Anniversary Paper: Image processing and manipulation through the pages ofMedical Physics

Abstract

The language of radiology has gradually evolved from "the film" (the foundation of radiology since Wilhelm Roentgen's 1895 discovery of x-rays) to "the image," an electronic manifestation of a radiologic examination that exists within the bits and bytes of a computer. Rather than simply storing and displaying radiologic images in a static manner, the computational power of the computer may be used to enhance a radiologist's ability to visually extract information from the image through image processing and image manipulation algorithms. Image processing tools provide a broad spectrum of opportunities for image enhancement. Gray-level manipulations such as histogram equalization, spatial alterations such as geometric distortion correction, preprocessing operations such as edge enhancement, and enhanced radiography techniques such as temporal subtraction provide powerful methods to improve the diagnostic quality of an image or to enhance structures of interest within an image. Furthermore, these image processing algorithms provide the building blocks of more advanced computer vision methods. The prominent role of medical physicists and the AAPM in the advancement of medical image processing methods, and in the establishment of the "image" as the fundamental entity in radiology and radiation oncology, has been captured in 35 volumes of Medical Physics.

Frequency response and distortion properties of nonlinear image processing algorithms and the importance of imaging context

“…Modern medical imaging has benefited from the use of nonlinear image processing algorithms. 1,2 Nonlinear image denoising, [3][4][5][6] enhancement, [7][8][9] and reconstruction [10][11][12][13] have all been used to successfully improve image interpretability and diagnostic outcomes or to reduce patient radiation exposure and scan times. In spite of the many benefits from nonlinear processing, a complete, objective, and standardized measure of nonlinear resolution performance has not been universally accepted by investigators in the field.…”

“…The images corresponding to each filter are equally scaled. Image intensity has units of pixelvalue2 × pixel area. White Noise Median Filter, Correlated Noise Median Filter, Anatomical Background Bilateral Filter, White Noise Bilateral Filter, Correlated Noise Bilateral Filter, Anatomical Background Wavelet Denoising, White Noise Wavelet Denoising, Correlated Noise Wavelet Denoising, Anatomical BackgroundFIG.…”

Imaging Informatics: Toward Capturing and Processing Semantic Information in Radiology Images

“…This replacement of filmbased radiology systems with an electronic infrastructure for the storage and retrieval of image data created opportunities for more efficient workflow [2]. Research has also involved the images themselves, with image processing methods developed for image segmentation, computer assisted detection, to reduce noise and distortion, and to provide advanced visualization to enhance the conspicuity of structures and abnormalities within images [3].…”