46 resultados para medical image segmentation
Filtro por publicador
- Aberystwyth University Repository - Reino Unido (7)
- Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España (1)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (4)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (3)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (5)
- Aston University Research Archive (13)
- Biblioteca de Teses e Dissertações da USP (3)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (15)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (10)
- Biblioteca Digital de Teses e Dissertações Eletrônicas da UERJ (1)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (91)
- Boston University Digital Common (10)
- Bulgarian Digital Mathematics Library at IMI-BAS (7)
- Cambridge University Engineering Department Publications Database (66)
- CentAUR: Central Archive University of Reading - UK (14)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (17)
- Cochin University of Science & Technology (CUSAT), India (12)
- CORA - Cork Open Research Archive - University College Cork - Ireland (1)
- Dalarna University College Electronic Archive (3)
- Digital Commons - Michigan Tech (1)
- Digital Commons at Florida International University (32)
- Digital Peer Publishing (2)
- DigitalCommons@The Texas Medical Center (23)
- DRUM (Digital Repository at the University of Maryland) (1)
- Duke University (3)
- Greenwich Academic Literature Archive - UK (1)
- Helda - Digital Repository of University of Helsinki (3)
- Indian Institute of Science - Bangalore - Índia (29)
- Instituto Politécnico de Leiria (2)
- Instituto Politécnico do Porto, Portugal (1)
- Lume - Repositório Digital da Universidade Federal do Rio Grande do Sul (2)
- Massachusetts Institute of Technology (11)
- National Center for Biotechnology Information - NCBI (3)
- Nottingham eTheses (1)
- Publishing Network for Geoscientific & Environmental Data (2)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (50)
- Queensland University of Technology - ePrints Archive (100)
- Repositório Alice (Acesso Livre à Informação Científica da Embrapa / Repository Open Access to Scientific Information from Embrapa) (2)
- Repositório Institucional da Universidade Tecnológica Federal do Paraná (RIUT) (1)
- Repositorio Institucional de la Universidad de Málaga (2)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (46)
- Repositorio Institucional Universidad EAFIT - Medelin - Colombia (1)
- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (1)
- SAPIENTIA - Universidade do Algarve - Portugal (1)
- Scielo Uruguai (2)
- Universidad de Alicante (5)
- Universidad Politécnica de Madrid (53)
- Universidade Complutense de Madrid (2)
- Universidade Federal do Pará (1)
- Universidade Federal do Rio Grande do Norte (UFRN) (12)
- Universitat de Girona, Spain (17)
- Universitätsbibliothek Kassel, Universität Kassel, Germany (1)
- Université de Montréal, Canada (8)
- University of Canberra Research Repository - Australia (1)
- University of Michigan (165)
- University of Queensland eSpace - Australia (17)
- University of Washington (4)
- WestminsterResearch - UK (3)
Resumo:
This paper proposes a method for segmentation of cell nuclei regions in epithelium of prostate glands. This structure provides information to diagnosis and prognosis of prostate cancer. In the initial step, the contrast stretching technique was applied in image in order to improve the contrast between regions of interest and other regions. After, the global thresholding technique was applied and the value of threshold was defined empirically. Finally, the false positive regions were removed using the connected components technique. The performance of the proposed method was compared with the Otsu technique and statistical measures of accuracy were calculated based on reference images (gold standard). The result of the mean value of accuracy of proposed method was 93% ± 0.07.