Biomedical Image Analysis

The history of biomedical imaging is comparatively short. In 1895, Wilhelm Conrad Rontgen discovered a new type of radiation, which he called the x-ray. The discovery caused a revolution in medicine, because for the first time it became possible to see inside the human body without surgery. Use of x-rays in medical centers spread rapidly, […]

Main Biomedical Imaging Modalities

A number of fundamentally different methods of obtaining images from tissue, called imaging modalities, emerged during the historical development of biomedical imaging, and the information that these modalities provide differs among modalities. It is outside our scope here to provide a detailed description of the physical and engineering foundations of the modalities, but a short […]

Biomedical Image Analysis

Biomedical image analysis is a highly interdisciplinary field, being at the interface of computer sciences, physics, medicine, biology, and engineering. Fundamentally, biomedical image analysis is the application of image processing techniques to biological or medical problems. However, in biomedical image analysis, a number of other fields play an important role: • Anatomy. Knowledge of shape, […]

Current Trends In Biomedical Imaging

Once computers started to play an instrumental role in image formation in modalities such as CT and MRI, the next step was indeed a small one: to use the same computers for image enhancement. Operations such as contrast enhancement, sharpening, and noise reduction became integrated functions in the imaging software. A solid body of image […]

Survey of Fundamental Image Processing Operators (Biomedical Image Analysis)

Many image processing operators covered in this topic are based on, or derived from, more fundamental image processing operators that are in wide use. A survey of these operators is presented in this topic to create a foundation on which subsequent topics build. For the purpose of this topic, the image is a matrix of […]

Brightness and Contrast Manipulation (Biomedical Image Analysis)

Software for viewing images often makes it possible to manipulate brightness and contrast. The displayed image values are remapped by applying a linear or nonlinear function f in a manner that where I(x,y) is the original image value, I’(x,y) the remapped image value, and Imin and Imax the smallest and largest intensity values in the […]

Image Enhancement and Restoration Part 1 (Biomedical Image Analysis)

Image enhancement and restoration use similar operators but are driven by different goals. Image restoration is a process specifically designed to counteract known image degradation: for example, to improve the overall point-spread function of an image. Image enhancement is a user-driven process to meet specific image quality criteria: for example, noise reduction, sharpening, or edge […]

Image Enhancement and Restoration Part 2 (Biomedical Image Analysis)

Edge enhancement and edge detection are very important operations in image processing. Many edge detection operators were devised early in the age of computerized image processing, and most build on finite differences. In all cases, special provisions are taken to ensure that edges are enhanced irrespective of their orientation. One of the earliest edge detectors […]

Intensity-Based Segmentation (Thresholding) (Biomedical Image Analysis)

The purpose of segmentation is to separate one or more regions of interest in an image from regions that do not contain relevant information. Regions that do not contain relevant information are called background. Depending on the image, segmentation can be a very complex process, and a comprehensive overview of the most relevant segmentation techniques […]

Multidimensional Thresholding (Biomedical Image Analysis)

The idea of thresholding can be extended to encompass multiple criteria. For example, color images contain different information in the various color channels. Gray-scale images contain additional information in the local neighborhood of each pixel: for example, the local standard deviation or the local contrast. For each pixel, multiple criteria can be defined to determine […]