Accurate segmentation of inflammatory and abnormal regions using medical thermal imagery. View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04-05

AUTHORS

Kakali Das, Mrinal Kanti Bhowmik, Omkar Chowdhuary, Debotosh Bhattacharjee, Barin Kumar De

ABSTRACT

Methodologies reported in the existing literature for identification of a region of interest (ROI) in medical thermograms suffer from over- and under-extraction of the abnormal and/or inflammatory region, thereby causing inaccurate diagnoses of the spread of an abnormality. We overcome this limitation by exploiting the advantages of a logarithmic transformation. Our algorithm extends the conventional region growing segmentation technique with a modified similarity criteria and a stopping rule. In this method, the ROI is generated by taking common information from two independent regions produced by two different versions of a region-growing algorithm that use different parameters. An automatic multi-seed selection procedure prevents missed segmentations in the proposed approach. We validate our technique by experimentation on various thermal images of the inflammation of affected knees and abnormal breasts. The images were obtained from three databases, namely the Knee joint dataset, the DBT-TU-JU dataset, and the DMR-IR dataset. The superiority of the proposed technique is established by comparison to the performance of state-of-the-art competing methodologies. This study performed temperature emitted inflammatory area segmentation on thermal images of knees and breasts. The proposed segmentation method is of potential value in thermal image processing applications that require expediency and automation. More... »

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s13246-019-00753-6

DOI

http://dx.doi.org/10.1007/s13246-019-00753-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1113261845

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30953251


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Tripura University", 
          "id": "https://www.grid.ac/institutes/grid.444729.8", 
          "name": [
            "Computer Science and Engineering, Tripura University, Suryamaninagar, Tripura, 799022, India. kakalids54@gmail.com."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Das", 
        "givenName": "Kakali", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tripura University", 
          "id": "https://www.grid.ac/institutes/grid.444729.8", 
          "name": [
            "Computer Science and Engineering, Tripura University, Suryamaninagar, Tripura, 799022, India."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bhowmik", 
        "givenName": "Mrinal Kanti", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tripura University", 
          "id": "https://www.grid.ac/institutes/grid.444729.8", 
          "name": [
            "Computer Science and Engineering, Tripura University, Suryamaninagar, Tripura, 799022, India."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chowdhuary", 
        "givenName": "Omkar", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Jadavpur University", 
          "id": "https://www.grid.ac/institutes/grid.216499.1", 
          "name": [
            "Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bhattacharjee", 
        "givenName": "Debotosh", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tripura University", 
          "id": "https://www.grid.ac/institutes/grid.444729.8", 
          "name": [
            "Department of Physics, Tripura University, Suryamaninagar, Tripura, 799022, India."
          ], 
          "type": "Organization"
        }, 
        "familyName": "De", 
        "givenName": "Barin Kumar", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1201/b12938-14", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003014002"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1166/jmihi.2014.1226", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003636482"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10916-008-9213-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008293649", 
          "https://doi.org/10.1007/s10916-008-9213-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00371-014-0963-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009426420", 
          "https://doi.org/10.1007/s00371-014-0963-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2177/jsci.39.497", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025434988"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1042/cs1040247", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035362788"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1042/cs1040247", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035362788"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/193229681000400414", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040729610"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/193229681000400414", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040729610"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.infrared.2012.03.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044080928"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1201/b12938-17", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048860639"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00371-010-0536-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050583069", 
