmentation times.
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Hage, Ilige S., and Ramsey F. Hamade. "Statistical and Physical Micro-Feature-Based Segmentation of Cortical Bone Images Using Artificial Intelligence." Materials Science Forum 783-786 (May 2014): 222–27. http://dx.doi.org/10.4028/www.scientific.net/msf.783-786.222.
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At the micro scale, dense cortical bone is structurally comprised mainly of Osteon units that contain Haversian canals, lacunae, and concentric lamellae solid matrix. Osteons are separated from each other by cement lines. These microfeatures of cortical bone are typically captured in digital histological images. In this work, we aim to automatically segment these features utilizing optimized pulse coupled neural networks (PCNN). These networks are artificially intelligent (AI) tools that can model neural activity and produce a series of binary pulses (images) representing the segmentations of an image. Two segmentation methods were used: one statistical and another based on the physical attributes of the microfeatures. The first, statistical-based segmentation method, cost functions based on entropy (probability of gray values) considerations are calculated. For the physical-based segmentation method, cost functions based on geometrical attributes associated with microfeatures such as relative size, shape (i.e., circular or elliptical) are used as targets for the fitness function of network optimization. Both of these methods were found to result in good quality segregation of the microfeatures of micro-images of bovine cortical bone.
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Mandolini, Marco, Agnese Brunzini, Giulia Facco, Alida Mazzoli, Archimede Forcellese, and Antonio Gigante. "Comparison of Three 3D Segmentation Software Tools for Hip Surgical Planning." Sensors 22, no. 14 (July 13, 2022): 5242. http://dx.doi.org/10.3390/s22145242.
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In hip arthroplasty, preoperative planning is fundamental to reaching a successful surgery. Nowadays, several software tools for computed tomography (CT) image processing are available. However, research studies comparing segmentation tools for hip surgery planning for patients affected by osteoarthritic diseases or osteoporotic fractures are still lacking. The present work compares three different software from the geometric, dimensional, and usability perspectives to identify the best three-dimensional (3D) modelling tool for the reconstruction of pathological femoral heads. Syngo.via Frontier (by Siemens Healthcare) is a medical image reading and post-processing software that allows low-skilled operators to produce prototypes. Materialise (by Mimics) is a commercial medical modelling software. 3D Slicer (by slicer.org) is an open-source development platform used in medical and biomedical fields. The 3D models reconstructed starting from the in vivo CT images of the pathological femoral head are compared with the geometries obtained from the laser scan of the in vitro bony specimens. The results show that Mimics and 3D Slicer are better for dimensional and geometric accuracy in the 3D reconstruction, while syngo.via Frontier is the easiest to use in the hospital setting.
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Mata, Christian, Josep Munuera, Alain Lalande, Gilberto Ochoa-Ruiz, and Raul Benitez. "MedicalSeg: A Medical GUI Application for Image Segmentation Management." Algorithms 15, no. 6 (June 8, 2022): 200. http://dx.doi.org/10.3390/a15060200.
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In the field of medical imaging, the division of an image into meaningful structures using image segmentation is an essential step for pre-processing analysis. Many studies have been carried out to solve the general problem of the evaluation of image segmentation results. One of the main focuses in the computer vision field is based on artificial intelligence algorithms for segmentation and classification, including machine learning and deep learning approaches. The main drawback of supervised segmentation approaches is that a large dataset of ground truth validated by medical experts is required. In this sense, many research groups have developed their segmentation approaches according to their specific needs. However, a generalised application aimed at visualizing, assessing and comparing the results of different methods facilitating the generation of a ground-truth repository is not found in recent literature. In this paper, a new graphical user interface application (MedicalSeg) for the management of medical imaging based on pre-processing and segmentation is presented. The objective is twofold, first to create a test platform for comparing segmentation approaches, and secondly to generate segmented images to create ground truths that can then be used for future purposes as artificial intelligence tools. An experimental demonstration and performance analysis discussion are presented in this paper.
