Invariant moment based feature analysis for abnormal erythrocyte recognition
Erythrocyte shape recognition is very important in the detection of thalassemia and anemia using microscopic images. This study aims to develop a computer aided shape recognizer for the recognition of abnormal shapes viz., tear drop, echinocyte, eliptocyte. Here such recognition is done using Hu...
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| creator | Das, D Ghosh, M Chakraborty, C Pal, M Maity, A K |
| description | Erythrocyte shape recognition is very important in the detection of thalassemia and anemia using microscopic images. This study aims to develop a computer aided shape recognizer for the recognition of abnormal shapes viz., tear drop, echinocyte, eliptocyte. Here such recognition is done using Hu's moments and other geometric features followed by gray level thresholding and marker controlled watershed segmentation. These features are statistically evaluated to show their significant in discriminating the mentioned abnormal and normal shapes. In the result, it is found that six moment based features are significant. |
| doi_str_mv | 10.1109/ICSMB.2010.5735380 |
| format | Conference Proceeding |
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In the result, it is found that six moment based features are significant.</description><subject>Analysis of variance</subject><subject>Artificial neural networks</subject><subject>Biomedical optical imaging</subject><subject>Bismuth</subject><subject>Erythrocyte</subject><subject>Invarian moments</subject><subject>light microscopic image</subject><subject>Medical diagnostic imaging</subject><subject>Microscopy</subject><subject>Optical imaging</subject><subject>watershed segmentation</subject><isbn>9781612840390</isbn><isbn>1612840396</isbn><isbn>9781612840383</isbn><isbn>1612840388</isbn><isbn>9781612840376</isbn><isbn>161284037X</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVkMtKxDAYRiMiKGNfQDd5gY75G3NbavFSqLhQ18PfNNFI20gShb69A87Gb3M4m7P4CLkAtgVg5qprX55utw3bu1BccM2OSGWUBgmNvmZc8-N_btgpqXL-ZPuJRmkhzkjfLT-YAi6FznF2ewyY3Ui9w_KdHMUFpzWHTH1MFIclphkn6tJaPlK0a3E0ORvfl1BCXM7Jiccpu-rADXm7v3ttH-v--aFrb_raAjelBpQCvbS68SOghcZwA1xah1wr6a2SyC1qLUejEJgajZAaEPRgG0QAviGXf93gnNt9pTBjWneHD_gvoHhQtA</recordid><startdate>201012</startdate><enddate>201012</enddate><creator>Das, D</creator><creator>Ghosh, M</creator><creator>Chakraborty, C</creator><creator>Pal, M</creator><creator>Maity, A K</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201012</creationdate><title>Invariant moment based feature analysis for abnormal erythrocyte recognition</title><author>Das, D ; Ghosh, M ; Chakraborty, C ; Pal, M ; Maity, A K</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c139t-1a65af6c82fd1ac12939136cea3876fc76a3ca886d97a107d95681a18bc2aa113</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Analysis of variance</topic><topic>Artificial neural networks</topic><topic>Biomedical optical imaging</topic><topic>Bismuth</topic><topic>Erythrocyte</topic><topic>Invarian moments</topic><topic>light microscopic image</topic><topic>Medical diagnostic imaging</topic><topic>Microscopy</topic><topic>Optical imaging</topic><topic>watershed segmentation</topic><toplevel>online_resources</toplevel><creatorcontrib>Das, D</creatorcontrib><creatorcontrib>Ghosh, M</creatorcontrib><creatorcontrib>Chakraborty, C</creatorcontrib><creatorcontrib>Pal, M</creatorcontrib><creatorcontrib>Maity, A K</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Das, D</au><au>Ghosh, M</au><au>Chakraborty, C</au><au>Pal, M</au><au>Maity, A K</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Invariant moment based feature analysis for abnormal erythrocyte recognition</atitle><btitle>2010 International Conference on Systems in Medicine and Biology</btitle><stitle>ICSMB</stitle><date>2010-12</date><risdate>2010</risdate><spage>242</spage><epage>247</epage><pages>242-247</pages><isbn>9781612840390</isbn><isbn>1612840396</isbn><eisbn>9781612840383</eisbn><eisbn>1612840388</eisbn><eisbn>9781612840376</eisbn><eisbn>161284037X</eisbn><abstract>Erythrocyte shape recognition is very important in the detection of thalassemia and anemia using microscopic images. This study aims to develop a computer aided shape recognizer for the recognition of abnormal shapes viz., tear drop, echinocyte, eliptocyte. Here such recognition is done using Hu's moments and other geometric features followed by gray level thresholding and marker controlled watershed segmentation. These features are statistically evaluated to show their significant in discriminating the mentioned abnormal and normal shapes. In the result, it is found that six moment based features are significant.</abstract><pub>IEEE</pub><doi>10.1109/ICSMB.2010.5735380</doi><tpages>6</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Analysis of variance Artificial neural networks Biomedical optical imaging Bismuth Erythrocyte Invarian moments light microscopic image Medical diagnostic imaging Microscopy Optical imaging watershed segmentation |
| title | Invariant moment based feature analysis for abnormal erythrocyte recognition |
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