Automatic extraction of positive cells in pathology images of meningioma based on the maximal entropy principle and HSV color space
This paper describes a computer-aided system for analyzing immunohistochemically stained meningioma cancer cell images. Accurate segmentation of cells in such images plays a critical role in diagnosing different type of meningioma cancer. The methodpresented to automatically extract the positive cel...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 4 |
|---|---|
| container_issue | |
| container_start_page | 1 |
| container_title | |
| container_volume | |
| creator | Anari, V. Mahzouni, P. Amirfattahi, R. |
| description | This paper describes a computer-aided system for analyzing immunohistochemically stained meningioma cancer cell images. Accurate segmentation of cells in such images plays a critical role in diagnosing different type of meningioma cancer. The methodpresented to automatically extract the positive cells in meninigioma tumor immunohistochemical pathology images based on HSV color space. First, according to distribution rules of positive cells in the HSV color space, it uses the component H, S and V as threshold conditions and leverages the maximal entropy principle to build a model to segment and extract positive cells. Experimental results shows that proposed algorithm can be used by pathologist to detection reliable quantitatively analyze the parameter of tumor cells and over come to disadvantages of the traditional approach. |
| doi_str_mv | 10.1109/IranianMVIP.2010.5941150 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_5941150</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5941150</ieee_id><sourcerecordid>5941150</sourcerecordid><originalsourceid>FETCH-LOGICAL-i90t-4139ca189999504fd8e1a9036a250e8b6fa149876057ecd26b98a0e27e25680f3</originalsourceid><addsrcrecordid>eNpFkEFLw0AQhVdUUGt_gZf5A6m7yWaTPZaitlBRsBRvZbqZtCvJJmRXac_-cVcs-C7DDO99PIYxEHwiBNf3iwGdRfe8XrxOUh6vuZZC5PyM3QiZSqkLXpTn_4t6v2DXqVAqUUWhrtjY-w8epZTWXF6z7-ln6FoM1gAdwoAm2M5BV0PfeRvsF4GhpvFgHfQY9l3T7Y5gW9yR_3W15Kzb2UiALXqqIIbDnqDFQzQ1QC4MXX-EfrDO2L4hQFfB_G0NJqIG8D0aumWXNTaexqc5YqvHh9Vsnixfnhaz6TKxmodEikwbFKWOyrmsq5IEap4pTHNO5VbVKKQuC8XzgkyVqq0ukVNaUJqrktfZiN39YS0RbWKjFofj5vS_7AeKC2ak</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Automatic extraction of positive cells in pathology images of meningioma based on the maximal entropy principle and HSV color space</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Anari, V. ; Mahzouni, P. ; Amirfattahi, R.</creator><creatorcontrib>Anari, V. ; Mahzouni, P. ; Amirfattahi, R.</creatorcontrib><description>This paper describes a computer-aided system for analyzing immunohistochemically stained meningioma cancer cell images. Accurate segmentation of cells in such images plays a critical role in diagnosing different type of meningioma cancer. The methodpresented to automatically extract the positive cells in meninigioma tumor immunohistochemical pathology images based on HSV color space. First, according to distribution rules of positive cells in the HSV color space, it uses the component H, S and V as threshold conditions and leverages the maximal entropy principle to build a model to segment and extract positive cells. Experimental results shows that proposed algorithm can be used by pathologist to detection reliable quantitatively analyze the parameter of tumor cells and over come to disadvantages of the traditional approach.</description><identifier>ISSN: 2166-6776</identifier><identifier>ISBN: 142449706X</identifier><identifier>ISBN: 9781424497065</identifier><identifier>EISBN: 1424497078</identifier><identifier>EISBN: 9781424497072</identifier><identifier>EISBN: 9781424497089</identifier><identifier>EISBN: 1424497086</identifier><identifier>DOI: 10.1109/IranianMVIP.2010.5941150</identifier><language>eng</language><publisher>IEEE</publisher><subject>Color Segmentation ; Entropy ; HSV Color Space ; Image color analysis ; Image segmentation ; Immune system ; Immunohistochemistry ; Maximal Entropy Principle ; Meningioma ; Microscopy ; Positive Cell ; Thresholding ; Tumors</subject><ispartof>2010 6th Iranian Conference on Machine Vision and Image Processing, 2010, p.1-4</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5941150$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>310,311,781,785,790,791,2059,27930,54925</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5941150$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Anari, V.</creatorcontrib><creatorcontrib>Mahzouni, P.</creatorcontrib><creatorcontrib>Amirfattahi, R.</creatorcontrib><title>Automatic extraction of positive cells in pathology images of meningioma based on the maximal entropy principle and HSV color space</title><title>2010 6th Iranian Conference on Machine Vision and Image Processing</title><addtitle>IranianMVIP</addtitle><description>This paper describes a computer-aided system for analyzing immunohistochemically stained meningioma cancer cell images. Accurate segmentation of cells in such images plays a critical role in diagnosing different type of meningioma cancer. The methodpresented to automatically extract the positive cells in meninigioma tumor immunohistochemical pathology images based on HSV color space. First, according to distribution rules of positive cells in the HSV color space, it uses the component H, S and V as threshold conditions and leverages the maximal entropy principle to build a model to segment and extract positive cells. Experimental results shows that proposed algorithm can be used by pathologist to detection reliable quantitatively analyze the parameter of tumor cells and over come to disadvantages of the traditional approach.