Decision Trees for the Detection of Skin Lesion Patterns in Lower Limbs Ulcers

• Published in: 2016 International Conference on Computational Science and Computational Intelligence (CSCI) pp. 677 - 681
• Main Authors: Seixas, Jose Luis, Mantovani, Rafael G.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2016
• Subjects: Decision trees; Image color analysis; Image segmentation; Lesions; machine learning; Medical diagnostic imaging; seed; Skin
• DOI: 10.1109/CSCI.2016.0133

Misleading diagnosis of skin diseases can result in complications during the healing process. Skin images provide important information for the medical staff for information storage and exchange, to trying to prevent this misdiagnosis from happening. For such, a good segmentation process is needed. The segmentation of these images is already being used and has been an effective tool for skin diseases recognition. This paper presents a method for targeting seeds for region growing algorithms, as several of region growing algorithms have good clustering results, but are sensitive to seed. Machine learning were use to create the seed for segmentation of medical images of skin ulcers in the lower limbs. For machine learning, decision tree algorithms were used, which bring a more intuitive approach. The results were compared with gold standard obtained with the help of experts, the results were good and opened paths that can be followed for further work since, even though good results, they can still be improved.