Analysis of the contour structural irregularity of skin lesions using wavelet decomposition

Ma, Lei; Staunton, Richard C.

doi:10.1016/j.patcog.2012.07.001

Cited by 32 publications

(27 citation statements)

References 18 publications

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“…Similar work was reported in [14] where wavelet decomposition was used to extract energy distributions among different wavelet sub-bands. Statistical and geometric irregularity descriptors based on the wavelet coefficients were used to model structural components from the contour.…”

Section: Related Workmentioning

confidence: 69%

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Search for Resolution Invariant Wavelet Features of Melanoma Learned by a Limited ANN Classifier

Surówka¹

2017

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Abstract. This article addresses the Computer Aided Diagnosis (CAD) of melanoma pigmented skin cancer. We present back-propagated Artificial Neural Network (ANN) classifiers discriminating dermoscopic skin lesion images into two classes: malignant melanoma and dysplastic nevus. Features used for our classification experiments are derived from wavelet decomposition coefficients of the image. Our research objective is i) to select the most efficient topology of the hidden layers and the network learning algorithm for full-size and downgraded image resolutions and, ii) to search for resolution-invariant topologies and learning methods. The analyzed classifiers should be fit to work on ARM-based hand-held devices, hence we take into account only limited learning setups.

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Section: Related Workmentioning

confidence: 69%

“…Texture-based methods (wavelet-or curvelet-based): they attempt to search for some frequency and scale information of the skin texture [12] to distinguish between benign and malignant skin progression [13,14]. The related works referenced in this article (Sec.…”

Section: Introductionmentioning

confidence: 99%

Search for Resolution Invariant Wavelet Features of Melanoma Learned by a Limited ANN Classifier

Surówka¹

2017

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“…Melanoma is among the most lethal type of skin cancer that increases rapidly throughout the world, with the five-year survival rate less than 15% for advanced-stage melanoma [1], [2]. Mortality rates of melanoma are associated with its high possibility of metastasizing to other organs (e.g.…”

Section: Introductionmentioning

confidence: 99%

Bi-Directional Dermoscopic Feature Learning and Multi-Scale Consistent Decision Fusion for Skin Lesion Segmentation

Wang

Jiang

Ding

et al. 2020

IEEE Trans. on Image Process.

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Accurate segmentation of skin lesion from dermoscopic images is a crucial part of computer-aided diagnosis of melanoma. It is challenging due to the fact that dermoscopic images from different patients have non-negligible lesion variation, which causes difficulties in anatomical structure learning and consistent skin lesion delineation. In this paper, we propose a novel bi-directional dermoscopic feature learning (biDFL) framework to model the complex correlation between skin lesions and their informative context. By controlling feature information passing through two complementary directions, a substantially rich and discriminative feature representation is achieved. Specifically, we place biDFL module on the top of a CNN network to enhance high-level parsing performance. Furthermore, we propose a multi-scale consistent decision fusion (mCDF) that is capable of selectively focusing on the informative decisions generated from multiple classification layers. By analysis of the consistency of the decision at each position, mCDF automatically adjusts the reliability of decisions and thus allows a more insightful skin lesion delineation. The comprehensive experimental results show the effectiveness of the proposed method on skin lesion segmentation, achieving state-of-the-art performance consistently on two publicly available dermoscopic image databases.Index Terms-Skin lesion segmentation, dermoscopic images, bi-directional dermoscopic feature learning, multi-scale consistent decision fusion.

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“…Several solutions [61][62][63][64] have been proposed for feature extraction and selection of pigmented skin lesions, in order to represent them according to a certain clinical criteria [65][66][67]. Such features may be used for the classification process, in order to provide dermatologists with a computer-aided diagnosis of pigmented skin lesions [2,12].…”

Section: Introductionmentioning

confidence: 99%

Computational methods for pigmented skin lesion classification in images: review and future trends

Oliveira

Papa

Pereira

et al. 2016

Neural Comput & Applic

171

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Skin cancer is considered as one of the most common types of cancer in several countries, and its incidence rate has increased in recent years. Melanoma cases have caused an increasing number of deaths worldwide, since this type of skin cancer is the most aggressive compared to other types. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. An overview of the main and current computational methods that have been proposed for pattern analysis and pigmented skin lesion classification is addressed in this review. In addition, a discussion about the application of such methods, as well as future trends, is also provided. Several methods for feature extraction from both macroscopic and dermoscopic images and models for feature selection are introduced and discussed. Furthermore, classification algorithms and evaluation procedures are described, and performance results for lesion classification and pattern analysis are given.

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Analysis of the contour structural irregularity of skin lesions using wavelet decomposition

Cited by 32 publications

References 18 publications

Search for Resolution Invariant Wavelet Features of Melanoma Learned by a Limited ANN Classifier

Search for Resolution Invariant Wavelet Features of Melanoma Learned by a Limited ANN Classifier

Bi-Directional Dermoscopic Feature Learning and Multi-Scale Consistent Decision Fusion for Skin Lesion Segmentation

Computational methods for pigmented skin lesion classification in images: review and future trends

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