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Fast Line-Based Imaging of Small Sample Features

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Title: Fast Line-Based Imaging of Small Sample Features


1
Fast Line-Based Imaging of Small Sample Features
  • M.A. Iwen1, G.S. Mandair2, M.D. Morris2, M.
    Strauss1,3 University of Michigan
  • 1 - Department of Mathematics, 2 - Department of
    Chemistry, 3 - Electrical Engineering and
    Computer Science

Problem Setup
Proposed Solutions
Experiments
  • Given a low resolution First-Pass Raman
    spectroscopic image, I, and a small set of
    interesting image features, P, how can we obtain
    higher resolution images of P using the smallest
    number of scans possible?
  • Image I is an n x m rectangle of pixels
  • P is an interesting set of pixels in I
  • Each scan images an entire row/column
  • Minimizing Scans minimizes both acquisition time
    and sample damage
  • Optimal Columns
  • Scan only the columns containing at least one P
    pixel.
  • Skips uninteresting columns in between the first
    and last interesting columns with P pixels
  • Easy to implement, but not the best solution
  • Optimal Rows Columns
  • Find a smallest possible set of rows and columns
    that covers every interesting pixel in P. Scan
    the found optimal rows columns.
  • Finding an optimal rows columns covering of P
    is a special case of the Set cover problem
  • The Set cover problem is generally NP hard
  • However, rows columns is solvable in
    polynomial time using Ford-Fulkerson method


Number of Lightest HELLO Pixels to Scan Vs.
Number Rows/Columns to Cover Them
4 x 3 Image I with Interesting P Black Pixels
Optimal Rows Columns
Standard Solution

Form Scan Graph.
  • Push Broom
  • Scan from the first column containing a P pixel
    to the last column containing a P pixel (and all
    columns in between).
  • Limitations
  • Manual and Non-adaptive

Scan Time (sec) to Image Lightest Bone
Pixels, Scan Time 60 8(scanned rows/columns)
Conclusions
Find min-cut and get Residual Network (RN). Label
RN nodes not reachable from source white and RN
nodes reachable from source gray.
  • Both Optimal Columns and Optimal Rows Columns
    can decrease integration time and sample damage
  • Optimal Columns is guaranteed to use lt the
    scans Push Broom uses
  • Optimal Rows Columns is guaranteed to use lt
    the scans Optimal Columns uses

Push Broom
Scan white rows and gray columns.
scanned
scanned
This work was supported in part by NSF grant
DMS-0510203
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