Segmentation of carbon nanotube images through an artificial neural network
Segmentation of carbon nanotube images is an important task for nanotechnology. The segmentation stage determines the accuracy of the measurement process of nanotube when assessing the quality of nanomaterials. In this work, we propose two segmentation algorithms for carbon nanotube images. Each alg...
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| Veröffentlicht in: | Soft computing (Berlin, Germany) Germany), 2017-02, Vol.21 (3), p.611-625 |
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| creator | Trujillo, María Celeste Ramírez Alarcón, Teresa E. Dalmau, Oscar S. Zamudio Ojeda, Adalberto |
| description | Segmentation of carbon nanotube images is an important task for nanotechnology. The segmentation stage determines the accuracy of the measurement process of nanotube when assessing the quality of nanomaterials. In this work, we propose two segmentation algorithms for carbon nanotube images. Each algorithm includes three stages: preprocessing, segmentation and postprocessing. The first one is applied on images from scanning electron microscopy and employs a matched filter bank in the preprocessing step followed by a neural network in the segmenting phase. The second algorithm uses the Perona–Malik filter for enhancing the nanotube information. The segmentation phase is composed of the relaxed Otsu’s threshold and an artificial neural network. This algorithm is applied on images from transmission electron microscopy. The postprocessing stage, for both algorithms, is based on mathematical morphology. The performance of the proposed algorithms is numerically evaluated by using real image databases, manually segmented by an expert. The algorithm for segmentation of scanning electron microscopy achieved 92.74% of overall accuracy, while the algorithm for segmentation of transmission electron microscopy obtained an accuracy of 73.99% if the whole image is considered. A performance improvement is accomplished if only the region of interest is segmented, arriving to 84.19% of overall accuracy. |
| doi_str_mv | 10.1007/s00500-016-2426-1 |
| format | Article |
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The segmentation stage determines the accuracy of the measurement process of nanotube when assessing the quality of nanomaterials. In this work, we propose two segmentation algorithms for carbon nanotube images. Each algorithm includes three stages: preprocessing, segmentation and postprocessing. The first one is applied on images from scanning electron microscopy and employs a matched filter bank in the preprocessing step followed by a neural network in the segmenting phase. The second algorithm uses the Perona–Malik filter for enhancing the nanotube information. The segmentation phase is composed of the relaxed Otsu’s threshold and an artificial neural network. This algorithm is applied on images from transmission electron microscopy. The postprocessing stage, for both algorithms, is based on mathematical morphology. The performance of the proposed algorithms is numerically evaluated by using real image databases, manually segmented by an expert. The algorithm for segmentation of scanning electron microscopy achieved 92.74% of overall accuracy, while the algorithm for segmentation of transmission electron microscopy obtained an accuracy of 73.99% if the whole image is considered. 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The segmentation stage determines the accuracy of the measurement process of nanotube when assessing the quality of nanomaterials. In this work, we propose two segmentation algorithms for carbon nanotube images. Each algorithm includes three stages: preprocessing, segmentation and postprocessing. The first one is applied on images from scanning electron microscopy and employs a matched filter bank in the preprocessing step followed by a neural network in the segmenting phase. The second algorithm uses the Perona–Malik filter for enhancing the nanotube information. The segmentation phase is composed of the relaxed Otsu’s threshold and an artificial neural network. This algorithm is applied on images from transmission electron microscopy. The postprocessing stage, for both algorithms, is based on mathematical morphology. The performance of the proposed algorithms is numerically evaluated by using real image databases, manually segmented by an expert. The algorithm for segmentation of scanning electron microscopy achieved 92.74% of overall accuracy, while the algorithm for segmentation of transmission electron microscopy obtained an accuracy of 73.99% if the whole image is considered. A performance improvement is accomplished if only the region of interest is segmented, arriving to 84.19% of overall accuracy.