Biomedical image decomposition and segmentation by using signal expansion methods

Signal processing leads to research on many subjects. One of them is the biomedical field. Biomedical signal processing has played an important role in the detection process of many diseases in recent years. Different methods are used for purposes. Segmentation, separation, filtering, analysis are the leading methods. In this thesis, various signal expansion methods have been used for the decomposition and segmentation of biomedical images. Wavelet and adaptive filter banks-based methods have been used on medical images. Filter design with different optimization methods. These designed filters have been used to decompose and reconstruct images. The performances of the designed methods and classical methods were examined by comparing.