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Garcia-Peraza-Herrera, Luis C., Lucas Fidon, Claudia D'Ettorre, Danail Stoyanov, Tom Vercauteren, and Sebastien Ourselin. "Image Compositing for Segmentation of Surgical Tools Without Manual Annotations." IEEE Transactions on Medical Imaging 40, no. 5 (May 2021): 1450–60. http://dx.doi.org/10.1109/tmi.2021.3057884.
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Akbari, Younes, Hanadi Hassen, Somaya Al-Maadeed, and Susu M. Zughaier. "COVID-19 Lesion Segmentation Using Lung CT Scan Images: Comparative Study Based on Active Contour Models." Applied Sciences 11, no. 17 (August 30, 2021): 8039. http://dx.doi.org/10.3390/app11178039.
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Pneumonia is a lung infection that threatens all age groups. In this paper, we use CT scans to investigate the effectiveness of active contour models (ACMs) for segmentation of pneumonia caused by the Coronavirus disease (COVID-19) as one of the successful methods for image segmentation. A comparison has been made between the performances of the state-of-the-art methods performed based on a database of lung CT scan images. This review helps the reader to identify starting points for research in the field of active contour models on COVID-19, which is a high priority for researchers and practitioners. Finally, the experimental results indicate that active contour methods achieve promising results when there are not enough images to use deep learning-based methods as one of the powerful tools for image segmentation.
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Yanni, Rafeek Mamdouh Tawfiq, Nashaat El-Khameesy El-Ghitany, Khaled Amer, Alaa Riad, and Hazem El-Bakry. "A New Model for Image Segmentation Based on Deep Learning." International Journal of Online and Biomedical Engineering (iJOE) 17, no. 07 (July 2, 2021): 28. http://dx.doi.org/10.3991/ijoe.v17i07.21241.
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Image segmentation of the medical image and its conversion into anatomical models is an important technique and main point in computer vision (CV) and image processing (IP), training tools that are used routinely in the fields of medicine and surgery. Segmenting images and converting them into a model that depends on its work on the different algorithms and the extent of technological advancement and method of application. The advancement of segmentation algorithms has led to the possibility of creating three-dimensional models for the patient to study without endangering his life. This paper describes a combination of two fields of solving segmentation problem to convert through the workflow of a hybrid algorithm structure Convolutional neural network (CNN, Active Contour & Deep Multi-Planar) and seg3d2 to switch DICOM medical rays “Digital Imaging and Communications in Medicine” into a 3Dimintional model, using data from active contour to be the input of deep learning. This research will be using are human liver DICOM images and is divided into two stages (medical image segmentation - retinal model optimization). This is to help doctors and surgeons to study the patient’s condition with accuracy and efficiency through the use of mixed reality technology in liver surgery [living donor liver transplantation (LDLT)], all implement by Seg3D2 and Python.
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Prasanthi, B., and Dr N. Nagamalleswararao. "Enhanced and Explored Intuitionistic Rough Based Fuzzy C-means Approach for MR Brain Image Segmentation." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 73. http://dx.doi.org/10.14419/ijet.v7i3.12.15866.
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Segmentation of magnetic resonance images is medically complex and important for study and diagnosis of medical brain images, because of its sensitivity in terms of noise for brain medical images. These are the main issues in classification of brain images. Because of uncertainty & vagueness of brain medical images, so that rough sets, fuzzy sets and Rough sets are mathematical tools evaluate and handle uncertainty and vagueness in medical brain images. Traditionally, different type of fuzzy sets, Rough sets and rough sets based approaches were introduced, they have different several drawbacks with respect to different parameters. So this paper introduces a novel image segmentation calculation method i.e. Enhanced and Explored Intuitionistic Rough based Fuzzy C-means Approach (EEISFCMA) with estimation of weight bias parameter for brain image segmentation. Intuitionistic Rough based fuzzy sets are generalized form of fuzzy, Rough sets and their representative elements are evaluated with non-membership and membership value. Proposed algorithm of this paper consists standard features of existing clustering without spatial weight context data, it defines sensitive of noise in brain images, so that our proposed algorithm deals with intensity and noise reduction of brain image effectively. Furthermore, to reduce iterations in clustering, proposed algorithm initializes cluster centroid based on weight measure using max-dist evaluation method before execution of proposed algorithm. Experimental results of proposed approach carried out efficient image segmentation results compared to existing segmented approaches developed in brain image and other related images. Mainly proposed approach have consists better experimental evaluation based on results.
