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    Wyświetlanie 1-8 z 8
    Tytuł:
    Projection-based text line segmentation with a variable threshold
    Autorzy:
    Ptak, R.
    Żygadło, B.
    Unold, O.
    Powiązania:
    https://bibliotekanauki.pl/articles/329884.pdf
    Data publikacji:
    2017
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    document image processing
    handwritten text
    text line segmentation
    projection profile
    offline cursive script recognition
    przetwarzanie obrazu dokumentu
    tekst odręczny
    segmentacja linii tekstu
    profil projekcyjny
    Opis:
    Document image segmentation into text lines is one of the stages in unconstrained handwritten document recognition. This paper presents a new algorithm for text line separation in handwriting. The developed algorithm is based on a method using the projection profile. It employs thresholding, but the threshold value is variable. This permits determination of low or overlapping peaks of the graph. The proposed technique is shown to improve the recognition rate relative to traditional methods. The algorithm is robust in text line detection with respect to different text line lengths.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2017, 27, 1; 195-206
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Breast cancer nuclei segmentation and classification based on a deep learning approach
    Autorzy:
    Kowal, Marek
    Skobel, Marcin
    Gramacki, Artur
    Korbicz, Józef
    Powiązania:
    https://bibliotekanauki.pl/articles/1838197.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    breast cancer
    nuclei segmentation
    image processing
    nowotwór piersi
    segmentacja jądra
    przetwarzanie obrazu
    Opis:
    One of the most popular methods in the diagnosis of breast cancer is fine-needle biopsy without aspiration. Cell nuclei are the most important elements of cancer diagnostics based on cytological images. Therefore, the first step of successful classification of cytological images is effective automatic segmentation of cell nuclei. The aims of our study include (a) development of segmentation methods of cell nuclei based on deep learning techniques, (b) extraction of some morphometric, colorimetric and textural features of individual segmented nuclei, (c) based on the extracted features, construction of effective classifiers for detecting malignant or benign cases. The segmentation methods used in this paper are based on (a) fully convolutional neural networks and (b) the marker-controlled watershed algorithm. For the classification task, seven various classification methods are used. Cell nuclei segmentation achieves 90% accuracy for benign and 86% for malignant nuclei according to the F-score. The maximum accuracy of the classification reached 80.2% to 92.4%, depending on the type (malignant or benign) of cell nuclei. The classification of tumors based on cytological images is an extremely challenging task. However, the obtained results are promising, and it is possible to state that automatic diagnostic methods are competitive to manual ones.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2021, 31, 1; 85-106
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neural network segmentation of images from stained cucurbits leaves with colour symptoms of biotic and abiotic stresses
    Autorzy:
    Gocławski, J.
    Sekulska-Nalewajko, J.
    Kuźniak, E.
    Powiązania:
    https://bibliotekanauki.pl/articles/330961.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    segmentacja obrazu
    przestrzeń koloru
    przetwarzanie morfologiczne
    progowanie obrazu
    sztuczna sieć neuronowa
    ochrona roślin
    image segmentation
    colour space
    morphological processing
    image thresholding
    artificial neural network
    WTA learning
    Widrow-Hoff learning
    Cucurbita species
    plant stress
    ROS detection
    Opis:
    The increased production of Reactive Oxygen Species (ROS) in plant leaf tissues is a hallmark of a plant's reaction to various environmental stresses. This paper describes an automatic segmentation method for scanned images of cucurbits leaves stained to visualise ROS accumulation sites featured by specific colour hues and intensities. The leaves placed separately in the scanner view field on a colour background are extracted by thresholding in the RGB colour space, then cleaned from petioles to obtain a leaf blade mask. The second stage of the method consists in the classification of within mask pixels in a hue-saturation plane using two classes, determined by leaf regions with and without colour products of the ROS reaction. At this stage a two-layer, hybrid artificial neural network is applied with the first layer as a self-organising Kohonen type network and a linear perceptron output layer (counter propagation network type). The WTA-based, fast competitive learning of the first layer was improved to increase clustering reliability. Widrow-Hoff supervised training used at the output layer utilises manually labelled patterns prepared from training images. The generalisation ability of the network model has been verified by K-fold cross-validation. The method significantly accelerates the measurement of leaf regions containing the ROS reaction colour products and improves measurement accuracy.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2012, 22, 3; 669-684
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Implementation and evaluation of medical imaging techniques based on conformal geometric algebra
    Autorzy:
    Franchini, Silvia
    Gentile, Antonio
    Vassallo, Giorgio
    Vitabile, Salvatore
    Powiązania:
    https://bibliotekanauki.pl/articles/329970.pdf
    Data publikacji:
    2020
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    medical image segmentation
    medical image registration
    computational geometry
    Clifford algebra
    conformal geometric algebra
    segmentacja obrazu
    rejestracja obrazu medycznego
    geometria obliczeniowa
    algebra Clifforda
    Opis:
    Medical imaging tasks, such as segmentation, 3D modeling, and registration of medical images, involve complex geometric problems, usually solved by standard linear algebra and matrix calculations. In the last few decades, conformal geometric algebra (CGA) has emerged as a new approach to geometric computing that offers a simple and efficient representation of geometric objects and transformations. However, the practical use of CGA-based methods for big data image processing in medical imaging requires fast and efficient implementations of CGA operations to meet both real-time processing constraints and accuracy requirements. The purpose of this study is to present a novel implementation of CGA-based medical imaging techniques that makes them effective and practically usable. The paper exploits a new simplified formulation of CGA operators that allows significantly reduced execution times while maintaining the needed result precision. We have exploited this novel CGA formulation to re-design a suite of medical imaging automatic methods, including image segmentation, 3D reconstruction and registration. Experimental tests show that the re-formulated CGA-based methods lead to both higher precision results and reduced computation times, which makes them suitable for big data image processing applications. The segmentation algorithm provides the Dice index, sensitivity and specificity values of 98.14%, 98.05% and 97.73%, respectively, while the order of magnitude of the errors measured for the registration methods is 10-5.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2020, 30, 3; 415-433
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    KHM clustering technique as a segmentation method for endoscopic colour images
    Autorzy:
    Frąckiewicz, M.
