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Web-Based Interactive Measurements

Table 2 : Description of basic workfl ow computations that can be applied to input images. Computation Type

Computation Location

Spatial Filter: Mean, Median, Gaussian, Morphological erosion or dilation

Segmentation: Empirical gradient threshold method

Calibration: Flat field correction


Intensity Scaling to 8 bits per pixel

Multi-resolution pyramid building

Mask generator by tessellation

Deep Zoom configuration

Spatial Filters: Sobel edge, Morphological erosion or dilation

Intensity Filters: Threshold, Contrast, Gamma, Brightness, Saturation, Hue, etc.

Connectivity analysis & region area

Server Server

Server Server Server

Server Server Client Client Client Client Set of pyramids Pyramid tiles Pyramid tiles Canvas

viewing of large images. We enhanced the viewing experience by displaying calibration information for the intensity and zoom level at the mouse cursor location. In addition, a user can bring together all the datasets for transparency-based overlay, multi-channel viewing, and measurement extraction via visual- ization confi guration UI.

4. Interactive measurements in a browser: Current computa- tional resources do not allow for the processing, transfer, visualization, and interactive measurements of TB-sized images. However, scientific explorations and measurements rely on data presented on a computer screen. Our approach is to process and visualize measurements of only the data being viewed in a browser. This approach avoids transferring all data between server and client, while leveraging hierarchical partitioning of a large FOV into its pyramid representation. It also assumes that the client/browser will be able to perform computations in near-real time over screen-size images, a capability present in most web browsers. T us, measurement operations are applied either directly to the pyramid tiles or to rendered pyramid tiles inside of a HTML canvas element. By operating directly on pyramid tiles, a user can zoom and pan while visually inspecting the updated results of measurements.


We illustrate how to assemble a large FOV of aerosolized particles from 40 × 59 FIB SEM image tiles, explore the spatial distri- bution of particles, and measure them using the web-based solution.

22 Inputs Collection Collection Collections

(Raw + Segmented) Collection

Collection Collection

Stitching vector

Tile size and shape, image mask size

Definition of layers and measurement widgets

Kernel size Parameter Stitching options Range min. and max. Kernel size

Min. object size, max. hole size, threshold adjustment delta


Outputs Collection Collection

Collection + Flat field image

Stitching vector Collection

Pyramid Image

Deep Zoom Visualization

Deep Zoom Deep Zoom Image + Table

1. Assembly: Aſt er uploading the aforementioned data to the web system, the collection of image tiles is processed by a set of user-specifi ed steps (that is, a computational workfl ow). T e workfl ow consists of executed Jobs in the menu, such as (1) “flat field correction jobs” using Gaussian filtering (kernel=120) followed by subtraction from the original (see Figure 4 ), (2) noise “fi ltering jobs” using the Gaussian kernel of size 3×3, (3) “segmentation jobs” by Empirical Gradient Segmentation (EGT) [ 4 ] and background removal based on EGT segmentation, (4) “stitching jobs” using the background removed input tiles and the MIST algorithm [ 5 ] derived from existing direct image alignment methods [ 6 – 8 ], and (5) multi- resolution “pyramid jobs” using the stitching vector and fl at fi eld corrected tiles (layer 1) and segmentation (layer 2). All workfl ow steps are also listed in Table 2 . T e steps are described in terms of their inputs, outputs, and operations, and classifi ed based on the client or server location of computations. T e design of a workfl ow sequence depends on the signal- to-noise ratio (SNR) of acquired images and on the time and computational resources allocated for the measurement tasks. Figure 4 shows visual comparison of stitching with and without flat field corrected small FOVs and the size of one FOV with respect to the imaged size of a specimen. T e standard deviation of image with fl at fi eld (14.5) becomes about 1.5 times smaller than the standard deviation without fl at fi eld correction (22.2). The computations can be executed on distributed resources • 2017 January

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