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Lo, Wen-Chien, Chung-Cheng Chiu, and Jia-Horng Yang. "Three-Dimensional Object Segmentation and Labeling Algorithm Using Contour and Distance Information." Applied Sciences 12, no. 13 (June 29, 2022): 6602. http://dx.doi.org/10.3390/app12136602.
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Object segmentation and object labeling are important techniques in the field of image processing. Because object segmentation techniques developed using two-dimensional images may cause segmentation errors for overlapping objects, this paper proposes a three-dimensional object segmentation and labeling algorithm that combines the segmentation and labeling functions using contour and distance information for static images. The proposed algorithm can segment and label the object without relying on the dynamic information of consecutive images and without obtaining the characteristics of the segmented objects in advance. The algorithm can also effectively segment and label complex overlapping objects and estimate the object’s distance and size according to the labeling contour information. In this paper, a self-made image capture system is developed to capture test images and the actual distance and size of the objects are also measured using measuring tools. The measured data is used as a reference for the estimated data of the proposed algorithm. The experimental results show that the proposed algorithm can effectively segment and label the complex overlapping objects, obtain the estimated distance and size of each object, and satisfy the detection requirements of objects at a long-range in outdoor scenes.
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Taugourdeau, Simon, Mathilde Dionisi, Mylène Lascoste, Matthieu Lesnoff, Jean Marie Capron, Fréderic Borne, Philippe Borianne, and Lionel Julien. "A First Attempt to Combine NIRS and Plenoptic Cameras for the Assessment of Grasslands Functional Diversity and Species Composition." Agriculture 12, no. 5 (May 17, 2022): 704. http://dx.doi.org/10.3390/agriculture12050704.
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Grassland represents more than half of the agricultural land. Numerous metrics (biomass, functional trait, species composition) can be used to describe grassland vegetation and its multiple functions. The measures of these metrics are generally destructive and laborious. Indirect measurements using optical tools are a possible alternative. Some tools have high spatial resolutions (digital camera), and others have high spectral resolutions (Near Infrared Spectrometry NIRS). A plenoptic camera is a multifocal camera that produces clear images at different depths in an image. The objective of this study was to test the interest of combining plenoptic images and NIRS data to characterize different descriptors of two Mediterranean legumes mixtures. On these mixtures, we measured biomass, species biomass, and functional trait diversity. NIRS and plenoptic images were acquired just before the field measurements. The plenoptic images were analyzed using Trainable Weka Segmentation ImageJ to evaluate the percentage of each species in the image. We calculated the average and standard deviation of the different colors (red, green, blue reflectance) in the image. We assessed the percentage of explanation of outputs of the images and NIRS analyses using variance partition and partial least squares. The biomass Trifolium michelianum and Vicia sativa were predicted with more than 50% variability explained. For the other descriptors, the variability explained was lower but nevertheless significant. The percentage variance explained was nevertheless quite low, and further work is required to produce a useable tool, but this work already demonstrates the interest in combining image analysis and NIRS.
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STRZELECKI, MICHAŁ, JACEK KOWALSKI, HYONGSUK KIM, and SOOHONG KO. "A NEW CNN OSCILLATOR MODEL FOR PARALLEL IMAGE SEGMENTATION." International Journal of Bifurcation and Chaos 18, no. 07 (July 2008): 1999–2015. http://dx.doi.org/10.1142/s0218127408021506.
