Entry 16

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Authors

  • Adam Hughes

Abstract

Nanotechnology and image processing are two of the most rapidly growing interdisciplinary research fields. Gold nanoparticles have recently appeared in novel applications ranging from photovoltaics [1], to protein sensing [2], and even to boiling water [3]. Likewise, fascinating applications of image processing, for example in areas like computer vision, ensure vested interest from academic and commercial organizations for the foreseeable future.

High-resolution microscopy (HRM) is the keystone between nanotech and image processing. This is not surprising, since small variations in nanoparticle morphology, both at the individual particle and ensemble scale, drastically affect the macroscopic properties of the composite. One would expect to find a considerable body of knowledge and software geared towards HRM nanomaterial processing; however, this is not the case. Despite similar requirements in image acquisition and processing, fields like cell profiling [4] vastly overshadow nanotech. HRM in terms of knowledge base and dedicated software. Using scientific Python, especially Scikit-image, we have begun addressing this disparity.

We sought to create a guide for nanomaterial image processing, but didn’t want to make biased assessments based on our experimental images. Using Scikit-image and a complementary particle analysis library, PyParty, we set out to build artificial electron microscope images. We could then compare the performance of preprocessing and segmentation algorithms in the context of nanoscience, and begin to assemble targeted workflows. The chosen image features particles of varying multiplicity, brightness and orientation, patterned over a shadowed background. Realistic particle edges were obtained with Gaussian smoothing, and normal noise was generated in Numpy. We have already successfully used these images in several endeavors, from assessing the performance of new supervised object classification tools [5], to building predictive models for nanoparticle-ligand binding on rough thin films. These images are available for public use, and we hope they will be repurposed many times.

[1]Shiva Shahin, Palash Gangopadhyay, and Robert a. Norwood. Ultrathin organic bulk heterojunction solar cells: Plasmon enhanced performance using Au nanoparticles. Applied Physics Letters, 101(5):053109, 2012.
[2]V V R Sai, Tapanendu Kundu, and Soumyo Mukherji. Novel U-bent fiber optic probe for localized surface plasmon resonance based biosensor. Biosensors & bioelectronics, 24(9):2804–9, May 2009.
[3]Phil McKenna. Nanoparticles Make Steam without Bringing Water to a Boil. MIT Technology Review, 2012.
[4]Kamentsky L, Jones TR, Fraser A, Bray M, Logan D, Madden K, Ljosa V, Rueden C, Harris GB, Eliceiri K, Carpenter AE (2011) Improved structure, function, and compatibility for CellProfiler: modular high-throughput image analysis software. Bioinformatics 2011/doi. PMID: 21349861 PMCID: PMC3072555
[5]C. Sommer, C. Straehle, U. Köthe, and F.A. Hamprecht. 8th IEEE International Symposium on Biomedical Imaging (ISBI 2011), in press

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