Techniques for a structural analysis of dermatoscopic imagery

Fleming, Matthew G.; Steger, Carsten; Zhang, Jun; Gao, Jianbo; Cognetta, Armand B.; Pollak, L.; Dyer, Charles R.

doi:10.1016/s0895-6111(98)00048-2

Cited by 119 publications

(70 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The automated detection of pigment network has received some recent attention [4,5,6,7,8,9,10]. Fleming et al [4] report techniques for extracting and visualizing pigment network via morphological operators.…”

Section: Previous Workmentioning

confidence: 99%

“…Fleming et al [4] report techniques for extracting and visualizing pigment network via morphological operators. Fischer et al [5] use local histogram equalization and gray level morphological operations to enhance the pigment network.…”

Section: Previous Workmentioning

confidence: 99%

“…All work to date has either: 1) not reported quantitative validation [4,5]; 2) validated against a small (n < 100) number of images [7]; 3) only considered or reported results for the 2-class problem (e.g. Absent /Present rather than Absent /Typical /Atypical ) [6,7,8,9,10]; 4) not explicitly identified the location of the network [6]; or 5) has made use of unrealistic exclusion criteria and other manual interventions [9].…”

Section: Previous Workmentioning

confidence: 99%

See 2 more Smart Citations

Modeling the Dermoscopic Structure Pigment Network Using a Clinically Inspired Feature Set

Sadeghi

Razmara

Wighton

et al. 2010

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. We present a method to detect and classify the dermoscopic structure pigment network which may indicate early melanoma in skin lesions. We locate the network as darker areas constituting a mesh, as well as lighter areas representing the 'holes' which the mesh surrounds. After identifying the lines and holes, 69 features inspired by the clinical definition are derived and used to classify the network into one of two classes: Typical or Atypical. We validate our method over a large, inclusive, real-world dataset consisting of 436 images and achieve an accuracy of 82% discriminating between three classes (Absent, Typical or Atypical ) and an accuracy of 93% discriminating between two classes (Absent or Present).

show abstract

Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

See 1 more Smart Citation

Modeling the Dermoscopic Structure Pigment Network Using a Clinically Inspired Feature Set

Sadeghi

Razmara

Wighton

et al. 2010

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…In these works, most of the algorithms carry out an automatic segmentation of the lesion, followed by the calculation of features such as color, texture and shape characteristics. In the following, an automatic learning algorithm is applied to select the most discriminant features, enabling the automatic classification (Anantha et al, 2004;Arroyo and Zapirain, 2014;Barata et al, 2012;Betta et al, 2006;Fleming et al, 1998;Grana et al, 2006;Leo et al, 2010;Sadeghi et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Reticular pattern detection in dermoscopy: an approach using Curvelet Transform

2016

View full text Add to dashboard Cite

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.

show abstract

“…Detection of the pigment network has been investigated recently [4][5][6][7][8]. Fleming et al [4] report the detailed techniques for extracting and measuring the pigment network characteristics.…”

Section: Introductionmentioning

confidence: 99%

A novel method for detection of pigment network in dermoscopic images using graphs

Sadeghi

Razmara

Lee

et al. 2011

Computerized Medical Imaging and Graphics

View full text Add to dashboard Cite

a b s t r a c tWe describe a novel approach to detect and visualize pigment network structures in dermoscopic images, based on the fact that the edges of pigment network structures form cyclic graphs which can be automatically detected and analyzed. First we perform a pre-processing step of image enhancement and edge detection. The resulting binary edge image is converted to a graph and the defined feature patterns are extracted by finding cyclic subgraphs corresponding to skin texture structures. We filtered these cyclic subgraphs to remove other round structures such as globules, dots, and oil bubbles, based on their size and color. Another high-level graph is created from each correctly extracted subgraph, with a node corresponding to a hole in the pigment network. Nodes are connected by edges according to their distances. Finally the image is classified according to the density ratio of the graph. Our results over a set of 500 images from a well known atlas of dermoscopy show an accuracy of 94.3% on classification of the images as pigment network Present or Absent.Crown

show abstract

Techniques for a structural analysis of dermatoscopic imagery

Cited by 119 publications

References 53 publications

Modeling the Dermoscopic Structure Pigment Network Using a Clinically Inspired Feature Set

Modeling the Dermoscopic Structure Pigment Network Using a Clinically Inspired Feature Set

Reticular pattern detection in dermoscopy: an approach using Curvelet Transform

A novel method for detection of pigment network in dermoscopic images using graphs

Contact Info

Product

Resources

About