          "https://doi.org/10.1007/s00371-010-0536-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ijthermalsci.2013.03.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051276633"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0031-9155/18/5/307", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059019702"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0967-3334/29/4/007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059123278"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/34.295913", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061156009"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/42.746635", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061170720"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/42.750259", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061170739"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tbme.2012.2227478", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061529034"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tbme.2013.2267212", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061529308"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tbme.2014.2363173", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061529694"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tgrs.2013.2260552", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061612977"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tip.2016.2624198", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061645296"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tmi.2015.2418031", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061696515"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1118/1.4958282", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062226972"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/0954411915580809", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063888920"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/0954411915580809", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063888920"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00371-017-1379-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085131273", 
          "https://doi.org/10.1007/s00371-017-1379-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00371-017-1379-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085131273", 
          "https://doi.org/10.1007/s00371-017-1379-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/jbhi.2017.2740500", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091268330"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iic.2015.7151001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093636981"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/memea.2008.4542999", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094037363"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/isacc.2015.7377350", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094237134"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iembs.1995.579902", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094423115"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/isacc.2015.7377351", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094701260"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/elmar.2015.7334485", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095685746"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-04-05", 
    "datePublishedReg": "2019-04-05", 
    "description": "Methodologies reported in the existing literature for identification of a region of interest (ROI) in medical thermograms suffer from over- and under-extraction of the abnormal and/or inflammatory region, thereby causing inaccurate diagnoses of the spread of an abnormality. We overcome this limitation by exploiting the advantages of a logarithmic transformation. Our algorithm extends the conventional region growing segmentation technique with a modified similarity criteria and a stopping rule. In this method, the ROI is generated by taking common information from two independent regions produced by two different versions of a region-growing algorithm that use different parameters. An automatic multi-seed selection procedure prevents missed segmentations in the proposed approach. We validate our technique by experimentation on various thermal images of the inflammation of affected knees and abnormal breasts. The images were obtained from three databases, namely the Knee joint dataset, the DBT-TU-JU dataset, and the DMR-IR dataset. The superiority of the proposed technique is established by comparison to the performance of state-of-the-art competing methodologies. This study performed temperature emitted inflammatory area segmentation on thermal images of knees and breasts. The proposed segmentation method is of potential value in thermal image processing applications that require expediency and automation.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s13246-019-00753-6", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1093172", 
        "issn": [
          "0158-9938", 
          "1879-5447"
        ], 
        "name": "Australasian Physical & Engineering Sciences in Medicine", 
        "type": "Periodical"
      }
    ], 
    "name": "Accurate segmentation of inflammatory and abnormal regions using medical thermal imagery.", 
    "productId": [
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30953251"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "8208130"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s13246-019-00753-6"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1113261845"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s13246-019-00753-6", 
      "https://app.dimensions.ai/details/publication/pub.1113261845"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T14:20", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000372_0000000372/records_117121_00000003.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/s13246-019-00753-6"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s13246-019-00753-6'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s13246-019-00753-6'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s13246-019-00753-6'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s13246-019-00753-6'


