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Automatic detection of microcalcifications in mammogram images
Hývlová, Denisa ; Jakubíček, Roman (referee) ; Harabiš, Vratislav (advisor)
This bachelor thesis is focused on detection of microcalcification in mammography images. The introduction describes connection between their presence and breast cancer, principle of mammography and the DICOM standard used in radiology. In the following part the methods used for microcalcification enhancement and segmentation are explained. Detection algorithm based on wavelet transform, morphological closing and thresholding was designed in MATLAB. For evaluation of the results a graphical user interface was developed and an algorithm for automatic evaluation of the success rate in annotated mammography database was implemented.
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Automatic detection of microcalcifications in mammogram images
Hývlová, Denisa ; Jakubíček, Roman (referee) ; Harabiš, Vratislav (advisor)
This bachelor thesis is focused on detection of microcalcification in mammography images. The introduction describes connection between their presence and breast cancer, principle of mammography and the DICOM standard used in radiology. In the following part the methods used for microcalcification enhancement and segmentation are explained. Detection algorithm based on wavelet transform, morphological closing and thresholding was designed in MATLAB. For evaluation of the results a graphical user interface was developed and an algorithm for automatic evaluation of the success rate in annotated mammography database was implemented.
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Texture modeling applied to medical images
Remeš, Václav ; Haindl, Michal (advisor)
and contributions This thesis presents novel descriptive multidimensional Markovian textural models applied to computer aided diagnosis in the field of X-ray mammogra- phy. These general mathematical models, applicable in wide areas of texture modeling outside X-ray mammography as well, provide ideal visual verification using synthesis of the corresponding measured data spaces, contrary to stan- dard discriminative models. All achieved results in the thesis are extensively benchmarked. The thesis presents two methods for breast density classification in X-ray mammography. The methods were tested on the widely known MIAS database and the state-of-the art INbreast database, with competitive results. Several methods for completely automatic mammogram texture enhance- ment are presented. These methods are based on the descriptive textural mod- els developed in the thesis which automatically adapt to the analyzed X-ray texture, thus being universal for any type of input without the need of further manual tuning of specific parameters. The methods' outputs highlight regions of interest, detected as textural abnormalities. The methods provide the pos- sibility of enhancement tuned to specific types of mammogram tissue. Hence, the enhanced mammograms can help radiologists to decrease their false negative...
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Texture modeling applied to medical images
Remeš, Václav ; Haindl, Michal (advisor)
and contributions This thesis presents novel descriptive multidimensional Markovian textural models applied to computer aided diagnosis in the field of X-ray mammogra- phy. These general mathematical models, applicable in wide areas of texture modeling outside X-ray mammography as well, provide ideal visual verification using synthesis of the corresponding measured data spaces, contrary to stan- dard discriminative models. All achieved results in the thesis are extensively benchmarked. The thesis presents two methods for breast density classification in X-ray mammography. The methods were tested on the widely known MIAS database and the state-of-the art INbreast database, with competitive results. Several methods for completely automatic mammogram texture enhance- ment are presented. These methods are based on the descriptive textural mod- els developed in the thesis which automatically adapt to the analyzed X-ray texture, thus being universal for any type of input without the need of further manual tuning of specific parameters. The methods' outputs highlight regions of interest, detected as textural abnormalities. The methods provide the pos- sibility of enhancement tuned to specific types of mammogram tissue. Hence, the enhanced mammograms can help radiologists to decrease their false negative...
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