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Segmentation of the textured images into disjoint homogeneous regions is a very important aspect of visual perception. The texture represents properties of visualized objects; it may provide information about their structure. One of the recently developed tools used for texture segmentation is a network of synchronized oscillators. A parallel network operation is based on a "temporary correlation" theory, which attempts to explain scene recognition as performed by the human brain. This theory states that the synchronized oscillations of neuron groups attract attention if it is focused on a coherent stimulus (image object). For more than one perceived stimulus, these synchronized patterns switch in time between different neuron groups, thus forming temporal maps coding several features of the analyzed scene. Consequently, to implement this theory, a new oscillator network was proposed for image segmentation. The segmentation is obtained due to local interactions among neighboring cells. Such a network was successfully used for segmentation of the wide range of different images, including textured and biomedical ones. The network is very suitable for a hardware realization owing to its parallel structure. The realization provides a much faster image segmentation when compared to computer simulation techniques. The paper presents a new mathematical oscillator model suitable to be implemented in a CNN network chip. The model was used to design and simulate a CMOS oscillator circuit, which enables parallel network operation. The proposed oscillator model was analyzed and discussed from the point of view of its computer simulations. Furthermore, it was demonstrated that the oscillator network which implements the presented model is able to perform segmentation of the sample textured images. Oscillator circuit and block diagram of the proposed network chip were also presented and discussed.
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Colebank, Mitchel J., L. Mihaela Paun, M. Umar Qureshi, Naomi Chesler, Dirk Husmeier, Mette S. Olufsen, and Laura Ellwein Fix. "Influence of image segmentation on one-dimensional fluid dynamics predictions in the mouse pulmonary arteries." Journal of The Royal Society Interface 16, no. 159 (October 2, 2019): 20190284. http://dx.doi.org/10.1098/rsif.2019.0284.
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Computational fluid dynamics (CFD) models are emerging tools for assisting in diagnostic assessment of cardiovascular disease. Recent advances in image segmentation have made subject-specific modelling of the cardiovascular system a feasible task, which is particularly important in the case of pulmonary hypertension, requiring a combination of invasive and non-invasive procedures for diagnosis. Uncertainty in image segmentation propagates to CFD model predictions, making the quantification of segmentation-induced uncertainty crucial for subject-specific models. This study quantifies the variability of one-dimensional CFD predictions by propagating the uncertainty of network geometry and connectivity to blood pressure and flow predictions. We analyse multiple segmentations of a single, excised mouse lung using different pre-segmentation parameters. A custom algorithm extracts vessel length, vessel radii and network connectivity for each segmented pulmonary network. Probability density functions are computed for vessel radius and length and then sampled to propagate uncertainties to haemodynamic predictions in a fixed network. In addition, we compute the uncertainty of model predictions to changes in network size and connectivity. Results show that variation in network connectivity is a larger contributor to haemodynamic uncertainty than vessel radius and length.
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Wang, Wenbo, Muhammad Yousaf, Ding Liu, and Ayesha Sohail. "A Comparative Study of the Genetic Deep Learning Image Segmentation Algorithms." Symmetry 14, no. 10 (September 21, 2022): 1977. http://dx.doi.org/10.3390/sym14101977.