 

This table displays all metadata directly associated to this object as RDF triples.

186 TRIPLES      20 PREDICATES      56 URIs      16 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s13246-019-00753-6 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author Nd2a8b5670ea64106905a4e6963898575
4 schema:citation sg:pub.10.1007/s00371-010-0536-9
5 sg:pub.10.1007/s00371-014-0963-0
6 sg:pub.10.1007/s00371-017-1379-4
7 sg:pub.10.1007/s10916-008-9213-1
8 https://doi.org/10.1016/j.ijthermalsci.2013.03.001
9 https://doi.org/10.1016/j.infrared.2012.03.002
10 https://doi.org/10.1042/cs1040247
11 https://doi.org/10.1088/0031-9155/18/5/307
12 https://doi.org/10.1088/0967-3334/29/4/007
13 https://doi.org/10.1109/34.295913
14 https://doi.org/10.1109/42.746635
15 https://doi.org/10.1109/42.750259
16 https://doi.org/10.1109/elmar.2015.7334485
17 https://doi.org/10.1109/iembs.1995.579902
18 https://doi.org/10.1109/iic.2015.7151001
19 https://doi.org/10.1109/isacc.2015.7377350
20 https://doi.org/10.1109/isacc.2015.7377351
21 https://doi.org/10.1109/jbhi.2017.2740500
22 https://doi.org/10.1109/memea.2008.4542999
23 https://doi.org/10.1109/tbme.2012.2227478
24 https://doi.org/10.1109/tbme.2013.2267212
25 https://doi.org/10.1109/tbme.2014.2363173
26 https://doi.org/10.1109/tgrs.2013.2260552
27 https://doi.org/10.1109/tip.2016.2624198
28 https://doi.org/10.1109/tmi.2015.2418031
29 https://doi.org/10.1118/1.4958282
30 https://doi.org/10.1166/jmihi.2014.1226
31 https://doi.org/10.1177/0954411915580809
32 https://doi.org/10.1177/193229681000400414
33 https://doi.org/10.1201/b12938-14
34 https://doi.org/10.1201/b12938-17
35 https://doi.org/10.2177/jsci.39.497
36 schema:datePublished 2019-04-05
37 schema:datePublishedReg 2019-04-05
38 schema:description Methodologies reported in the existing literature for identification of a region of interest (ROI) in medical thermograms suffer from over- and under-extraction of the abnormal and/or inflammatory region, thereby causing inaccurate diagnoses of the spread of an abnormality. We overcome this limitation by exploiting the advantages of a logarithmic transformation. Our algorithm extends the conventional region growing segmentation technique with a modified similarity criteria and a stopping rule. In this method, the ROI is generated by taking common information from two independent regions produced by two different versions of a region-growing algorithm that use different parameters. An automatic multi-seed selection procedure prevents missed segmentations in the proposed approach. We validate our technique by experimentation on various thermal images of the inflammation of affected knees and abnormal breasts. The images were obtained from three databases, namely the Knee joint dataset, the DBT-TU-JU dataset, and the DMR-IR dataset. The superiority of the proposed technique is established by comparison to the performance of state-of-the-art competing methodologies. This study performed temperature emitted inflammatory area segmentation on thermal images of knees and breasts. The proposed segmentation method is of potential value in thermal image processing applications that require expediency and automation.
39 schema:genre research_article
40 schema:inLanguage en
41 schema:isAccessibleForFree false
42 schema:isPartOf sg:journal.1093172
43 schema:name Accurate segmentation of inflammatory and abnormal regions using medical thermal imagery.
44 schema:productId N0ec4b989487b42cdace0d92f5e195356
45 N4606cfb8ffe749afb28a3ec7cb423dc4
46 N49f401104e344250b60820b4a387fe6e
47 Nc06eefdbdb9d4ed3a0041776c02548e7
48 schema:sameAs https://app.dimensions.ai/details/publication/pub.1113261845
49 https://doi.org/10.1007/s13246-019-00753-6
50 schema:sdDatePublished 2019-04-11T14:20
51 schema:sdLicense https://scigraph.springernature.com/explorer/license/
52 schema:sdPublisher Nbfb05ccad5174fe29135c17a6c1e7065
53 schema:url http://link.springer.com/10.1007/s13246-019-00753-6
54 sgo:license sg:explorer/license/
55 sgo:sdDataset articles
56 rdf:type schema:ScholarlyArticle
57 N09127f65c2594932beaf156c6de14a95 schema:affiliation https://www.grid.ac/institutes/grid.444729.8
58 schema:familyName Das
59 schema:givenName Kakali
60 rdf:type schema:Person
61 N0ec4b989487b42cdace0d92f5e195356 schema:name pubmed_id
62 schema:value 30953251
63 rdf:type schema:PropertyValue
64 N10f74530ccbe4e50888f8d709737c61f rdf:first Nab74120c829d4760b2cef9bbf98028d8
65 rdf:rest N9a5c8c69f878485ba010b8ae8f710b27
66 N3832aff10a134a219ae6bb55299a0437 schema:affiliation https://www.grid.ac/institutes/grid.216499.1
67 schema:familyName Bhattacharjee
68 schema:givenName Debotosh
69 rdf:type schema:Person
70 N4606cfb8ffe749afb28a3ec7cb423dc4 schema:name nlm_unique_id
71 schema:value 8208130
72 rdf:type schema:PropertyValue
73 N49f401104e344250b60820b4a387fe6e schema:name doi
74 schema:value 10.1007/s13246-019-00753-6
75 rdf:type schema:PropertyValue
76 N67386a24b277492a90aa2d5cabcdc4c4 schema:affiliation https://www.grid.ac/institutes/grid.444729.8
77 schema:familyName De
78 schema:givenName Barin Kumar
79 rdf:type schema:Person
80 N73c8e18f4cbf4076989bca7f0b77c63b rdf:first N67386a24b277492a90aa2d5cabcdc4c4
81 rdf:rest rdf:nil
82 N7f1005baf4d14ad783306ed9351b656f schema:affiliation https://www.grid.ac/institutes/grid.444729.8
83 schema:familyName Bhowmik
84 schema:givenName Mrinal Kanti
85 rdf:type schema:Person
86 N9a5c8c69f878485ba010b8ae8f710b27 rdf:first N3832aff10a134a219ae6bb55299a0437
87 rdf:rest N73c8e18f4cbf4076989bca7f0b77c63b
88 Nab74120c829d4760b2cef9bbf98028d8 schema:affiliation https://www.grid.ac/institutes/grid.444729.8
89 schema:familyName Chowdhuary
90 schema:givenName Omkar
91 rdf:type schema:Person
92 Nbfb05ccad5174fe29135c17a6c1e7065 schema:name Springer Nature - SN SciGraph project
93 rdf:type schema:Organization
94 Nc06eefdbdb9d4ed3a0041776c02548e7 schema:name dimensions_id
95 schema:value pub.1113261845
