Pattern Recognition in Macroscopic and Dermoscopic Images for Skin Lesion Diagnosis

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Roberta B. Oliveira (Author) Other; Aledir S. Pereira (Author) Other; João Manuel R. S. Tavares (Author) FEUP

Conference proceedings International

Title: European Congress on Computational Methods in Applied Sciences and Engineering: ECCOMAS 2017: VipIMAGE 2017

Pages: 504-514

6th ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing (VipIMAGE)

Porto, PORTUGAL, OCT 18-20, 2017

Indexing

ISI Web of Knowledge - 6 Citations

ISI Web of Science

Scopus - 8 Citations

Scientific classification

CORDIS: Technological sciences

FOS: Engineering and technology

Other information

Authenticus ID: P-00N-5X1

DOI: 10.1007/978-3-319-68195-5_55

Abstract (EN): Pattern recognition in macroscopic and dermoscopic images is a challenging task in skin lesion diagnosis. The search for better performing classification has been a relevant issue for pattern recognition in images. Hence, this work was particularly focused on skin lesion pattern recognition, especially in macroscopic and dermoscopic images. For the pattern recognition in macroscopic images, a computational approach was developed to detect skin lesion features according to the asymmetry, border, colour and texture properties, as well as to diagnose types of skin lesions, i.e., nevus, seborrheic keratosis and melanoma. In this approach, an anisotropic diffusion filter is applied to enhance the input image and an active contour model without edges is used in the segmentation of the enhanced image. Finally, a support vector machine is used to classify each feature property according to their clinical principles, and also for the classification between different types of skin lesions. For the pattern recognition in dermoscopic images, classification models based on ensemble methods and input feature manipulation are used. The feature subsets was used to manipulate the input feature and to ensure the diversity of the ensemble models. Each ensemble classification model was generated by using an optimum-path forest classifier and integrated with a majority voting strategy. The performed experiments allowed to analyse the effectiveness of the developed approaches for pattern recognition in macroscopic and dermoscopic images, with the results obtained being very promising.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11