Abstract
Medical ultrasound is a type of imaging that uses high-frequency sound waves to create images of body parts. A transducer, which creates high-frequency sound waves that flow through the bodily tissues, measures the dimensions, shape, and consistency of soft tissues and organs. The sound waves are reflected from the body tissues to vibrate the transducer which has piezoelectric material. The piezoelectric material converts the sound waves to electrical pulses that travel to the ultrasonic scanner where the electrical signal is amplified and processed to form a digital image in real-time. These images can be useful in detecting and treating a wide range of diseases and ailments.
The main disadvantage of ultrasound imaging is the addition of noise during the signal processing stage which can result in images that can be difficult to interpret. Different kinds of noise are introduced during the acquisition or transmission of the image. Ultrasound images are mostly affected by speckle noise. Speckle noise is a random creation of multiple tiny dots in an image and is produced when sound waves randomly interfere with tiny particles on a scale equal to the sound wavelength. The quality of the image is reduced by speckle noise, which limits the ability of human observation to form judgments based on the diagnostic examination. Speckle noise reduces contrast in an image, making it challenging to execute subsequent image processing operations like segmentation and edge detection.
This research dissertation provides a comprehensive study on the removal of speckle noise in ultrasound images. Different techniques to remove speckle noise in ultrasound images is discussed. Multiple experiments are done using single filters, hybrid filters and deep learning algorithms. The experimental results lead to the conclusion that deep learning algorithms excelled, particularly when confronted with high speckle variances. Specifically, among the deep learning algorithms, DnCNNL5 exhibited the most favorable performance under high speckle variances. However, it is important to acknowledge that deep learning algorithms are disadvantaged by longer processing times.
Hybrid filters emerged as the second-best performers in terms of speckle noise removal, accompanied by the second-best processing time. These hybrid filters showed effective noise reduction capabilities due to their combination of the best performing individual filters. This result was as expected, considering that hybrid filters, constructed by amalgamating the best individual filters, were anticipated to perform better than single filters in terms of denoising efficacy. On the other hand, single filter algorithms demonstrated the shortest processing time but ranked last in terms of removing speckle noise.