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Author (down) David Guillamet; Jordi Vitria; B. Shiele edit  doi
openurl 
  Title Introducing a weighted non-negative matrix factorization for image classification Type Journal Article
  Year 2003 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 24 Issue 14 Pages 2447–2454  
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  Abstract IF: 0.809  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ GVS2003 Serial 382  
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Author (down) David Guillamet; Jordi Vitria edit  doi
openurl 
  Title Evaluation of distance metrics for recognition based on non-negative matrix factorization Type Journal Article
  Year 2003 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 24 Issue 9-10 Pages 1599 –1605  
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  Abstract IF: 0.809  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ GuV2003b Serial 380  
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Author (down) D. Seron; F. Moreso; C. Gratin; Jordi Vitria; E. Condom edit  url
openurl 
  Title Automated classification of renal interstitium and tubules by local texture analysis and a neural network Type Journal Article
  Year 1996 Publication Analytical and Quantitative Cytology and Histology Abbreviated Journal  
  Volume 18 Issue 5 Pages 410-9, PMID: 8908314  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ SMG1996 Serial 76  
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Author (down) Cristina Sanchez Montes; Jorge Bernal; Ana Garcia Rodriguez; Henry Cordova; Gloria Fernandez Esparrach edit  url
openurl 
  Title Revisión de métodos computacionales de detección y clasificación de pólipos en imagen de colonoscopia Type Journal Article
  Year 2020 Publication Gastroenterología y Hepatología Abbreviated Journal GH  
  Volume 43 Issue 4 Pages 222-232  
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  Abstract Computer-aided diagnosis (CAD) is a tool with great potential to help endoscopists in the tasks of detecting and histologically classifying colorectal polyps. In recent years, different technologies have been described and their potential utility has been increasingly evidenced, which has generated great expectations among scientific societies. However, most of these works are retrospective and use images of different quality and characteristics which are analysed off line. This review aims to familiarise gastroenterologists with computational methods and the particularities of endoscopic imaging, which have an impact on image processing analysis. Finally, the publicly available image databases, needed to compare and confirm the results obtained with different methods, are presented.  
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  Notes MV; Approved no  
  Call Number Admin @ si @ SBG2020 Serial 3404  
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Author (down) Cristina Sanchez Montes; F. Javier Sanchez; Jorge Bernal; Henry Cordova; Maria Lopez Ceron; Miriam Cuatrecasas; Cristina Rodriguez de Miguel; Ana Garcia Rodriguez; Rodrigo Garces Duran; Maria Pellise; Josep Llach; Gloria Fernandez Esparrach edit   pdf
doi  openurl
  Title Computer-aided Prediction of Polyp Histology on White-Light Colonoscopy using Surface Pattern Analysis Type Journal Article
  Year 2019 Publication Endoscopy Abbreviated Journal END  
  Volume 51 Issue 3 Pages 261-265  
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  Abstract Background and study aims: To evaluate a new computational histology prediction system based on colorectal polyp textural surface patterns using high definition white light images.
Patients and methods: Textural elements (textons) were characterized according to their contrast with respect to the surface, shape and number of bifurcations, assuming that dysplastic polyps are associated with highly contrasted, large tubular patterns with some degree of bifurcation. Computer-aided diagnosis (CAD) was compared with pathological diagnosis and the diagnosis by the endoscopists using Kudo and NICE classification.
Results: Images of 225 polyps were evaluated (142 dysplastic and 83 non-dysplastic). CAD system correctly classified 205 (91.1%) polyps, 131/142 (92.3%) dysplastic and 74/83 (89.2%) non-dysplastic. For the subgroup of 100 diminutive (<5 mm) polyps, CAD correctly classified 87 (87%) polyps, 43/50 (86%) dysplastic and 44/50 (88%) non-dysplastic. There were not statistically significant differences in polyp histology prediction based on CAD system and on endoscopist assessment.
Conclusion: A computer vision system based on the characterization of the polyp surface in the white light accurately predicts colorectal polyp histology.
 
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  Notes MV; 600.096; 600.119; 600.075 Approved no  
  Call Number Admin @ si @ SSB2019 Serial 3164  
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