Narodna in univerzitetna knjižnica, Ljubljana (NUK)
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  • Characterization and automatic counting of F.I.S.H. signals in 3-D tissue images
    Umesh Adiga, P. S. ; Knight, Samantha J. L. ; Chaudhuri, B. B.
    The evaluation of malignancy-related features often helps to determine the prognoses for patients with carcinomas. One technique, which is becoming increasingly important for assessing such ... prognostic features is that of Fluorescence in situ Hybridization (FISH). By counting the number of FISH signals in a stack of 2D images of a tumor (which together constitute the 3-D image volume), it is possible to determine whether there has been any loss or gain of the target DNA sequences and thereby evaluate the stage of the disease. However, visual counting of the FISH signals in this way is a tedious, fatiguing and time-consuming task. Therefore, we have developed an automated system for the quantitative evaluation of FISH signals. We present and discuss the implementation of an image processing module that segments, characterizes and counts the FISH signals in 3-D images of thick prostate tumor tissue specimens. Possible errors in the automatic counting of signals are listed and ways to circumvent these errors are described. We define a feature vector for a FISH signal and describe how we have used the weighted feature vector to segment specific signals from noise artifacts. In addition, we present a method, which allows overlapping FISH signals to be distinguishedby fitting a local Gaussian model around the intensity profile and studying the feature vector of each model. Our complete image processing module overcomes the problems of manual counting of FISH signals in 3-D imagesof tumor specimens, thereby providing improved diagnostic and prognosticcapability in qualitative diagnostic pathology.
    Vir: Image analysis & stereology : official journal of the International Society for Stereology. - ISSN 1580-3139 (Vol. 20, no. 1, mar. 2001, str. 41-52)
    Vrsta gradiva - članek, sestavni del
    Leto - 2001
    Jezik - angleški
    COBISS.SI-ID - 16024281

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