Temat: image segmentation - Prolib Integro

Skocz do pozycji: 1.

Tytuł:: Robust and Accurate Iris Segmentation Algorithm for Color and Noisy Eye Images
Autorzy:: Strzelczyk, P.
Tematy:: biometrics
image segmentation
iris recognition; Pokaż więcej
Wydawca:: Instytut Łączności - Państwowy Instytut Badawczy
Powiązania:: https://bibliotekanauki.pl/articles/308523.pdf Link otwiera się w nowym oknie
Opis:: Efficient and robust segmentation of iris images captured in the uncontrolled environments is one of the challenges of non-cooperative iris recognition systems. We address this problem by proposing a novel iris segmentation algorithm, which is suitable both for monochrome and color eye images. The method presented use modified Hough transform to roughly localize the possible iris and pupil boundaries, approximating them by circles. A voting mechanisms is applied to select a candidate iris regions. The detailed iris boundary is approximated by the spline curve. Its shape is determined by minimizing introduced boundary energy function. The described algorithm was submitted to the NICE.I iris image segmentation contest, when it was ranked 11th and 10th out of total 97.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Avoiding spots detection in analysis of electrophoretic gel images
Autorzy:: Bierczyńska-Krzysik, Anna
Walczak, Beata
Daszykowski, Michał
Silberring, Jerzy
Dostawca treści:: Repozytorium Uniwersytetu Jagiellońskiego

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Skocz do pozycji: 3.

Tytuł:: Efektywne algorytmy segmentacji obrazu. Implementacja dla systemu Android
Effective algorithms for image segmentation. Implementation on Android OS
Autorzy:: Wiercioch, Magdalena
Opis:: The thesis presents one of the digital image processing steps, which is segmentation. It discusses the commonly used techniques. The core part is a stand-alone implementation of efficient segmentation algorithms, which theoretical foundations are taken from selected scientific articles. The next step was to improve one method. In addition, there was created the module for performing an interactive image segmentation.
W ramach pracy zajęto się jednym z etapów przetwarzania obrazu, jakim jest segmentacja. Omówione zostały zazwyczaj stosowane techniki. Zasadniczą częścią pracy jest samodzielna implementacja efektywnych metod segmentacji, których podstawy teoretyczne zostały zaczerpnięte z wybranych artykułów naukowych. W dalszym etapie podjęto się próby poprawy jednej z metod. Dodatkowo powstał moduł pozwalający na wykonywanie segmentacji z nadzorem użytkownika.
Dostawca treści:: Repozytorium Uniwersytetu Jagiellońskiego

Inne

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Skocz do pozycji: 4.

Tytuł:: Active contour segmentation of disjoint objects applied to medical images
Autorzy:: Pięta, Ł.
Tomczyk, A.
Szczepaniak, P. S.
Tematy:: segmentacja obrazu
image segmentation
potential active contours; Pokaż więcej
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Powiązania:: https://bibliotekanauki.pl/articles/333451.pdf Link otwiera się w nowym oknie
Opis:: Potential contours are methods for automatic image analysis. In the present paper, potential contours adapted in the supervised way are used for segmentation of disjoint objects and examined using medical images.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Development of an acousto-optic system for hyperspectral image segmentation
Autorzy:: Isaza, César
Mosquera, Julio M.
Gómez-Méndez, Gustavo A.
Zavala-De Paz, Jonny P.
Karina-Anaya, Ely
Rizzo-Sierra, José A.
Palillero-Sandoval, Omar
Tematy:: hyperspectral imaging
acousto-optic system
image segmentation; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/949748.pdf Link otwiera się w nowym oknie
Opis:: Image segmentation is a typical operation in many image analysis and computer vision applications. However, hyperspectral image segmentation is a field which have not been fully investigated. In this study an analogue-digital image segmentation technique is presented. The system uses an acousto-optic tuneable filter, and a CCD camera to capture hyperspectral images that are stored in a digital grey scale format. The dataset was built considering several objects with remarkable differences in the reflectance and brightness components. In addition, the work presents a semi-supervised segmentation technique to deal with the complex problem of hyperspectral image segmentation, with its corresponding quantitative and qualitative evaluation. Particularly, the developed acousto-optic system is capable to acquire 120 frames through the whole visible light spectrum. Moreover, the analysis of the spectral images of a given object enables its segmentation using a simple subtraction operation. Experimental results showed that it is possible to segment any region of interest with a good performance rate by using the proposed analogue-digital segmentation technique.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: Optimal Selection of Image Segmentation Algorithm for Heat-Emitting Objects
Autorzy:: Fabijanska, A
Sankowski, D.
Tematy:: image processing
image segmentation
image quantitative analysis
heat-emitting objects; Pokaż więcej
Wydawca:: Społeczna Akademia Nauk w Łodzi
Powiązania:: https://bibliotekanauki.pl/articles/108774.pdf Link otwiera się w nowym oknie
Opis:: In this paper problem of image segmentation quality is delibered. Especially, issue of segmentation method selection for images presenting heat-emitting objects is discussed. Particular attention is paid to thresholding method and edge-based image segmentation. Results of applying these methods to exemplary images are presented and discussed.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: Automated deep learning-based segmentation of COVID-19 lesions from chest computed tomography images
Autorzy:: Haghparast, Mohammad
Salehi, Mohammad
Ghaffari, Hamed
Ardekani, Mahdieh Afkhami
Taramsari, Alireza Bashari
Opis:: Purpose: The novel coronavirus COVID-19, which spread globally in late December 2019, is a global health crisis. Chest computed tomography (CT) has played a pivotal role in providing useful information for clinicians to detect COVID-19. However, segmenting COVID-19-infected regions from chest CT results is challenging. Therefore, it is desirable to develop an efficient tool for automated segmentation of COVID-19 lesions using chest CT. Hence, we aimed to propose 2D deep-learning algorithms to automatically segment COVID-19-infected regions from chest CT slices and evaluate their performance. Material and methods: Herein, 3 known deep learning networks: U-Net, U-Net++, and Res-Unet, were trained from scratch for automated segmenting of COVID-19 lesions using chest CT images. The dataset consists of 20 labelled COVID-19 chest CT volumes. A total of 2112 images were used. The dataset was split into 80% for training and validation and 20% for testing the proposed models. Segmentation performance was assessed using Dice similarity coefficient, average symmetric surface distance (ASSD), mean absolute error (MAE), sensitivity, specificity, and precision. Results: All proposed models achieved good performance for COVID-19 lesion segmentation. Compared with Res-Unet, the U-Net and U-Net++ models provided better results, with a mean Dice value of 85.0%. Compared with all models, U-Net gained the highest segmentation performance, with 86.0% sensitivity and 2.22 mm ASSD. The U-Net model obtained 1%, 2%, and 0.66 mm improvement over the Res-Unet model in the Dice, sensitivity, and ASSD, respectively. Compared with Res-Unet, U-Net++ achieved 1%, 2%, 0.1 mm, and 0.23 mm improvement in the Dice, sensitivity, ASSD, and MAE, respectively. Conclusions: Our data indicated that the proposed models achieve an average Dice value greater than 84.0%. Two-dimensional deep learning models were able to accurately segment COVID-19 lesions from chest CT images, assisting the radiologists in faster screening and quantification of the lesion regions for further treatment. Nevertheless, further studies will be required to evaluate the clinical performance and robustness of the proposed models for COVID-19 semantic segmentation.
Dostawca treści:: Repozytorium Uniwersytetu Jagiellońskiego