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Medical optical imaging, with the aid of the “terahertz tomography”, is a novel medical imaging technique based on the electromagnetic waves. Such advanced imaging techniques strive for the detailed theoretical and computational analysis for better verification and validation. Two important aspects, the analytic approach for the understanding of the Schrodinger transforms and machine learning approaches for the understanding of the medical images segmentation, are presented in this manuscript. While developing an AI algorithm for complex datasets, the computational speed and accuracy cannot be overlooked. With the passage of time, machine learning approaches have been further modified using the Bayesian, genetic and quantum approaches. These strategies have boosted the efficiency of the machine learning, and specifically the deep learning tools, by taking into account the probabilistic, evolutionary and quantum qubits hypothesis and operations, respectively. The current research encompasses the detailed analysis of image segmentation algorithms based on the evolutionary approach. The image segmentation algorithm that converts the color model from RGB to HSI and the image segmentation algorithm that uses the clustering technique are discussed in detail, and further extensions of these genetic algorithms to quantum algorithms are proposed. Based on the genetic algorithm, the optimal selection of parameters is realized so as to achieve a better segmentation effect.
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Carlier, Alexis, Sébastien Dandrifosse, Benjamin Dumont, and Benoît Mercatoris. "Wheat Ear Segmentation Based on a Multisensor System and Superpixel Classification." Plant Phenomics 2022 (January 28, 2022): 1–10. http://dx.doi.org/10.34133/2022/9841985.
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The automatic segmentation of ears in wheat canopy images is an important step to measure ear density or extract relevant plant traits separately for the different organs. Recent deep learning algorithms appear as promising tools to accurately detect ears in a wide diversity of conditions. However, they remain complicated to implement and necessitate a huge training database. This paper is aimed at proposing an easy and quick to train and robust alternative to segment wheat ears from heading to maturity growth stage. The tested method was based on superpixel classification exploiting features from RGB and multispectral cameras. Three classifiers were trained with wheat images acquired from heading to maturity on two cultivars at different levels of fertilizer. The best classifier, the support vector machine (SVM), yielded satisfactory segmentation and reached 94% accuracy. However, the segmentation at the pixel level could not be assessed only by the superpixel classification accuracy. For this reason, a second assessment method was proposed to consider the entire process. A simple graphical tool was developed to annotate pixels. The strategy was to annotate a few pixels per image to be able to quickly annotate the entire image set, and thus account for very diverse conditions. Results showed a lesser segmentation score (F1-score) for the heading and flowering stages and for the zero nitrogen input object. The methodology appeared appropriate for further work on the growth dynamics of the different wheat organs and in the frame of other segmentation challenges.
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Pellis, E., A. Murtiyoso, A. Masiero, G. Tucci, M. Betti, and P. Grussenmeyer. "AN IMAGE-BASED DEEP LEARNING WORKFLOW FOR 3D HERITAGE POINT CLOUD SEMANTIC SEGMENTATION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-2/W1-2022 (February 25, 2022): 429–34. http://dx.doi.org/10.5194/isprs-archives-xlvi-2-w1-2022-429-2022.
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Abstract. The interest in high-resolution semantic 3D models of historical buildings continuously increased during the last decade, thanks to their utility in protection, conservation and restoration of cultural heritage sites. The current generation of surveying tools allows the quick collection of large and detailed amount of data: such data ensure accurate spatial representations of the buildings, but their employment in the creation of informative semantic 3D models is still a challenging task, and it currently still requires manual time-consuming intervention by expert operators. Hence, increasing the level of automation, for instance developing an automatic semantic segmentation procedure enabling machine scene understanding and comprehension, can represent a dramatic improvement in the overall processing procedure. In accordance with this observation, this paper aims at presenting a new workflow for the automatic semantic segmentation of 3D point clouds based on a multi-view approach. Two steps compose this workflow: first, neural network-based semantic segmentation is performed on building images. Then, image labelling is back-projected, through the use of masked images, on the 3D space by exploiting photogrammetry and dense image matching principles. The obtained results are quite promising, with a good performance in the image segmentation, and a remarkable potential in the 3D reconstruction procedure.
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Savareh, Behrouz Alizadeh, Hassan Emami, Mohamadreza Hajiabadi, Seyed Majid Azimi, and Mahyar Ghafoori. "Wavelet-enhanced convolutional neural network: a new idea in a deep learning paradigm." Biomedical Engineering / Biomedizinische Technik 64, no. 2 (April 24, 2019): 195–205. http://dx.doi.org/10.1515/bmt-2017-0178.