</description><subject>Color Segmentation</subject><subject>Entropy</subject><subject>HSV Color Space</subject><subject>Image color analysis</subject><subject>Image segmentation</subject><subject>Immune system</subject><subject>Immunohistochemistry</subject><subject>Maximal Entropy Principle</subject><subject>Meningioma</subject><subject>Microscopy</subject><subject>Positive Cell</subject><subject>Thresholding</subject><subject>Tumors</subject><issn>2166-6776</issn><isbn>142449706X</isbn><isbn>9781424497065</isbn><isbn>1424497078</isbn><isbn>9781424497072</isbn><isbn>9781424497089</isbn><isbn>1424497086</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFkEFLw0AQhVdUUGt_gZf5A6m7yWaTPZaitlBRsBRvZbqZtCvJJmRXac_-cVcs-C7DDO99PIYxEHwiBNf3iwGdRfe8XrxOUh6vuZZC5PyM3QiZSqkLXpTn_4t6v2DXqVAqUUWhrtjY-w8epZTWXF6z7-ln6FoM1gAdwoAm2M5BV0PfeRvsF4GhpvFgHfQY9l3T7Y5gW9yR_3W15Kzb2UiALXqqIIbDnqDFQzQ1QC4MXX-EfrDO2L4hQFfB_G0NJqIG8D0aumWXNTaexqc5YqvHh9Vsnixfnhaz6TKxmodEikwbFKWOyrmsq5IEap4pTHNO5VbVKKQuC8XzgkyVqq0ukVNaUJqrktfZiN39YS0RbWKjFofj5vS_7AeKC2ak</recordid><startdate>201010</startdate><enddate>201010</enddate><creator>Anari, V.</creator><creator>Mahzouni, P.</creator><creator>Amirfattahi, R.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201010</creationdate><title>Automatic extraction of positive cells in pathology images of meningioma based on the maximal entropy principle and HSV color space</title><author>Anari, V. ; Mahzouni, P. ; Amirfattahi, R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-4139ca189999504fd8e1a9036a250e8b6fa149876057ecd26b98a0e27e25680f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Color Segmentation</topic><topic>Entropy</topic><topic>HSV Color Space</topic><topic>Image color analysis</topic><topic>Image segmentation</topic><topic>Immune system</topic><topic>Immunohistochemistry</topic><topic>Maximal Entropy Principle</topic><topic>Meningioma</topic><topic>Microscopy</topic><topic>Positive Cell</topic><topic>Thresholding</topic><topic>Tumors</topic><toplevel>online_resources</toplevel><creatorcontrib>Anari, V.</creatorcontrib><creatorcontrib>Mahzouni, P.</creatorcontrib><creatorcontrib>Amirfattahi, R.</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 Electronic Library (IEL)</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>Anari, V.</au><au>Mahzouni, P.</au><au>Amirfattahi, R.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Automatic extraction of positive cells in pathology images of meningioma based on the maximal entropy principle and HSV color space</atitle><btitle>2010 6th Iranian Conference on Machine Vision and Image Processing</btitle><stitle>IranianMVIP</stitle><date>2010-10</date><risdate>2010</risdate><spage>1</spage><epage>4</epage><pages>1-4</pages><issn>2166-6776</issn><isbn>142449706X</isbn><isbn>9781424497065</isbn><eisbn>1424497078</eisbn><eisbn>9781424497072</eisbn><eisbn>9781424497089</eisbn><eisbn>1424497086</eisbn><abstract>This paper describes a computer-aided system for analyzing immunohistochemically stained meningioma cancer cell images. Accurate segmentation of cells in such images plays a critical role in diagnosing different type of meningioma cancer. The methodpresented to automatically extract the positive cells in meninigioma tumor immunohistochemical pathology images based on HSV color space. First, according to distribution rules of positive cells in the HSV color space, it uses the component H, S and V as threshold conditions and leverages the maximal entropy principle to build a model to segment and extract positive cells. Experimental results shows that proposed algorithm can be used by pathologist to detection reliable quantitatively analyze the parameter of tumor cells and over come to disadvantages of the traditional approach.</abstract><pub>IEEE</pub><doi>10.1109/IranianMVIP.2010.5941150</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 2166-6776 |
| ispartof | 2010 6th Iranian Conference on Machine Vision and Image Processing, 2010, p.1-4 |
| issn | 2166-6776 |
| language | eng |
| recordid | cdi_ieee_primary_5941150 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Color Segmentation Entropy HSV Color Space Image color analysis Image segmentation Immune system Immunohistochemistry Maximal Entropy Principle Meningioma Microscopy Positive Cell Thresholding Tumors |
| title | Automatic extraction of positive cells in pathology images of meningioma based on the maximal entropy principle and HSV color space |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-12T08%3A54%3A52IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Automatic%20extraction%20of%20positive%20cells%20in%20pathology%20images%20of%20meningioma%20based%20on%20the%20maximal%20entropy%20principle%20and%20HSV%20color%20space&rft.btitle=2010%206th%20Iranian%20Conference%20on%20Machine%20Vision%20and%20Image%20Processing&rft.au=Anari,%20V.&rft.date=2010-10&rft.spage=1&rft.epage=4&rft.pages=1-4&rft.issn=2166-6776&rft.isbn=142449706X&rft.isbn_list=9781424497065&rft_id=info:doi/10.1109/IranianMVIP.2010.5941150&rft_dat=%3Cieee_6IE%3E5941150%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=1424497078&rft.eisbn_list=9781424497072&rft.eisbn_list=9781424497089&rft.eisbn_list=1424497086&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5941150&rfr_iscdi=true |