Evaluation of Speckle Noise Reduction Filters and Machine Learning Algorithms for Ultrasound Images

Medical ultrasound imaging involves the use of high-frequency sound waves to produce images of various body parts. A transducer generates these sound waves, which traverse through bodily tissues, providing measurements of soft tissue and organ dimensions, shapes, and consistencies. The quality of an ultrasound image depends on the frequency of the transducer used. Higher-frequency transducers yield better resolution, but they are limited in their ability to penetrate deeply into the body due to their shorter wavelengths, making them more susceptible to absorption. Lower-frequency transducers can penetrate deeper because they have longer wavelengths, although their resolution isn't as sharp as that of high-frequency transducers. The primary drawback associated with ultrasound imaging is the introduction of noise during the signal processing stage, which can lead to images that are challenging to interpret. Efficient medical image processing plays a pivotal role in improving the quality and utility of ultrasound imaging for medical applications, enhancing image comprehension, and aiding in accurate diagnoses. One critical aspect of the pre-processing stage in medical image analysis, particularly in ultrasound images, is speckle noise reduction. To improve the analysis and diagnosis in various applications, it has become essential to employ software tools for speckle noise removal. In this paper we propose hybrid filter, which is combination of filters to be used to remove noise along with Convolutional Neural Networks. Experiments were performed at different speckle variances and the results showed that hybrid filter performed well when the speckle variance is between 0.1 and 0.3, while DnCNNL5 performed well when the speckle variance was high (between 0.5 and 0.9).