    Palus, H.
    Powiązania:
    https://bibliotekanauki.pl/articles/907830.pdf
    Data publikacji:
    2011
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    segmentacja obrazu
    obraz barwny
    biomedycyna
    biomedical colour image segmentation
    k-harmonic means technique
    kappa-means technique
    Opis:
    In this paper, the idea of applying the k-harmonic means (KHM) technique in biomedical colour image segmentation is presented. The k-means (KM) technique establishes a background for the comparison of clustering techniques. Two original initialization methods for both clustering techniques and two evaluation functions are described. The proposed method of colour image segmentation is completed by a postprocessing procedure. Experimental tests realized on real endoscopic colour images show the superiority of KHM over KM.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2011, 21, 1; 203-209
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Assessment of hydrocephalus in children based on digital image processing and analysis
    Autorzy:
    Fabijańska, A.
    Węgliński, T.
    Zakrzewski, K.
    Nowosławska, E.
    Powiązania:
    https://bibliotekanauki.pl/articles/330914.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    hydrocephalus
    computed tomography
    image segmentation
    Evans index
    frontal horn ratio
    occipital horn ratio
    ventricular angle
    frontal horn radius
    wodogłowie
    tomografia komputerowa
    segmentacja obrazu
    Opis:
    Hydrocephalus is a pathological condition of the central nervous system which often affects neonates and young children. It manifests itself as an abnormal accumulation of cerebrospinal fluid within the ventricular system of the brain with its subsequent progression. One of the most important diagnostic methods of identifying hydrocephalus is Computer Tomography (CT). The enlarged ventricular system is clearly visible on CT scans. However, the assessment of the disease progress usually relies on the radiologist’s judgment and manual measurements, which are subjective, cumbersome and have limited accuracy. Therefore, this paper regards the problem of semi-automatic assessment of hydrocephalus using image processing and analysis algorithms. In particular, automated determination of popular indices of the disease progress is considered. Algorithms for the detection, semi-automatic segmentation and numerical description of the lesion are proposed. Specifically, the disease progress is determined using shape analysis algorithms. Numerical results provided by the introduced methods are presented and compared with those calculated manually by a radiologist and a trained operator. The comparison proves the correctness of the introduced approach.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2014, 24, 2; 299-312
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Closest paths in graph drawings under an elastic metric
    Autorzy:
    Baran, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/330666.pdf
    Data publikacji:
    2018
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    elastic shape analysis
    pattern recognition
    superpixel segmentation
    analiza kształtu
    rozpoznawanie wzorca
    segmentacja obrazu
    superpiksel
    Opis:
    This work extends the dynamic programming approach to calculation of an elastic metric between two curves to finding paths in pairs of graph drawings that are closest under this metric. The new algorithm effectively solves this problem when all paths between two given nodes in one of these graphs have the same length. It is then applied to the problem of pattern recognition constrained by a superpixel segmentation. Segmentations of test images, obtained without statistical modeling given two shape endpoints, have good accuracy.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2018, 28, 2; 387-397
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Line segmentation of handwritten text using histograms and tensor voting
    Autorzy:
    Babczyński, Tomasz
    Ptak, Roman
    Powiązania:
    https://bibliotekanauki.pl/articles/330796.pdf
    Data publikacji:
    2020
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    document image processing
    handwritten text
    text line segmentation
    projection profile
    text string
    offline cursive script recognition
    ICDAR 2009 competition
    przetwarzanie obrazu dokumentu
    tekst odręczny
    segmentacja linii tekstu
    profil projekcyjny
    ciąg tekstowy
    Opis:
    There are a large number of historical documents in libraries and other archives throughout the world. Most of them are written by hand. In many cases they exist in only one specimen and are hard to reach. Digitization of such artifacts can make them available to the community. But even digitized, they remain unsearchable, and an important task is to draw the contents in the computer readable form. One of the first steps in this direction is to recognize where the lines of the text are. Computational intelligence algorithms can be used to solve this problem. In the present paper, two groups of algorithms, namely, projection-based and tensor voting-based, are compared. The performance is evaluated on a data set and with the procedure proposed by the organizers of the ICDAR 2009 competition.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2020, 30, 3; 585-596
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-8 z 8

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