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Skocz do pozycji: 8.

Tytuł:: Applied multiphase level set function in image segmentation
Autorzy:: Rymarczyk, T.
Filipowicz, S.F.
Sikora, J.
Tematy:: image segmentation
level set method
Mumford-Shah functional; Pokaż więcej
Wydawca:: Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Powiązania:: https://bibliotekanauki.pl/articles/97726.pdf Link otwiera się w nowym oknie
Opis:: The application of the level set function for the image segmentation was presented in this paper. The image segmentation refers to the process of partitioning a digital image into multiple regions. There is typically used to locate objects and boundaries in images. The level set method is a powerful tool for representing moving or stationary interfaces. There was used the idea of the variational formulation for geometric active contours. There was used to minimization problem in image processing to compute piecewise-smooth optimal approximations of the given image. The proposed algorithm has been applied to real pictures with promising results in the image segmentation.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: CUDA accelerated Medical Segmentation metrics with MedEval3D
Autorzy:: Mitura, Jakub
Chrapko, Beata E.
Tematy:: CUDA
Computer Tomagraphy
PET/CT
medical image segmentation; Pokaż więcej
Wydawca:: Warszawska Wyższa Szkoła Informatyki
Powiązania:: https://bibliotekanauki.pl/articles/2082265.pdf Link otwiera się w nowym oknie
Opis:: Medical segmentation metrics are crucial for development of correct segmentation algorithms in medical imaging domain. In case of three dimensional large arrays representing studies like CT, PET/CT or MRI of critical importance is availability of library implementing high performance metrics. MedEval3D is created in order to fulfill this need thanks to implementation of CUDA acceleration. Most of implemented metrics like Dice coefficient, Jacard coefficient etc. are based on confusion matrix, what enable effective reuse of calculations across multiple metrics improving performance in such use case. Additionally algorithms like interclass correlation and Mahalanobis distance are also introduced. In both cases their implementations are significantly faster then their counterparts from other available libraries. Lastly programming interface to all of the metrics was created in Julia programming language.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 10.

Tytuł:: Improving Segmentation of 3D Retina Layers Based on Graph Theory Approach for Low Quality OCT Images
Autorzy:: Stankiewicz, A.
Marciniak, T.
Dąbrowski, A.
Stopa, M.
Rakowicz, P.
Marciniak, E.
Tematy:: Optical Coherence Tomography (OCT)
segmentation of retinal layers
image segmentation
graph theory; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/221548.pdf Link otwiera się w nowym oknie
Opis:: This paper presents signal processing aspects for automatic segmentation of retinal layers of the human eye. The paper draws attention to the problems that occur during the computer image processing of images obtained with the use of the Spectral Domain Optical Coherence Tomography (SD OCT). Accuracy of the retinal layer segmentation for a set of typical 3D scans with a rather low quality was shown. Some possible ways to improve quality of the final results are pointed out. The experimental studies were performed using the so-called B-scans obtained with the OCT Copernicus HR device.
Dostawca treści:: Biblioteka Nauki

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