Extraction of skin lesions from non-dermoscopic images for surgical excision of melanoma
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.
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.
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.
The experimental results of qualitative and quantitative evaluations demonstrate that our method can outperform other state-of-the-art algorithms exist in the literature.
KeywordsConvolutional neural network Deep learning Medical image segmentation Melanoma excision Skin cancer
Compliance with ethical standards
Conflict of interest
The authors declare that there is no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
This articles does not contain patient data.
- 1.American Cancer Society (2016) Cancer facts & figures 2016. American Cancer Society, AtlantaGoogle Scholar
- 3.Jerant AF, Johnson JT, Sheridan C, Caffrey TJ (2000) Early detection and treatment of skin cancer. Am Fam Physician 2:357–386Google Scholar
- 8.Jafari MH, Samavi S, Soroushmehr SMR, Mohaghegh H, Karimi N, Najarian K (2016) Set of descriptors for skin cancer diagnosis using non-dermoscopic color images. In: 2016 IEEE international conference on image processing (ICIP)Google Scholar
- 9.Glaister J (2013) Automatic segmentation of skin lesions from dermatological photographs. M.S. thesis Department of Systems Design Engineering, University of Waterloo, WaterlooGoogle Scholar
- 12.Cavalcanti P, Yari Y, Scharcanski J (2010) Pigmented skin lesion segmentation on macroscopic images. In: Procedings of 25th international conference on image vision computing, pp 1–7Google Scholar
- 13.Cavalcanti P, Scharcanski J, Lopes C (2010) Shading attenuation in human skin color images. In: 6th International symposium on advances in visual computing, pp 190–198Google Scholar
- 18.Melinščak M, Prentašić P, Lončarić S (2015) Retinal vessel segmentation using deep neural networks. In: 10th International conference on computer vision theory and applicationsGoogle Scholar
- 19.Nasr-Esfahani E, Samavi S, Karimi N, Soroushmehr SMR, Ward K, Jafari MH, Felfeliyan B, Najarian K (2016) Vessel extraction in X-ray angiograms using deep learning. In: 2016 38th Annual international conference of the IEEE engineering in medicine and biology society (EMBC)Google Scholar
- 21.Zhao R, Ouyang W, Li H, Wang X (2015) Saliency detection by multi-context deep learning. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 1265–1274Google Scholar
- 22.Jafari MH, Karimi N, Nasr-Esfahani E, Samavi S, Soroushmehr SMR, Ward K, Najarian K (2016) Skin lesion segmentation in clinical images using deep learning. In: 2016 23rd International conference on pattern recognition (ICPR)Google Scholar