M Hossein Jafari1, Ebrahim Nasr-Esfahani1, Nader Karimi1, S M Reza Soroushmehr2,3, Shadrokh Samavi4,5, Kayvan Najarian2,3,6. 1. Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran. 2. Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI, USA. 3. Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, USA. 4. Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran. samavi@mcmaster.ca. 5. Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, USA. samavi@mcmaster.ca. 6. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Abstract
PURPOSE: Computerized prescreening of suspicious moles and lesions for malignancy is of great importance for assessing the need and the priority of the removal surgery. Detection can be done by images captured by standard cameras, which are more preferable due to low cost and availability. One important step in computerized evaluation is accurate detection of lesion's region, i.e., segmentation of an image into two regions as lesion and normal skin. METHODS: In this paper, a new method based on deep neural networks is proposed for accurate extraction of a lesion region. The input image is preprocessed, and then, its patches are fed to a convolutional neural network. Local texture and global structure of the patches are processed in order to assign pixels to lesion or normal classes. A method for effective selection of training patches is proposed for more accurate detection of a lesion's border. RESULTS: Our results indicate that the proposed method could reach the accuracy of 98.7% and the sensitivity of 95.2% in segmentation of lesion regions over the dataset of clinical images. CONCLUSION: The experimental results of qualitative and quantitative evaluations demonstrate that our method can outperform other state-of-the-art algorithms exist in the literature.
PURPOSE: Computerized prescreening of suspicious moles and lesions for malignancy is of great importance for assessing the need and the priority of the removal surgery. Detection can be done by images captured by standard cameras, which are more preferable due to low cost and availability. One important step in computerized evaluation is accurate detection of lesion's region, i.e., segmentation of an image into two regions as lesion and normal skin. METHODS: In this paper, a new method based on deep neural networks is proposed for accurate extraction of a lesion region. The input image is preprocessed, and then, its patches are fed to a convolutional neural network. Local texture and global structure of the patches are processed in order to assign pixels to lesion or normal classes. A method for effective selection of training patches is proposed for more accurate detection of a lesion's border. RESULTS: Our results indicate that the proposed method could reach the accuracy of 98.7% and the sensitivity of 95.2% in segmentation of lesion regions over the dataset of clinical images. CONCLUSION: The experimental results of qualitative and quantitative evaluations demonstrate that our method can outperform other state-of-the-art algorithms exist in the literature.
Entities:
Keywords:
Convolutional neural network; Deep learning; Medical image segmentation; Melanoma excision; Skin cancer
Authors: E Nasr-Esfahani; S Samavi; N Karimi; S M R Soroushmehr; K Ward; M H Jafari; B Felfeliyan; B Nallamothu; K Najarian Journal: Conf Proc IEEE Eng Med Biol Soc Date: 2016-08
Authors: F Nachbar; W Stolz; T Merkle; A B Cognetta; T Vogt; M Landthaler; P Bilek; O Braun-Falco; G Plewig Journal: J Am Acad Dermatol Date: 1994-04 Impact factor: 11.527
Authors: Mohammad Havaei; Axel Davy; David Warde-Farley; Antoine Biard; Aaron Courville; Yoshua Bengio; Chris Pal; Pierre-Marc Jodoin; Hugo Larochelle Journal: Med Image Anal Date: 2016-05-19 Impact factor: 8.545
Authors: Travers Ching; Daniel S Himmelstein; Brett K Beaulieu-Jones; Alexandr A Kalinin; Brian T Do; Gregory P Way; Enrico Ferrero; Paul-Michael Agapow; Michael Zietz; Michael M Hoffman; Wei Xie; Gail L Rosen; Benjamin J Lengerich; Johnny Israeli; Jack Lanchantin; Stephen Woloszynek; Anne E Carpenter; Avanti Shrikumar; Jinbo Xu; Evan M Cofer; Christopher A Lavender; Srinivas C Turaga; Amr M Alexandari; Zhiyong Lu; David J Harris; Dave DeCaprio; Yanjun Qi; Anshul Kundaje; Yifan Peng; Laura K Wiley; Marwin H S Segler; Simina M Boca; S Joshua Swamidass; Austin Huang; Anthony Gitter; Casey S Greene Journal: J R Soc Interface Date: 2018-04 Impact factor: 4.293