Digital Image Analysis

A digital image composed of pixels performs an analog image transformed to numerical form using ones and zeros (binary) so that it can be stored and used in a computer system. The digital imaging process consists of four key steps: image acquisition (capture), storage and management (saving), manipulation and annotation (editing), and viewing, display or transmission (sharing) of images. Before digital images become extensively used for periodic clinical work, standards are needed and the entire imaging process approved. For example, when six practicing pathologists were asked to all photographs the same section on a glass slide with similar microscopes that had the same associated digital cameras, they all produce dissimilar images.  Moreover, global manipulation (e.g. contrast enhancement) of Papanicolaou test digital images has been shown to significantly change their interpretation. We also need to pay more consideration to the digital pathology diagnosing station (cockpit) to that they integrate computers with appropriate performance and graphics cards, screens with exceptional image resolution and color quality, as well as connectivity to the Internet, laboratory information system (LIS) and electronic medical record (EMR). The use of computer monitors for digital pathology should, possibly, employ a Macbeth color manager (array of color squares) or correspondent to guarantee precise color balance once a digital image has been developed, computer applications can be leveraged to evaluate the information they hold. More than a few algorithms have been developed (e.g. pattern recognition algorithms) that potential to increase accuracy, reliability, specificity, and productivity. For example, computer assisted image analysis (CAIA) has been used to score (quantify) certain immunohistochemical stains (e.g. ER, PR and HER-2/neu breast biomarkers). In this manner, CAIA gives all pathologists the similar yardstick for scoring immunohistochemistry results in Breast cancer cases. This quantitative method to tissue analysis using WSI has been stated to as "slide-based histocytometry". Multispectral image analysis is additional emerging device that exploits both spatial and spectral image statistics to classify images. This computerized technology has already been shown to be important in certain clinical settings (e.g. cytopathology) to help distinguish and classify morphologically similar lesions.

  • Automated image analysis
  • Analysis software
  • Challenges in image analysis
  • Quantitative image analysis reasearch
  • Visualization methods for diagnosis
  • Annotation tools
  • Pattern recognition

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