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Image Stitching for Optical Microscopy

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Presentation on theme: "Image Stitching for Optical Microscopy"— Presentation transcript:

1 Image Stitching for Optical Microscopy
Timothy Blattner, Bertrand Stivalet, Walid Keyrouz Shujia Zhou IAB

2 Image Stitching for Optical Microscopy
Objectives Stitching of optical microscopy images at interactive rates General purpose library, ImageJ/Fiji plug-in, etc. Success criterion Transformative impact Run sample problem in < 1 min > 10x speed improvement IAB

3 Credits Joe Chalfoun, Mary Brady NIST IAB

4 Image Stitching Problem
Optical microscopes scan a plate and take overlapping partial images (tiles) Need to assemble image tiles into one large image Modern microscopy automated: Scientists are acquiring & processing large sets of images IAB

5 Image Stitching Problem…
Header Image Stitching Problem… Two phases: Compute the X & Y translations for all tiles Apply the translations & compose the stitched image Main focus is on phase I IAB Footer

6 Image Stitching Algorithm
Loop over all images: Read an image tile Compute its FFT-2D Compute correlation coefficients with west and north neighbors Depends on FFT-2D for each tile Major compute portions: FFT-2D of tiles Compute and normalize phase correlation Inverse FFT-2D Reduce max normalize IAB

7 Algorithm’s Parallel Characteristics
Almost embarrassingly parallel Large number of independent computations For an n x m grid: FFT for all images nxm NCC for all image pairs 2nxm - n - m FFT-1 for the NCCs of all image pairs 2nxm - n - m Caveats Data FFT dependencies Limited memory IAB

8 Data Set Grid of 59x42 images (2478)
1392x bit grayscale images (2.8 MB per image) ~ 7 GB Source: Kiran Bhadriraju (NIST) IAB

9 Evaluation Platform Hardware
Dual Intel® Xeon® E-5620 CPUs (quad-core, 2.4 GHz, hyper-threading) 24 GB RAM Dual NVIDIA® TeslaTM C2070 cards Reference Implementations Fiji™ Stitching plugin, >3.6 hours MATLAB® prototype, ~17.5 minutes on a similar machine Software Ubuntu Linux 12.04/x86_64, kernel 3.2.0 Libc , libstd BOOST 1.48, FFTW 3.3, libTIFF4 NVIDIA CUDA & CUFFT 5.0 IAB

10 Implementations & Results FFTW Exhaustive, CUDA 5.0
Time Speedup CPU Threads GPUs C++ Sequential 10 min 37 sec 1 Simple Multi-Threaded 1 min 48 sec 5.8x 8 Pipelined Multi-Threaded 1 min 22 sec 7.7x 19 Simple GPU 9 min 47 sec 1.08x Pipelined-Hybrid 25 sec 25.5x 13 2 IAB

11 Java Implementation Allows easy integration info Fiji
Tool used by many biologists for image stitching Pure Java code is extremely slow FFT computations Cross correlation Use JNI with FFTW and C code Java native interface Allows calling functions off of the virtual machine Requires compilation (gcc) IAB

12 Java Implementation Runtimes 42x59 Tiles
Threads Sequential > 4 hours 1 Sequential with JNI ~30 minutes Pipelined with JNI 3 min 42 sec 16 IAB

13 Closure—General 25x speedup compared to Sequential C++ code
518x speedup compared to Fiji stitching plugin Representative data set: 42x59 grid ~25 sec Can budget compute time to: Generate stitched image Carry out additional analysis Enables computationally steerable experiments IAB

14 Closure—Java Implementation
Single threaded-executes full grid in ~45 minutes using FFTW native interface Multi-threaded executes in ~4 minutes Optimized version uses native intrinsics for computing cross correlation Provides simple integration into Fiji application Need to provide JNI for GPU functions JCUDA IAB

15 Thank You Questions? IAB

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