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Artificial neural networks</subject><subject>Carbon</subject><subject>Carbon nanotubes</subject><subject>Computational Intelligence</subject><subject>Control</subject><subject>Deep learning</subject><subject>Engineering</subject><subject>Filter banks</subject><subject>Focus</subject><subject>Image segmentation</subject><subject>Image transmission</subject><subject>Matched filters</subject><subject>Mathematical Logic and Foundations</subject><subject>Mathematical morphology</subject><subject>Mechatronics</subject><subject>Nanomaterials</subject><subject>Neural networks</subject><subject>Preprocessing</subject><subject>Quality assessment</subject><subject>Robotics</subject><subject>Scanning electron microscopy</subject><subject>Support vector machines</subject><subject>Transmission electron microscopy</subject><issn>1432-7643</issn><issn>1433-7479</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp1kEtLAzEUhYMoWKs_wN2A6-jNo8lkKcUXFlyo65DJZKZT26QmGcR_b9oKrlyds_jOudyD0CWBawIgbxLADAADEZhyKjA5QhPCGcOSS3W89xRLwdkpOktpBUCJnLEJen51_cb5bPIQfBW6yprYFOeND3lsXDVsTO9SlZcxjP2yMr4yMQ_dYAezrrwb417yV4gf5-ikM-vkLn51it7v797mj3jx8vA0v11gy4jIWBDWSiEUcaZRSnLXth1QZcF0pK6tVJbXirbECJC1rCkTjTXcNFS4WV1oNkVXh95tDJ-jS1mvwhh9OampIlIBrxkUihwoG0NK0XV6G8sz8VsT0LvN9GEzXTbTu800KRl6yKTC-t7Fv-b_Qz-k9G6w</recordid><startdate>20170201</startdate><enddate>20170201</enddate><creator>Trujillo, María Celeste Ramírez</creator><creator>Alarcón, Teresa E.</creator><creator>Dalmau, Oscar S.</creator><creator>Zamudio Ojeda, Adalberto</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><orcidid>https://orcid.org/0000-0002-1828-8458</orcidid></search><sort><creationdate>20170201</creationdate><title>Segmentation of carbon nanotube images through an artificial neural network</title><author>Trujillo, María Celeste Ramírez ; Alarcón, Teresa E. ; Dalmau, Oscar S. ; Zamudio Ojeda, Adalberto</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-613d76691eab9974eddf029c0af188c79c4892d1a607878236bca4ab26e584ed3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Artificial neural networks</topic><topic>Carbon</topic><topic>Carbon nanotubes</topic><topic>Computational Intelligence</topic><topic>Control</topic><topic>Deep learning</topic><topic>Engineering</topic><topic>Filter banks</topic><topic>Focus</topic><topic>Image segmentation</topic><topic>Image transmission</topic><topic>Matched filters</topic><topic>Mathematical Logic and Foundations</topic><topic>Mathematical morphology</topic><topic>Mechatronics</topic><topic>Nanomaterials</topic><topic>Neural networks</topic><topic>Preprocessing</topic><topic>Quality assessment</topic><topic>Robotics</topic><topic>Scanning electron microscopy</topic><topic>Support vector machines</topic><topic>Transmission electron microscopy</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Trujillo, María Celeste Ramírez</creatorcontrib><creatorcontrib>Alarcón, Teresa E.</creatorcontrib><creatorcontrib>Dalmau, Oscar S.</creatorcontrib><creatorcontrib>Zamudio Ojeda, Adalberto</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Soft computing (Berlin, Germany)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Trujillo, María Celeste Ramírez</au><au>Alarcón, Teresa E.</au><au>Dalmau, Oscar S.</au><au>Zamudio Ojeda, Adalberto</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Segmentation of carbon nanotube images through an artificial neural network</atitle><jtitle>Soft computing (Berlin, Germany)</jtitle><stitle>Soft Comput</stitle><date>2017-02-01</date><risdate>2017</risdate><volume>21</volume><issue>3</issue><spage>611</spage><epage>625</epage><pages>611-625</pages><issn>1432-7643</issn><eissn>1433-7479</eissn><abstract>Segmentation of carbon nanotube images is an important task for nanotechnology. The segmentation stage determines the accuracy of the measurement process of nanotube when assessing the quality of nanomaterials. In this work, we propose two segmentation algorithms for carbon nanotube images. Each algorithm includes three stages: preprocessing, segmentation and postprocessing. The first one is applied on images from scanning electron microscopy and employs a matched filter bank in the preprocessing step followed by a neural network in the segmenting phase. The second algorithm uses the Perona–Malik filter for enhancing the nanotube information. The segmentation phase is composed of the relaxed Otsu’s threshold and an artificial neural network. This algorithm is applied on images from transmission electron microscopy. The postprocessing stage, for both algorithms, is based on mathematical morphology. The performance of the proposed algorithms is numerically evaluated by using real image databases, manually segmented by an expert. The algorithm for segmentation of scanning electron microscopy achieved 92.74% of overall accuracy, while the algorithm for segmentation of transmission electron microscopy obtained an accuracy of 73.99% if the whole image is considered. A performance improvement is accomplished if only the region of interest is segmented, arriving to 84.19% of overall accuracy.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00500-016-2426-1</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-1828-8458</orcidid></addata></record> |
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| subjects | Accuracy Algorithms Artificial Intelligence Artificial neural networks Carbon Carbon nanotubes Computational Intelligence Control Deep learning Engineering Filter banks Focus Image segmentation Image transmission Matched filters Mathematical Logic and Foundations Mathematical morphology Mechatronics Nanomaterials Neural networks Preprocessing Quality assessment Robotics Scanning electron microscopy Support vector machines Transmission electron microscopy |
| title | Segmentation of carbon nanotube images through an artificial neural network |
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