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Measurement of Corneal Neovascularisation with the Use of Image Processing Techniques

100%
Murawski K., Białas D., Rękas M.
Acta Physica Polonica A
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2015
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vol. 127
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issue 6
1732-1736
EN
The aim of the article is to present the developed technique to measure the degree of neovascularisation of the cornea image in visible light. The study used an image sensor attached to a slit lamp. The proposed technique consists in performing segmentation of the color image to enhance blood vessels and setting values of the degree of cornea neovascularisation. Comparison of the indicator before and after treatment of the cornea can objectively assess the effectiveness of the adopted method of treatment.
2
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Point Investigation Method for Cancer Changed Tissues

88%
Kłonowski Ł., Kulas Z., Bereś-Pawlik E., Rząca M., Czarnecki R., Grzebieniak Z.
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EN
In this paper we describe the method of point investigation for cancer changed tissues with application of fluorescence phenomenon. The measurements have been made using a specially constructed scanning setup and fiber sensors. The experiment with investigation of endogenous fluorescence has been made on different types of slide tissues (e.g. breast and intestine tumor or precancerous and pathological skin tissues). The obtained spectral characteristics of fluorescence, with typical intensity peaks in 480-520 nm range, have explicitly outlined healthy and pathologically changed areas. The intensity of detected fluorescence determines the evaluation of disease advancement. Moreover, the ability to scan the surface of a tissue sample with constantly moving step of scanning setup in X-Y axis allows us to present the results in a spatial distribution of fluorescence intensity.
3
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Feed Forward Neural Network for Autofluorescence Imaging Classification

88%
Kulas Z., Bereś-Pawlik E., Wierzbicki J.
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issue 6
1189-1193
EN
The key elements in cancer diagnostics are the early identification and estimation of the tumor growth and its spread in order to determine the area to be operated on. The aim of our study was to develop new methods of analyzing autofluorescence images which will allow us an objective and accurate assessment of the location of a tumor and will also be helpful in determining the advancement of the disease. The proposed classification methods are based on neural network algorithms. An Olympus company endoscopic system was used for an autofluorescence intestine imaging study. The autofluorescence imaging analysis process can be divided into several main stages. The first step is preparation of a training data set. The second one involves selection of feature space, namely the selection of those features which enable distinguishing the pathologically altered areas from the healthy ones. Final stages of the analysis include pathologically changed tissue classification and diagnosis.
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