{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/19989"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/19989","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Machine vision microscopy as an on-line sensor for bioprocesses","abstract":"A machine vision microscopy system (combination of video hardware and software and microscope for automated identification) was developed for classifying and counting microscopic objects in a Bacillus thuringiensis (Bt) fermentation. Image morphology properties consisting of area, perimeter, major length, minor length, number of holes, area of holes, and perimeter of holes were collected for vegetative cells, spores and protein crystals, and cells with included spores and crystals. Spatial resolution was 0.1333 $\\mu$m/pixel in the horizontal direction and 0.1667 $\\mu$m/pixel in the vertical direction.","abstract_html":"A machine vision microscopy system (combination of video hardware and software and microscope for automated identification) was developed for classifying and counting microscopic objects in a Bacillus thuringiensis (Bt) fermentation. Image morphology properties consisting of area, perimeter, major length, minor length, number of holes, area of holes, and perimeter of holes were collected for vegetative cells, spores and protein crystals, and cells with included spores and crystals. Spatial resolution was 0.1333 <span class=\"etd-inline-math\">&mu;</span>m/pixel in the horizontal direction and 0.1667 <span class=\"etd-inline-math\">&mu;</span>m/pixel in the vertical direction.","abstract_has_math":true,"creators":["Richburg, Brent Allen"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Agricultural and Biological Engineering","degree_department":null,"school":null,"contributors":["Reid, John F."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":1992,"date_issued":"1992","date_published":"1992","updated_at":"2026-07-22T22:25:15Z","subjects":["Biology, Microbiology","Engineering, Agricultural"],"languages":["eng"],"rights":["Copyright 1992 Richburg, Brent Allen"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["AAI9236578","(UMI)AAI9236578"],"render_values":[{"text":"AAI9236578","href":null,"code":true},{"text":"(UMI)AAI9236578","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/19989","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Reid, John F."]},{"key":"dc:creator","label":"Author","values":["Richburg, Brent Allen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["1992","2011-05-07T12:25:13Z","10000-01-01"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Agricultural and Biological Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Biology, Microbiology","Engineering, Agricultural"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 1992 Richburg, Brent Allen"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["AAI9236578","(UMI)AAI9236578","http://hdl.handle.net/2142/19989"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["A machine vision microscopy system (combination of video hardware and software and microscope for automated identification) was developed for classifying and counting microscopic objects in a Bacillus thuringiensis (Bt) fermentation. Image morphology properties consisting of area, perimeter, major length, minor length, number of holes, area of holes, and perimeter of holes were collected for vegetative cells, spores and protein crystals, and cells with included spores and crystals. Spatial resolution was 0.1333 $\\mu$m/pixel in the horizontal direction and 0.1667 $\\mu$m/pixel in the vertical direction.","A distance classifier and a neural network classifier were evaluated for accuracy using a limited data set. The neural network classifier was selected, refined, and implemented into software to classify, count and identify objects on the vision system display. The classifier accurately identified 92.0 percent of vegetative cells and 91.0 percent of spores when compared to human classification. Vegetative cell counts were 97.3 percent accurate and spore counts were 77.9 percent accurate. Identification of cells with included spores/crystals was 16.7 percent accurate and count accuracy for this class was 27.8 percent. The system did not accurately identify cells with inclusions due to their imaging characteristics. However, the system classified spores and vegetative cells with enough precision for determination of changes in cell and spore population over time. Spore counts from the machine vision system were plotted over time and showed that the system could identify when spore population began to increase and when it reached a maximum.","Object counts were performed in approximately 1.7 seconds for live images. The count results from five images were averaged together to get object counts. Averaging more images did not lower variability or change the mean count. The counts obtained from the machine vision microscopy system accurately followed trends in vegetative cell growth when compared to optical density measurements. The machine vision microscopy system predicted beginning and end of log growth within an hour of those predicted by optical density measurements.","Made available in DSpace on 2011-05-07T12:25:13Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9236578.pdf: 3433451 bytes, checksum: 6184c0b0e44ae694edde09fcb152c276 (MD5) Previous issue date: 1992","Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:40:47Z Item is restricted indefinitely.","Restriction data tranferred 2014-07-01T11:17:33-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission","ETDs are only available to UIUC Users without author permission","U of I Only"]},{"key":"dc:title","label":"Title","values":["Machine vision microscopy as an on-line sensor for bioprocesses"]}]}],"canonical_facts":{"dc:contributor":["Reid, John F."],"dc:creator":["Richburg, Brent Allen"],"dc:date":["1992","2011-05-07T12:25:13Z","10000-01-01"],"dc:description":["A machine vision microscopy system (combination of video hardware and software and microscope for automated identification) was developed for classifying and counting microscopic objects in a Bacillus thuringiensis (Bt) fermentation. Image morphology properties consisting of area, perimeter, major length, minor length, number of holes, area of holes, and perimeter of holes were collected for vegetative cells, spores and protein crystals, and cells with included spores and crystals. Spatial resolution was 0.1333 $\\mu$m/pixel in the horizontal direction and 0.1667 $\\mu$m/pixel in the vertical direction.","A distance classifier and a neural network classifier were evaluated for accuracy using a limited data set. The neural network classifier was selected, refined, and implemented into software to classify, count and identify objects on the vision system display. The classifier accurately identified 92.0 percent of vegetative cells and 91.0 percent of spores when compared to human classification. Vegetative cell counts were 97.3 percent accurate and spore counts were 77.9 percent accurate. Identification of cells with included spores/crystals was 16.7 percent accurate and count accuracy for this class was 27.8 percent. The system did not accurately identify cells with inclusions due to their imaging characteristics. However, the system classified spores and vegetative cells with enough precision for determination of changes in cell and spore population over time. Spore counts from the machine vision system were plotted over time and showed that the system could identify when spore population began to increase and when it reached a maximum.","Object counts were performed in approximately 1.7 seconds for live images. The count results from five images were averaged together to get object counts. Averaging more images did not lower variability or change the mean count. The counts obtained from the machine vision microscopy system accurately followed trends in vegetative cell growth when compared to optical density measurements. The machine vision microscopy system predicted beginning and end of log growth within an hour of those predicted by optical density measurements.","Made available in DSpace on 2011-05-07T12:25:13Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9236578.pdf: 3433451 bytes, checksum: 6184c0b0e44ae694edde09fcb152c276 (MD5) Previous issue date: 1992","Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:40:47Z Item is restricted indefinitely.","Restriction data tranferred 2014-07-01T11:17:33-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission","ETDs are only available to UIUC Users without author permission","U of I Only"],"dc:identifier":["AAI9236578","(UMI)AAI9236578","http://hdl.handle.net/2142/19989"],"dc:language":["eng"],"dc:rights":["Copyright 1992 Richburg, Brent Allen"],"dc:subject":["Biology, Microbiology","Engineering, Agricultural"],"dc:title":["Machine vision microscopy as an on-line sensor for bioprocesses"],"dc:type":["text"],"thesis:degree_discipline":["Agricultural and Biological Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:15Z"}