96 rdf:type schema:PropertyValue
97 Nd2a8b5670ea64106905a4e6963898575 rdf:first N09127f65c2594932beaf156c6de14a95
98 rdf:rest Ne0daf2a5df454b6789fe7a84bd2a124f
99 Ne0daf2a5df454b6789fe7a84bd2a124f rdf:first N7f1005baf4d14ad783306ed9351b656f
100 rdf:rest N10f74530ccbe4e50888f8d709737c61f
101 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
102 schema:name Information and Computing Sciences
103 rdf:type schema:DefinedTerm
104 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
105 schema:name Artificial Intelligence and Image Processing
106 rdf:type schema:DefinedTerm
107 sg:journal.1093172 schema:issn 0158-9938
108 1879-5447
109 schema:name Australasian Physical & Engineering Sciences in Medicine
110 rdf:type schema:Periodical
111 sg:pub.10.1007/s00371-010-0536-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050583069
112 https://doi.org/10.1007/s00371-010-0536-9
113 rdf:type schema:CreativeWork
114 sg:pub.10.1007/s00371-014-0963-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009426420
115 https://doi.org/10.1007/s00371-014-0963-0
116 rdf:type schema:CreativeWork
117 sg:pub.10.1007/s00371-017-1379-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085131273
118 https://doi.org/10.1007/s00371-017-1379-4
119 rdf:type schema:CreativeWork
120 sg:pub.10.1007/s10916-008-9213-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008293649
121 https://doi.org/10.1007/s10916-008-9213-1
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1016/j.ijthermalsci.2013.03.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051276633
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1016/j.infrared.2012.03.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044080928
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1042/cs1040247 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035362788
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1088/0031-9155/18/5/307 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059019702
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1088/0967-3334/29/4/007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059123278
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1109/34.295913 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061156009
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1109/42.746635 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061170720
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1109/42.750259 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061170739
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1109/elmar.2015.7334485 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095685746
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1109/iembs.1995.579902 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094423115
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1109/iic.2015.7151001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093636981
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1109/isacc.2015.7377350 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094237134
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1109/isacc.2015.7377351 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094701260
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1109/jbhi.2017.2740500 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091268330
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1109/memea.2008.4542999 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094037363
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1109/tbme.2012.2227478 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061529034
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1109/tbme.2013.2267212 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061529308
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1109/tbme.2014.2363173 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061529694
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1109/tgrs.2013.2260552 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061612977
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1109/tip.2016.2624198 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061645296
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1109/tmi.2015.2418031 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061696515
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1118/1.4958282 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062226972
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1166/jmihi.2014.1226 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003636482
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1177/0954411915580809 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063888920
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1177/193229681000400414 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040729610
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1201/b12938-14 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003014002
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1201/b12938-17 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048860639
176 rdf:type schema:CreativeWork
177 https://doi.org/10.2177/jsci.39.497 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025434988
178 rdf:type schema:CreativeWork
179 https://www.grid.ac/institutes/grid.216499.1 schema:alternateName Jadavpur University
180 schema:name Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India.
181 rdf:type schema:Organization
182 https://www.grid.ac/institutes/grid.444729.8 schema:alternateName Tripura University
183 schema:name Computer Science and Engineering, Tripura University, Suryamaninagar, Tripura, 799022, India.
184 Computer Science and Engineering, Tripura University, Suryamaninagar, Tripura, 799022, India. kakalids54@gmail.com.
185 Department of Physics, Tripura University, Suryamaninagar, Tripura, 799022, India.
186 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...