Reticular pattern detection in dermoscopy: an approach using Curvelet Transform

Abstract

Introduction: Dermoscopy is a non-invasive in vivo imaging technique, used in dermatology in feature identification, among pigmented melanocytic neoplasms, from suspicious skin lesions. Often, in the skin exam is possible to ascertain markers, whose identification and proper characterization is difficult, even when it is used a magnifying lens and a source of light. Dermoscopic images are thus a challenging source of a wide range of digital features, frequently with clinical correlation. Among these markers, one of particular interest to diagnosis in skin evaluation is the reticular pattern. Methods: This paper presents a novel approach (avoiding pre-processing, e.g. segmentation and filtering) for reticular pattern detection in dermoscopic images, using texture spectral analysis. The proposed methodology involves a Curvelet Transform procedure to identify features. Results: Feature extraction is applied to identify a set of discriminant characteristics in the reticular pattern, and it is also employed in the automatic classification task. The results obtained are encouraging, presenting Sensitivity and Specificity of 82.35% and 76.79%, respectively. Conclusions: These results highlight the use of automatic classification, in the context of artificial intelligence, within a computer-aided diagnosis strategy, as a strong tool to help the human decision making task in clinical practice. Moreover, the results were obtained using images from three different sources, without previous lesion segmentation, achieving to a rapid, robust and low complexity methodology. These properties boost the presented approach to be easily used in clinical practice as an aid to the diagnostic process.

Appending Global to Local features for Skin Lesion Classification on Dermoscpic Images

“…In the last decade, many procedures to detect melanoma have been presented. Some of these attempts were to imitative dermatologists performance of by extracting and detecting most dermoscopic structures, like pigment network (Masood,2016), (Barata et al,2014), (Xie et al,2017), (Garbe et al,2016), irregular streaks (Machado et al,2016), (Adjed,2017) granularities, blotches (Barata et al,2013), regression structures (Kumar and Kumanan,2018) and blue-white veil (Sreena and Lijiya,2018), (Madooei et al,2018). Then these shapes can be used to score a lesion in a similar dermatologists adopted the pathway.…”

A Hybrid Artificial Intelligence Model for Skin Cancer Diagnosis

“…Recently, frequency domain analysis in the medical domain leads to the development of CAD system using spectral features such as wavelet-based analysis [9], multi-wavelet analysis [10], curvelet [11] and contourlet [12]. The various textural features such as singularities, curves and contours in smooth and non-smooth regions of medical images are represented by the analysis as mentioned above based on the filters used in the decomposition stage and their arrangements.…”

“…A review of different implementations of neural networks for skin cancer diagnosis is provided in Brinker et al [20]. Also, support vector machine [6,10,21], Bayes [11], Random forest [21], AdaBoost [12] and k-nearest neighbour [21] supervised classifiers also used in skin cancer diagnosis. A Self Organizing Map (SOM) [22] is a method of unsupervised learning that differs from other neural network techniques in that the desired output need not be specified.…”