scikit-image: image processing in Python - PubMed (original) (raw)

scikit-image: image processing in Python

Stéfan van der Walt et al. PeerJ. 2014.

Abstract

scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal Modified BSD open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image. More information can be found on the project homepage, http://scikit-image.org.

Keywords: Education; Image processing; Open source; Python; Reproducible research; Scientific programming; Visualization.

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Figures

Figure 1

Figure 1. Illustration of several functions available in scikit-image: adaptive threshold, local maxima, edge detection and labels.

The use of NumPy arrays as our data container also enables the use of NumPy’s built-in histogram function.

Figure 2

Figure 2. scikit-image is used to track the propagation of cracks (black lines) in a drying colloidal droplet.

The sequence of pictures shows the temporal evolution of the system with the drop contact line, in green, detected by the Hough transform and the circle, in white, used to extract an annulus of pixel intensities. The result shown illustrates the angular position of cracks and their time of appearance.

Figure 3

Figure 3. The measure.profile_line function being used to track recovery in spinal cord injuries.

(A) An image of fluorescently-labeled nerve cells in an injured zebrafish embryo. (B) The automatically determined region of interest. The SciPy library was used to determine the region extent (Oliphant, 2007; Jones, Oliphant & Peterson, 2001), and functions from the scikit-image draw module were used to draw it. (C) The image intensity along the line of interest, averaged over the displayed width.

Figure 4

Figure 4. Use of scikit-image to study silicon wafer impurities.

(A) An image of an as-cut silicon wafer before it has been processed into a solar cell. (B) A PL image of the same wafer. Wafer defects, which have a negative impact solar cell efficiency, are visible as dark regions. (C) Image processing results. Defects in the crystal growth (dislocations) are colored blue, while red indicates the presence of impurities.

Figure 5

Figure 5. An example application of scikit-image: image registration and warping to combine overlapping images.

(A) Photographs taken in Petra, Jordan by François Malan. License: CC-BY. (B) Putative matches computed from ORB binary features. (C) Matches filtered using RANSAC. (D) The second input frame (middle) is warped to align with the first input frame (left), yielding the averaged image shown on the right. (E) The final panorama image, registered and warped using scikit-image, blended with Enblend.

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