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Reviews; Medical Biotechnology

Digital image analysis in liver fibrosis: basic requirements and clinical implementation

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Pages 653-660 | Received 31 Jan 2016, Accepted 20 Apr 2016, Published online: 12 May 2016

Figures & data

Figure 1. Digital image analysis process starting from biopsy to quantification of final results.

Figure 1. Digital image analysis process starting from biopsy to quantification of final results.

Figure 2. Typical set-up on optical microscope with digital image acquisition.

Figure 2. Typical set-up on optical microscope with digital image acquisition.

Figure 3. System parameters for magnification and resolution; Mobj, Mvc, Mmon and Mtot represent microscope magnification, video coupler magnification, PC monitor magnification and total magnification, respectively; dmon, dcam, dx,cam and dy,cam represent diagonal dimension of monitor and diagonal dimension of camera sensor, horizontal size of camera sensor and vertical size of the camera sensor, respectively; Ropt, Rdigital and λ represent optical resolution, digital resolution and wavelength, respectively; Nx, and Ny represent number of pixels in horizontal and vertical directions, respectively.

Figure 3. System parameters for magnification and resolution; Mobj, Mvc, Mmon and Mtot represent microscope magnification, video coupler magnification, PC monitor magnification and total magnification, respectively; dmon, dcam, dx,cam and dy,cam represent diagonal dimension of monitor and diagonal dimension of camera sensor, horizontal size of camera sensor and vertical size of the camera sensor, respectively; Ropt, Rdigital and λ represent optical resolution, digital resolution and wavelength, respectively; Nx, and Ny represent number of pixels in horizontal and vertical directions, respectively.

Figure 4. Digital image analysis steps performed on the image of a chronic hepatitis C patients’ biopsy interpreted as Ishak F6 by the pathologist, (a) original image, (b) region-of-interest (ROI) selection (bordering the specimen area), (c) manual thresholding (white voids within the sample represent steatosis), (d) fibrosis and parenchyma areas are converted into a binary colored overlay, (e) feature identification and extraction, (f) quantification of fibrosis and parenchyma areas, trichrome proportionate area is calculated as 0.287/(0.287 + 0.619) × 100% = 31.68%.

Figure 4. Digital image analysis steps performed on the image of a chronic hepatitis C patients’ biopsy interpreted as Ishak F6 by the pathologist, (a) original image, (b) region-of-interest (ROI) selection (bordering the specimen area), (c) manual thresholding (white voids within the sample represent steatosis), (d) fibrosis and parenchyma areas are converted into a binary colored overlay, (e) feature identification and extraction, (f) quantification of fibrosis and parenchyma areas, trichrome proportionate area is calculated as 0.287/(0.287 + 0.619) × 100% = 31.68%.
Supplemental material

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