Skin Lesion Assessment based on Plenoptic Images for Melanoma Classification.
		    Titulo anterior:
		    Texture and Patterns in 3D plenoptic skin lesion assessment: A taxonomy proposal for melanoma classification

Publications

Dermoscopic skin lesion image segmentation based on Local Binary Pattern Clustering: Comparative study

Publication . Pereira, Pedro M. M.; Fonseca-Pinto, Rui; Paiva, Rui Pedro; Assuncao, Pedro A. A.; Tavora, Luis M. N.; Thomaz, Lucas A.; Faria, Sergio M. M.

Accurate skin lesion segmentation is important for identification and classification through computational methods. However, when performed by dermatologists, the results of clinical segmentation are affected by a certain margin of inaccuracy (which exists since dermatologist do not delineate lesions for segmentation but for extraction) and also significant inter- and intra-individual variability, such segmentation is not sufficiently accurate for segmentation studies. This work addresses these limitations to enable detailed analysis of lesions’ geometry along with extraction of non-linear characteristics of region-of-interest border lines. A comprehensive review of 39 segmentation methods is carried out and a contribution to improve dermoscopic image segmentation is presented to determine the regions-of-interest of skin lesions, through accurate border lines with fine geometric details. This approach resorts to Local Binary Patterns and k-means clustering for precise identification of lesions boundaries, particularly the melanocytic. A comparative evaluation study is carried out using three different datasets and reviewed algorithms are grouped according to their approach. Results show that algorithms from the same group tend to perform similarly. Nevertheless, their performance does not depend uniquely on the algorithm itself but also on the underlying dataset characteristics. Throughout several evaluations, the proposed Local Binary Patterns method presents, consistently, better average performance than the current state-of-the-art techniques across the three different datasets without the need of training or supervised learning steps. Overall, apart from presenting a new segmentation method capable of outperforming the current state-of-the-art, this paper provides insightful information about the behaviour and performance of different image segmentation algorithms.

2020-05Journal article

Open access

Skin lesion classification enhancement using border-line features – The melanoma vs nevus problem

Publication . Pereira, Pedro M. M.; Fonseca-Pinto, Rui; Paiva, Rui Pedro; Assuncao, Pedro A. A.; Tavora, Luis M. N.; Thomaz, Lucas A.; Faria, Sergio M. M.

Machine learning algorithms are progressively assuming an important role as a computational tool to support clinical diagnosis, namely in the classification of pigmented skin lesions. The current classification methods commonly rely on features derived from shape, colour, or texture, obtained after image segmentation, but these do not always guarantee the best results. To improve the classification accuracy, this work proposes to further exploit the border-line characteristics of the lesion segmentation mask, by combining gradients with local binary patterns (LBP). In the proposed method, these border-line features are used together with the conventional ones to enhance the performance of skin lesion classification algorithms. When the new features are combined with the classical ones, the experimental results show higher accuracy, which impacts positively the overall performance of the classification algorithms. As the medical image datasets usually present large class imbalance, which results in low sensitivity for the classifiers, the border-line features have a positive impact on this classification metric, as evidenced by the experimental results. Both the features’ usefulness and their impact are assessed in regard to the classification results, which in turn are statistically tested for completeness, using three different classifiers and two medical image datasets.

2020-03Journal article

Open access