Catarina Barata

Two Systems for the Detection of Melanomas in Dermoscopy Images Using Texture and Color Features

Melanoma is one of the deadliest forms of cancer; hence, great effort has been put into the development of diagnosis methods for this disease. This paper addresses two different systems for the detection of melanomas in dermoscopy images. The first system uses global methods to classify skin lesions, whereas the second system uses local features and the bag-of-features classifier. This paper aims at determining the best system for skin lesion classification. The other objective is to compare the role of color and texture features in lesion classification and determine which set of features is more discriminative. It is concluded that color features outperform texture features when used alone and that both methods achieve very good results, i.e., Sensitivity = 96% and Specificity = 80% for global methods against Sensitivity = 100% and Specificity = 75% for local methods. The classification results were obtained on a data set of 176 dermoscopy images from Hospital Pedro Hispano, Matosinhos.

A System for the Detection of Pigment Network in Dermoscopy Images Using Directional Filters

A pigment network is one of the most important dermoscopic structures. This paper describes an automatic system that performs its detection in dermoscopy images. The proposed system involves a set of sequential steps. First, a preprocessing algorithm is applied to the dermoscopy image. Then, a bank of directional filters and a connected component analysis are used in order to detect the “lines” of the pigment network. Finally, features are extracted from the detected network and used to train an AdaBoost algorithm to classify each lesion regarding the presence of the pigment network. The algorithm was tested on a dataset of 200 medically annotated images from the database of Hospital Pedro Hispano (Matosinhos), achieving a sensitivity = 91.1% and a specificity = 82.1%.

Improving Dermoscopy Image Classification Using Color Constancy

Robustness is one of the most important characteristics of computer-aided diagnosis systems designed for dermoscopy images. However, it is difficult to ensure this characteristic if the systems operate with multisource images acquired under different setups. Changes in the illumination and acquisition devices alter the color of images and often reduce the performance of the systems. Thus, it is important to normalize the colors of dermoscopy images before training and testing any system. In this paper, we investigate four color constancy algorithms: Gray World, max-RGB, Shades of Gray, and General Gray World. Our results show that color constancy improves the classification of multisource images, increasing the sensitivity of a bag-of-features system from 71.0% to 79.7% and the specificity from 55.2% to 76% using only 1-D RGB histograms as features.

Color identification in dermoscopy images using Gaussian mixture models

Development of Computer Aided Diagnosis systems that mimic the performance of dermatologists when diagnosing dermoscopy images is a challenging task. Despite the relevance of color in the diagnosis of melanomas, few of the proposed systems exploit this characteristic directly. In this paper we propose a new methodology for color identification in dermoscopy images. Our approach is to learn a statistical model for each color using Gaussian mixtures. The results show that the proposed method performs well, with an average Spearman correlation of 0.7981, with respect to a human expert.

Melanoma detection algorithm based on feature fusion

A Computer Aided-Diagnosis (CAD) System for melanoma diagnosis usually makes use of different types of features to characterize the lesions. The features are often combined into a single vector that can belong to a high dimensional space (early fusion). However, it is not clear if this is the optimal strategy and works on other fields have shown that early fusion has some limitations. In this work, we address this issue and investigate which is the best approach to combine different features comparing early and late fusion. Experiments carried on the datasets PH2 (single source) and EDRA (multi source) show that late fusion performs better, leading to classification scores of Sensitivity = 98% and Specificity = 90% (PH2) and Sensitivity = 83% and Specificity = 76% (EDRA).

Detecting the pigment network in dermoscopy images: A directional approach

Several algorithms have been recently proposed for the analysis of dermoscopy images and the detection of melanomas. However, the pigment network is not considered in most of these works, although this cue plays a major role in clinical diagnosis routines. This paper proposes an algorithm for the detection of the pigment network. The algorithm is based on a bank of directional filters (difference of Gaussians) and explores color, directionality and topological properties of the network.

A Bag-of-Features Approach for the Classification of Melanomas in Dermoscopy Images: The Role of Color and Texture Descriptors

The identification of melanomas in dermoscopy images is still an up to date challenge. Several Computer Aided-Diagnosis Systems for the early diagnosis of melanomas have been proposed in the last two decades. This chapter presents an approach to diagnose melanomas using Bag-of-features, a classification method based on a local description of the image in small patches. Moreover, a comparison between color and texture descriptors is performed in order to assess their discriminative power. The presented results show that local descriptors allow an accurate representation of dermoscopy images and achieve good classification scores: Sensitivity \(=\) 93 % and Specificity \(=\) 88 %. Furthermore it shows that color descriptors perform better than texture ones in the detection of melanomas.

Evaluation of Color Based Keypoints and Features for the Classification of Melanomas Using the Bag-of-Features Model

Dermatologists consider color as one of the major discriminative aspects of melanoma. In this paper we evaluate the importance of color in the keypoint detection and description steps of the Bag-of-Features model. We compare the performance of gray scale against that of color sampling methods using Harris Laplace detector and its color extensions. Moreover, we compare the performance of SIFT and Color-SIFT patch descriptors. Our results show that color detectors and Color-SIFT perform better and are more discriminative achieving Sensitivity = 85%, Specificity = 87% and Accuracy = 87% in PH2 database [17].

Bag-of-Features Classification Model for the Diagnose of Melanoma in Dermoscopy Images Using Color and Texture Descriptors

Melanoma detection using medical oriented approaches has been a trend in skin cancer research. This paper uses a Bag-of-Feature model for the detection of melanomas in dermoscopy images and aims at identifying the role of different local texture and color descriptors. This is a medical oriented approach and the reported results are promising (Sensitivity = 93%, Specificity=85%), showing the ability of this method to describe medical dermoscopic features. Moreover, the results show that color descriptors outperform texture ones.

On the role of texture and color in the classification of dermoscopy images

This paper addresses the detection of melanoma lesions in dermoscopy images, using texture and color features. Although melanoma detection has been studied in several works, using different types of texture, color and shape features, it is not always clear what is the role of each set of features and which features are most discriminative. This papers aims at clarifying the role of texture and color features. Furthermore, the proposed systems is based on features which can be easily implemented and tested by other researchers. It is concluded that both types of features achieve good detection scores when used alone. The best results (SE=94.1%, SP=77.4%) are achieved by combining them both.