While many feature extraction techniques have been employed, it is still not quite clear which of feature extraction methods should be preferred. Among different available methods, feature-based methods are very dominant. Nabizadeh, Nooshin John, Nigel Kubat, MiroslavĪutomated Magnetic Resonance Imaging brain tumor detection and segmentation is a challenging task. As the result, the highest recognition rate is achieved using Haar, whereas for coefficients cutting for C(i) < 0.1, Haar wavelet has a highest percentage, therefore the retention rate or significan coefficient retained for Haaris lower than other wavelet types (db5, coif3, sym4, and bior2.4)Įfficacy Evaluation of Different Wavelet Feature Extraction Methods on Brain MRI Tumor Detection After finding the recognition rate, some tests are conducted using Energy Compaction for all five types of wavelets above. Comparison analysis is done based on recognition rate percentage with two samples stored in database for reference images. The next step is recognition using normalized Euclidean distance. First, the iris image is segmented from eye image then enhanced with histogram equalization. Some steps have to be done in the research. In the research, iris recognition based on five mentioned wavelets was done and then comparison analysis was conducted for which some conclusions taken.
Wavelet transforms used are Haar, Daubechies, Coiflets, Symlets, and Biorthogonal. One of method is wavelet that extract the image feature based on energy. To identify texture in an image, texture analysis method can be used. Human iris has a very unique pattern which is possible to be used as a biometric recognition. Wavelet Types Comparison for Extracting Iris Feature Based on Energy Compaction
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