The empirical mode decomposition and the discrete wavelet transform for detection of human cataract in ultrasound signals
This paper presents a new approach for human cataract automatical detection based on ultrasound signal processing. Two signal decomposition techniques, empirical mode decomposition and discrete wavelet transform are used in the presented method. Performance comparison of these two decomposition methods when applied to this specific ultrasound signal is given. Described method includes ultrasonic signal decomposition to enhance signal specific features and increase signal to noise ratio with the following decision rules based on adaptive thresholding. The resulting detection performance of the proposed method using empirical mode decomposition was better to compare to discrete wavelet transform and resulted in 70% correctly identified "healthy subject" cases and 82%, 97% and 100% correctly identified "cataract cases" in the incipience, immature and mature cataract subject groups, respectively. Discussion is given on the reasons of different results and the differences between the two used signal decomposition techniques.
empirical mode decomposition discrete wavelet transform human cataract ultrasound biomedical signal processing clinical decision support