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Abstract Purpose: Manual brain tumor segmentation is a challenging task that requires the use of machine learning techniques. One of the machine learning techniques that has been given much attention is the convolutional neural network (CNN). The performance of the CNN can be enhanced by combining other data analysis tools such as wavelet transform. Materials and methods: In this study, one of the famous implementations of CNN, a fully convolutional network (FCN), was used in brain tumor segmentation and its architecture was enhanced by wavelet transform. In this combination, a wavelet transform was used as a complementary and enhancing tool for CNN in brain tumor segmentation. Results: Comparing the performance of basic FCN architecture against the wavelet-enhanced form revealed a remarkable superiority of enhanced architecture in brain tumor segmentation tasks. Conclusion: Using mathematical functions and enhancing tools such as wavelet transform and other mathematical functions can improve the performance of CNN in any image processing task such as segmentation and classification.
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Xue, Zhiyun, L. Rodney Long, Sameer Antani, Leif Neve, Yaoyao Zhu, and George R. Thoma. "A unified set of analysis tools for uterine cervix image segmentation." Computerized Medical Imaging and Graphics 34, no. 8 (December 2010): 593–604. http://dx.doi.org/10.1016/j.compmedimag.2010.04.002.
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Ma, Manzeng, Dan Liu, and Ruirui Zhang. "An Infrared Image Target Segmentation Based on Improved Threshold Method." International Journal of Circuits, Systems and Signal Processing 15 (July 30, 2021): 820–28. http://dx.doi.org/10.46300/9106.2021.15.90.
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In recent years, infrared images have been applied in more and more extensive fields and the current research of infrared image segmentation and recognition can’t satisfy the needs of practical engineering applications. The interference of various factors on infrared detectors result in the targets detected presenting the targets of low contrast, low signal-to-noise ratio (SNR) and fuzzy edges on the infrared image, thus increasing the difficulty of target detection and recognition; therefore, it is the key point to segment the target in an accurate and complete manner when it comes to infrared target detection and recognition and it has great importance and practical value to make in-depth research in this respect. Intelligent algorithms have paved a new way for infrared image segmentation. To achieve target detection, segmentation, recognition and tracking with infrared imaging infrared thermography technology mainly analyzes such features as the grayscale, location and contour information of both background and target of infrared image, segments the target from the background with the help of various tools, extracts the corresponding target features and then proceeds recognition and tracking. To seek the optimal threshold of an image can be seen as to find the optimum value of a confinement problem. As to seek the threshold requires much computation, to seek the threshold through intelligent algorithms is more accurate. This paper proposes an automatic segmentation method for infrared target image based on differential evolution (DE) algorithm and OTSU. This proposed method not only takes into consideration the grayscale information of the image, but also pays attention to the relevant information of neighborhood space to facilitate more accurate image segmentation. After determining the scope of the optimal threshold, it integrates DE’s ability of globally searching the optimal solution. This method can lower the operation time and improve the segmentation efficiency. The simulation experiment proves that this method is very effective.
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Zimeras, S., and L. G. Gortzis. "Interactive Tele-Radiological Segmentation Systems for Treatment and Diagnosis." International Journal of Telemedicine and Applications 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/713739.
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Telehealth is the exchange of health information and the provision of health care services through electronic information and communications technology, where participants are separated by geographic, time, social and cultural barriers. The shift of telemedicine from desktop platforms to wireless and mobile technologies is likely to have a significant impact on healthcare in the future. It is therefore crucial to develop a general information exchange e-medical system to enables its users to perform online and offline medical consultations through diagnosis. During the medical diagnosis, image analysis techniques combined with doctor’s opinions could be useful for final medical decisions. Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. In medical images, segmentation has traditionally been done by human experts. Even with the aid of image processing software (computer-assisted segmentation tools), manual segmentation of 2D and 3D CT images is tedious, time-consuming, and thus impractical, especially in cases where a large number of objects must be specified. Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered. Therefore automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use. The main purpose of this work is to analyze segmentation techniques for the definition of anatomical structures under telemedical systems.
