Decision Trees for the Detection of Skin Lesion Patterns in Lower Limbs Ulcers
- 1 December 2016
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in 2016 International Conference on Computational Science and Computational Intelligence (CSCI)
- p. 677-681
- https://doi.org/10.1109/csci.2016.0133
Abstract
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.Keywords
This publication has 14 references indexed in Scilit:
- Hyper-Parameter Tuning of a Decision Tree Induction AlgorithmPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Color energy as a seed descriptor for image segmentation with region growing algorithms on skin wound imagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- Superpixel based color contrast and color distribution driven salient object detectionSignal Processing: Image Communication, 2013
- A Survey of Evolutionary Algorithms for Decision-Tree InductionIEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 2011
- A Brief Survey of Color Image Preprocessing and Segmentation TechniquesJournal of Pattern Recognition Research, 2011
- Machine Learning in Medical ImagingIEEE Signal Processing Magazine, 2010
- A RBFN Perceptive Model for Image ThresholdingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Color image segmentation: advances and prospectsPattern Recognition, 2001
- Adaptive color segmentation-a comparison of neural and statistical methodsIEEE Transactions on Neural Networks, 1997
- Automatic white balance for digital still cameraIEEE Transactions on Consumer Electronics, 1995