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Implementation of wavelet transform in C++
Valouch, Lukáš ; Hasmanda, Martin (referee) ; Beneš, Radek (advisor)
The aim of this thesis is implementation of wavelet transform algorithm for noise reduction. The noise reduction itself is focused on improving informative capabilities of sonographic (ultrasound) images in medicine. For this purpose, thresholding of detailed coefficients on individual levels of multiresolution analysis was used. Common procedures were not used for searching for the most suitable thresholds of those levels. The alternative concept's design is based on fundamental empirical approach, where the individual thresholds are optimised by evolution algorithms. However, with this algorithmic procedure, more problems manifest regarding the objective evaluation of the success of noise reduction. Because of this, the program uses commonly used parameters such as mean square error of the whole image, linear slope edge approximation, relative contrast of two differently bright and distinct points and the standard deviation of compact surface. Described theoretical knowledge is used in developed application DTWT. It executes multilevel decomposition and reversed reconstruction by discrete time wavelet transform, thresholding of detailed coefficients and final evaluation of performed noise reduction. The developed tool can be used separately to reduce noise. For our purposes, it has been modified in way, that it executed through the component for evolutionary optimization of parameters (Optimize Parameters) in created scenario in RapidMiner program. In the optimization process, this component used evaluation received from DTWT program as fitness function. Optimal thresholds were sought separately for three wavelet families - Daubeschies, Symmlets and Coiflets. The evolution algorithm chose soft threshold for all three wavelet families. In comparison to hard threshold, it is more suitable for noise reduction, but it has tendencies to blur the edges more. The devised method had in most cases greater evaluated success of noise reduction with wavelet transform with threshold search done by evolution algorithms, than commonly used filters. In visual comparison however the wavelet transform introduced some minor depreciating artefacts into the image. It is always about compromise between noise reduction and maximal preservation of image information. Objectively evaluating this dilemma is not easy and is always dependant on subjective viewpoint which in case of sonographic images is that of the attending physician.
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Text separation
Burlak, Vladimír ; Macho, Tomáš (referee) ; Richter, Miloslav (advisor)
This bachelor’s thesis describes basic methods of picture segmentation for the purpose of separation of text from a background. In first part are described general methods meant for picture processing. There is described verging, edge detection and interference reduction. In second part are defined categories of text in picture. Third part is focused on program which will help with picture segmentation.
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Free algebraic structures and their application for segmentation of a digital image
Čambalová, Kateřina ; Solovjovs, Sergejs (referee) ; Pavlík, Jan (advisor)
The thesis covers methods for image segmentation. Fuzzy segmentation is based on the thresholding method. This is generalized to accept multiple criteria. The whole process is mathematically based on the free algebra theory. Free distributive lattice is created from poset of elements based on image properties and the lattice members are represented by terms used by the threshoding. Possible segmentation results compose the equivalence classes distribution. The thesis also contains description of resulting algorithms and methods for their optimization. Also the method of area subtracting is introduced.
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Segmentation in the color fundus imges
Malínský, Miloš ; Jiřík, Radovan (referee) ; Kolář, Radim (advisor)
Optic nerve head and macula are important structures in fundus images. Detection and measurement plays crucial role in several diagnosis methods of optic disease. This work is focused on the detection of the central point of macula and optic nerve head, where the inner border is detected too. There are many methods for extracting this structure in retinal images. Due to the unique properties of each acquisition technique, a single generally acknowledged detection algorithm does not exist. The whole detection process is described from preprocessing through segmentation towards postprocessing. Presented methods are based on the combination of correlation techniques, Hough transform, active contours and morphological operations. The detected contours of the optic nerve head are evaluated and quantitatively compared with the contour drawn by experienced ophthalmologist. The master thesis contains quantity of images that help to describe detection methods.
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Wavelet Filtering of ECG Signals
Staša, Josef ; Kolář, Radim (referee) ; Kozumplík, Jiří (advisor)
The aim of this project is introduction to problems about filtering of signals using wavelet transform. This method is very effective and in present it is often exploited. The main imposition is to use wavelet transform for filtering of ECG signals. Practical part of my project is realized wiener filtering based on DTWT in MATLAB software, to show advantages, disadvantages and compare linear filtering .
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Quality assessment of retinal images
Tvarůžek, Marek ; Jiřík, Radovan (referee) ; Kolář, Radim (advisor)
This thesis deals with the properties of retinal images, which are taken with a digital fundus camera. The basic image processing operations are described and applied on biomedical images. Based on these operations, the algorithm for image quality assessment is designed and implemented in Matlab. The quality metrics quantify the focusing. The proposed methods are tested on two datasets and the results are presented and discussed.
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Segmentation in microscopic images
T.Kovács, Matúš ; Jiřík, Radovan (referee) ; Kolář, Radim (advisor)
This thesis deals with the segmentation of microscopic images from plant sections. It describes the importance of the histogram for obtaining information from the image, and the utilization of the wavelet transformation for the preprocessing of the images. The thesis describes and categorizes different segmentation methods. In the thesis we use MATLAB for the validation of the presented theories and as the interface for creating a software model. The created software application automatically analyzes and evaluates microscopic images.
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Automatic detection of neural fibers losses
Václavek, Martin ; Jiřík, Radovan (referee) ; Kolář, Radim (advisor)
This work is focused on detection of loss in nerve fibre layer on colour pictures of retina, witch are makes by fundus camera. It describe every simple objects of retina, optic nerve head, macula lutea and vascular bed. It detect optic nerve head and his near area, witch is general for detection of breakdownds. It use several metodes of picture adjusting for picture elaboration and objects detection (segmentation, thresholding, enhancement, hough transformation ). The detection of loss in nerve fibre layer is based on comparing of statistic parameters ( average, standart deviation, skewness coefficient and kurtosis coefficient histogram, entropy ) in choosed areas with and withou destruction of nerve layers. Vascular bed have badwatsh on results, cause of this we using hand choosing of essay.
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Applications of wavelet transform in Mathematica and Sage
Novotný, Radek ; Trzos, Michal (referee) ; Rajmic, Pavel (advisor)
This thesis focuses on image processing using wavelet transform. The usage of wavelet transform is analysed especially for image compression and image noise reduction purposes. The analysis describes in detail aspects and application of the following wavelet transform methods: CWT, DWT, DTWT, 2D DWT. The thesis further explains the meaning of the mother wavelet and studies certain specific kinds of wavelets, kinds of thresholding and its purposes and also touches on the JPEG2000 standard. Mathematica and Sage software packages were used to design algorithms for image compression and image noise reduction, utilising relevant wavelet transform findings. The concluding part of the thesis compares the two software packages and results obtained using different algorithms.
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Objects Classification in Images
Gabriel, Petr ; Petyovský, Petr (referee) ; Janáková, Ilona (advisor)
This master's thesis deal with problems of classification objects on the basis of atributes get from images. This thesis pertain to a branch of computer vision. Describe possible instruments of classification (e.g. neural networks, decision tree, etc.). Essential part is description objects by means of atributes. They are imputs to classifier. Practical part of this thesis deal with classification of object collection, which can be usually found at home (e.g. scissors, compact disc, sticky, etc.). Analyzed image is preprocessed , segmented by thresholding in HSV color map. Then defects caused by a segmentation are reconstructed by morfological operations. After are determined atribute values, which are imputs to classifier. Classifier has form of decision tree.
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