Images generated by Medical Imaging Equipment play a key role in assisting medical diagnosis, and a variety of analysis and processing methods help to better mine information from images.
1. Image Enhancement
Image enhancement is a basic and important processing method. It aims to improve the visual effect of the image and make the details in the image clearer, so as to help doctors observe the characteristics of the lesion more accurately. Grayscale adjustment is one of the commonly used means. By changing the grayscale distribution of the image, the contrast between the target area and the background is enhanced. For example, in an X-ray chest film, the grayscale difference between the lesion area of the lungs may be small from that of normal tissues. Grayscale stretching can expand the grayscale range of the lesion area, making it easier to find. In addition, filtering technology is also widely used in image enhancement. For example, high-pass filtering can highlight the edge information in the image, which is very helpful for detecting the boundaries of tumors or the contours of blood vessels; while low-pass filtering can reduce the noise in the image and make the image smoother.
2. Image Segmentation
Image segmentation is the process of separating different tissues, organs or lesion areas in medical images from the background. The threshold-based segmentation method sets a threshold according to the grayscale difference between different areas in the image and divides the pixels into different categories. For example, in an MRI image of the brain, brain tissue can be separated from cerebrospinal fluid by a suitable threshold. Region growing is also a commonly used segmentation technique. It starts from a seed point in the image and gradually merges adjacent pixels according to similarity criteria (such as grayscale similarity, texture similarity, etc.) until it grows to the point where it can no longer grow. This method works well for segmenting lesion areas with similar characteristics. Image segmentation can provide doctors with more accurate lesion range and morphological information, which helps to accurately assess the condition.
3. 3D reconstruction
3D reconstruction technology converts two-dimensional medical imaging data into a three-dimensional model to provide doctors with more intuitive spatial information. 3D reconstruction based on volume rendering generates a three-dimensional image based on principles such as light projection by assigning color and opacity to each voxel in the volume data. This method can display the three-dimensional structure of internal organs. For example, in the three-dimensional reconstruction of the heart, the spatial relationship between the various chambers and blood vessels of the heart can be clearly seen. Surface reconstruction extracts the surface contours of tissues or organs to construct a three-dimensional model. It is widely used for tissues with clear surface structures such as bones. 3D reconstruction helps doctors observe lesions from multiple angles, which is especially important in surgical planning and simulation.
4. Quantitative analysis
Quantitative analysis is a method of extracting numerical information from medical images to assist diagnosis. For example, in CT images, the volume, density and other parameters of lesions can be measured, which can serve as an important basis for evaluating the nature and progression of lesions. Texture analysis is also part of quantitative analysis, which distinguishes normal tissue from diseased tissue by analyzing the texture features of images (such as roughness, directionality, etc.). In addition, by performing quantitative analysis on images at different time points, the dynamic changes of the disease, such as the growth rate of tumors and the effect of treatment, can also be monitored. These analysis and processing methods work together to provide strong support for the effective application of medical images in clinical diagnosis and treatment.
