Pablo Gautério Cavalcanti

Automated prescreening of pigmented skin lesions using standard cameras

This paper describes a new method for classifying pigmented skin lesions as benign or malignant. The skin lesion images are acquired with standard cameras, and our method can be used in telemedicine by non-specialists. Each acquired image undergoes a sequence of processing steps, namely: (1) preprocessing, where shading effects are attenuated; (2) segmentation, where a 3-channel image representation is generated and later used to distinguish between lesion and healthy skin areas; (3) feature extraction, where a quantitative representation for the lesion area is generated; and (4) lesion classification, producing an estimate if the lesion is benign or malignant (melanoma). Our method was tested on two publicly available datasets of pigmented skin lesion images. The preliminary experimental results are promising, and suggest that our method can achieve a classification accuracy of 96.71%, which is significantly better than the accuracy of comparable methods available in the literature.

Shading attenuation in human skin color images

This paper presents a new automatic method to significantly attenuate the color degradation due to shading in color images of the human skin. Shading is caused by illumination variation across the scene due to changes in local surface orientation, lighting conditions, and other factors. Our approach is to estimate the illumination variation by modeling it with a quadric function, and then relight the skin pixels with a simple operation. Therefore, the subsequent color skin image processing and analysis is simplified in several applications. We illustrate our approach in two typical color imaging problems involving human skin, namely: (a) pigmented skin lesion segmentation, and (b) face detection. Our preliminary experimental results show that our shading attenuation approach helps reducing the complexity of the color image analysis problem in these applications.

Pigmented skin lesion segmentation on macroscopic images

Several pigmented skin lesion segmentation methods have been proposed for dermoscopy images, however skin lesion segmentation on macroscopic images have not received much attention. Despite the fact that dermoscopy is a very specialized technique, in some practical situations, patients do not have a fast access to an specialist. In this situations, pre-screening systems can be used to evaluate a suspect skin lesion by a non-specialist, and lesion segmentation is very important for the success of such systems. This paper proposes a new method for segmenting pigmented skin lesions on macroscopic images acquired with standard cameras. Our method is simpler than comparable methods proposed for dermoscopy, and our experiments based on publicly available datasets of pigmented skin lesion images show promising results. Our approach can achieve an average segmentation error of 24.85%, which is better than the accuracy of comparable methods available in the literature (even for illumination corrected images).

A two-stage approach for discriminating melanocytic skin lesions using standard cameras

In this paper, we propose a novel approach to discriminate malignant melanomas and benign atypical nevi, since both types of melanocytic skin lesions have very similar characteristics. Recent studies involving the non-invasive diagnosis of melanoma indicate that the concentrations of the two main classes of melanin present in the human skin, eumelanin and pheomelanin, can potentially be used in the computation of relevant features to differentiate these lesions. So, we describe how these features can be estimated using only standard camera images. Moreover, we demonstrate that using these features in conjunction with features based on the well known ABCD rule, it is possible to achieve 100% of sensitivity and more than 99% accuracy in melanocytic skin lesion discrimination, which is a highly desirable characteristic in a prescreening system.

An ICA-based method for the segmentation of pigmented skin lesions in macroscopic images

Segmentation is an important step in computer-aided diagnostic systems for pigmented skin lesions, since that a good definition of the lesion area and its boundary at the image is very important to distinguish benign from malignant cases. In this paper a new skin lesion segmentation method is proposed. This method uses Independent Component Analysis to locate skin lesions in the image, and this location information is further refined by a Level-set segmentation method. Our method was evaluated in 141 images and achieved an average segmentation error of 16.55%, lower than the results for comparable state-of-the-art methods proposed in literature.

Macroscopic Pigmented Skin Lesion Segmentation and Its Influence on Lesion Classification and Diagnosis

Melanoma is a type of malignant pigmented skin lesion, and currently is among the most dangerous existing cancers. However, differentiating malignant and benign cases is a hard task even for experienced specialists, and a computer-aided diagnosis system can be an useful tool. Usually, the system starts by pre-processing the image, i.e. removing undesired artifacts such as hair, freckles or shading effects. Next, the system performs a segmentation step to identify the lesion boundaries. Finally, based on the image area identified as lesion, several features are computed and a classification is provided. In this chapter we describe all these steps, giving special attention to segmentation approaches for pigmented skin lesions, proposed for standard camera images (i.e. simple color photographs). Next, we compare the segmentation results to identify which techniques have more accurate results, and discuss how these results may influence in the following steps: the feature extraction and the final lesion classification.

A coarse-to-fine approach for segmenting melanocytic skin lesions in standard camera images

Melanoma is a type of malignant melanocytic skin lesion, and it is among the most life threatening existing cancers if not treated at an early stage. Computer-aided prescreening systems for melanocytic skin lesions is a recent trend to detect malignant melanocytic skin lesions in their early stages, and lesion segmentation is an important initial processing step. A good definition of the lesion area and its border is very important for discriminating between benign and malignant cases. In this paper, we propose to segment melanocytic skin lesions using a sequence of steps. We start by pre-segmenting the skin lesion, creating a new image representation (channel) where the lesion features are more evident. This new channel is thresholded, and the lesion border pre-detection is refined using an active-contours algorithm followed by morphological operations. Our experimental results based on a publicly available dataset suggest that our method potentially can be more accurate than comparable state-of-the-art methods proposed in literature.

Segmentation of pigmented skin lesions using Non-negative Matrix Factorization

Melanoma is a type of malignant pigmented skin lesion, which currently is among the most dangerous existing cancers. Segmentation is an important step in computer-aided pre-screening systems for pigmented skin lesions, because a good definition of the lesion area and its rim is very important for discriminating between benign and malignant cases. In this paper, we propose to segment pigmented skin lesions using the Non-negative Matrix Factorization of the multi-channel skin lesion image representation. Our preliminary experimental results on a publicly available dataset suggest that our method obtains lower segmentation errors (in average) than comparable state-of-the-art methods proposed in literature.

Lung nodule segmentation in chest computed tomography using a novel background estimation method

BACKGROUND Lung cancer results in the highest number of cancer deaths worldwide. The segmentation of lung nodules is an important task in computer systems to help physicians differentiate malignant lesions from benign lesions. However, it has already been observed that this may be a difficult task, especially when nodules are connected to an anatomical structure. METHODS This paper proposes a method to estimate the background of the nodule area and how this estimation is used to facilitate the segmentation task. RESULTS Our experiments indicate more than 99% of accuracy with less than 1% of false positive rate (FPR). CONCLUSIONS The proposed methods achieved better results than a state-of-the-art approach, indicating potential to be used in medical image processing systems.

Texture Information in Melanocytic Skin Lesion Analysis Based on Standard Camera Images

The classification of melanocytic skin lesions is a very difficult task, and usually computer-aided diagnosis systems or screening systems focus on reproducing medical criteria as the ABCD rule. However, the texture information can also contribute significantly for the lesion classification, since malignant cases tends to present texture patterns different from benign cases. In this chapter, we detail five representative sets of features that have been proposed in the literature for the representation of melanocytic lesions texture information, and then we analyze how these features distinguish between malignant and benign classes using two well known classifiers.