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Image Segmentation for Extracting Nanoparticles

Title: Image Segmentation for Extracting Nanoparticles.
Name(s): Allada, Kartheek, author
Park, Chiwoo, professor directing thesis
Shrivastava, Abhishek Kumar, committee member
Liu, Tao, 1969-, committee member
Barbu, Adrian G. (Adrian Gheorghe), 1971-, committee member
Florida State University, degree granting institution
College of Engineering, degree granting college
Department of Industrial and Manufacturing Engineering, degree granting department
Type of Resource: text
Genre: Text
Issuance: monographic
Date Issued: 2015
Publisher: Florida State University
Place of Publication: Tallahassee, Florida
Physical Form: computer
online resource
Extent: 1 online resource (82 pages)
Language(s): English
Abstract/Description: With the advent of nanotechnology, nanomaterials have drastically improved our lives in a very short span of time. The more we can tap into this resource, the more we can change our lives for better. All the applications of nanomaterials depend on how well we can synthesize the nanoparticles in accordance with our desired shape and size, as they determine the properties and thereby the functionality of the nanomaterials. Therefore in this report, it is focused on how to extract the shape of the nanoparticles from electron microscope images using image segmentation more accurately and more efficiently. By developing automated image segmentation procedure, we can systematically determine the contours of an assortment of nanoparticles from electron microscope images; reducing data examination and interpretation time substantially. As a result, the defects in the nanomaterials can be reduced drastically by providing an automated update to the parameters controlling the production of nanomaterials. The report proposes new image segmentation techniques that specifically work very effectively in extracting nanoparticles from electron microscope images. These techniques are manifested by imparting new features to Sliding Band Filter (SBF) method called Gradient Band Filter (GBF) and by amalgamating GBF with Active Contour Without Edges method, followed by fine tuning of μ (a positive parameter in Mumford-Shah functional). The incremental improvement in the performance (in terms of computation time, accuracy and false positives) of extracting nanoparticles is therefore portrayed by comparing image segmentation by SBF versus GBF, followed by comparing Active Contour Without Edges versus Active Contour Without Edges with the fusion of Gradient Band Filter (ACGBF). In addition we compare the performance of a new technique called Variance Method to fine tune the value of μ with fine tuning of μ based on ground truth, followed by gauging the improvement in the performance of image segmentation by ACGBF with fine tuned value of μ over ACGBF with an arbitrary value of μ.
Identifier: FSU_2015fall_Allada_fsu_0071N_12975 (IID)
Submitted Note: A Thesis submitted to the Department of Industrial & Manufacturing Engineering in partial fulfillment of the requirements for the degree of Master of Science.
Degree Awarded: Fall Semester 2015.
Date of Defense: November 09, 2015.
Keywords: Active Contours, Image Segmentation, Nanoparticles, Sliding Band Filter
Bibliography Note: Includes bibliographical references.
Advisory Committee: Chiwoo Park, Professor Directing Thesis; Abhishek Shrivastava, Committee Member; Tao Liu, Committee Member; Adrian Barbu, Committee Member.
Subject(s): Industrial engineering
Persistent Link to This Record:
Owner Institution: FSU

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Allada, K. (2015). Image Segmentation for Extracting Nanoparticles. Retrieved from