How can you use image processing to improve ultrasound images?

2 Image segmentation

Image segmentation is the process of dividing an image into meaningful regions or objects, such as organs, tumors, vessels, and lesions in ultrasound images. Common image segmentation techniques include thresholding, region growing, clustering, and active contours. Thresholding methods use a predefined or adaptive value to separate the pixels into foreground and background. Region growing starts from a seed pixel or region and expands it by adding neighboring pixels that meet certain criteria. Clustering groups the pixels into clusters based on their similarity or dissimilarity in terms of features. Active contours use curves or surfaces that deform according to internal and external forces to fit the boundaries of the objects. Each technique has its own advantages and disadvantages; for example, thresholding is simple and fast but may not work well for images with low contrast or complex shapes, while active contours can adapt to different shapes and sizes but may be computationally expensive and sensitive to initialization and parameters.

3 Image registration

Image registration is the process of aligning two or more images of the same or different modalities, taken at different times, from different viewpoints, or under different conditions. For ultrasound images, image registration can help compare, fuse, or track the images for applications such as multimodal imaging, image-guided surgery, and motion analysis. There are three common image registration techniques for ultrasound images: feature-based, intensity-based, and hybrid. Feature-based methods extract and match salient features from the images to estimate the transformation that aligns them. Intensity-based methods use the pixel values or statistics of the images to measure the similarity or dissimilarity between the images and optimize the transformation that maximizes or minimizes it. Hybrid methods combine the advantages of feature-based and intensity-based methods by using features to initialize or guide the intensity-based optimization. All three techniques have their own set of advantages and disadvantages; however, hybrid methods can achieve high speed and accuracy with more complex implementation and integration.

4 Image analysis

Image analysis is the process of extracting useful information or knowledge from an image. For ultrasound images, this can help quantify and characterize the properties and functions of the structures in the image, such as shape, size, texture, elasticity, blood flow, and motion. Common techniques used for this purpose include morphometry, which measures the geometric or spatial features of the structures; texture analysis, which measures the patterns or variations of the pixel values; elastography, which measures the stiffness or elasticity of the tissues; and Doppler, which measures the velocity or direction of the blood flow. These methods can help assess morphology and growth, differentiate tissues and lesions based on their texture features, detect and stage diseases that affect elasticity, and evaluate hemodynamics and vascularization.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?