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Felfoldi, J., L. Baranyai, F. Firtha, L. Friedrich, and Cs Balla. "Image processing based method for characterization of the fat/meat ratio and fat distribution of pork and beef samples." Progress in Agricultural Engineering Sciences 9, no. 1 (December 1, 2013): 27–53. http://dx.doi.org/10.1556/progress.9.2013.2.
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The fat content (fat distribution) of the pork and beef raw material is one of their most important quality characteristics. Image processing methods were applied to provide with quantitative parameters related to these properties. Different hardware tools were tested to select the appropriate imaging alternative. Statistical analysis of the RGB data was performed in order to find appropriate classification function for segmentation. Discriminant analysis of the RGB data of selected image regions (fat-meat-background) resulted in a good segmentation of the fat regions. Classification function was applied on the RGB images of the samples, to identify and measure the regions in question. The fat-meat ratio and textural parameters (entropy, contrast, etc.) were determined. Comparison of the image parameters with the sensory evaluation results showed an encouraging correlation.
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Nayak, U. P., M. Müller, D. Britz, M. A. Guitar, and F. Mücklich. "Image Processing using Open Source Tools and their Implementation in the Analysis of Complex Microstructures." Practical Metallography 58, no. 8 (August 1, 2021): 484–506. http://dx.doi.org/10.1515/pm-2021-0039.
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Abstract Considering the dependance of materials’ properties on the microstructure, it is imperative to carry out a thorough microstructural characterization and analysis to bolster its development. This article is aimed to inform the users about the implementation of FIJI, an open source image processing software for image segmentation and quantitative microstructural analysis. The rapid advancement of computer technology in the past years has made it possible to swiftly segment and analyze hundreds of micrographs reducing hours’ worth of analysis time to a mere matter of minutes. This has led to the availability of several commercial image processing software programs primarily aimed at relatively inexperienced users. Despite the advantages like ‘one-click solutions’ offered by commercial software, the high licensing cost limits its widespread use in the metallographic community. Open-source platforms on the other hand, are free and easily available although rudimentary knowledge of the user-interface is a pre-requisite. In particular, the software FIJI has distinguished itself as a versatile tool, since it provides suitable extensions from image processing to segmentation to quantitative stereology and is continuously developed by a large user community. This article aims to introduce the FIJI program by familiarizing the user with its graphical user-interface and providing a sequential methodology to carry out image segmentation and quantitative microstructural analysis.
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Tzavara, Nefeli Panagiota, and Bjørn-Jostein Singstad. "Transfer Learning in Polyp and Endoscopic Tool Segmentation from Colonoscopy Images." Nordic Machine Intelligence 1, no. 1 (November 1, 2021): 32–34. http://dx.doi.org/10.5617/nmi.9132.
Full textAbstract:
Colorectal cancer is one of the deadliest and most widespread types of cancer in the world. Colonoscopy is the procedure used to detect and diagnose polyps from the colon, but today's detection rate shows a significant error rate that affects diagnosis and treatment. An automatic image segmentation algorithm may help doctors to improve the detection rate of pathological polyps in the colon. Furthermore, segmenting endoscopic tools in images taken during colonoscopy may contribute towards robotic assisted surgery. In this study, we trained and validated both pre-trained and not pre-trained segmentation models on two different data sets, containing images of polyps and endoscopic tools. Finally, we applied the models on two separate test sets and the best polyp model got a dice score 0.857 and the test instrument model got a dice score 0.948. Moreover, we found that pre-training of the models increased the performance in segmenting polyps and endoscopic tools.