Analysis of Pigmented Skin Lesion Border Irregularity Using the Harmonic Wavelet Transform

Clawson, Kathy; Morrow, Philip; Scotney, Bryan; McKenna, John; Dolan, O.M.

doi:10.1109/imvip.2009.11

Cited by 18 publications

(19 citation statements)

References 14 publications

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“…Work [32] presented a contours harmonic wavelet coefficients forming a sequence of multi-scale roughness descriptors to characterise the distribution of energy across contour's line. The descriptors for regular and irregular borders were compared with ground truth from experts.…”

Section: Related Workmentioning

confidence: 99%

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: 99%

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

Surówka¹

2017

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“…Together with the mean and variance of the energy of D j at each significant level [9], the following features related to contour irregularity were defined:-1) Entropy of wavelet energy,…”

Section: Statistical Measuresmentioning

confidence: 99%

“…The detail coefficients in D 10 locate at the low frequencies where there is some basic structure, but for computational efficiency it was found to be better to use this when included in the approximation set A 9 . Therefore, as expected, with HDs up to 3.1, the middle-frequency bands represented by D 6 to D 9 have been shown to contain significant discrimination information, and these were selected as the significant levels. to D 9 (s = 9, s t = 6), that is the boundaries representing the structural portion of the original lesion.…”

Section: Significant Level Selection and Reconstruction Of The Structmentioning

confidence: 99%

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

Staunton

2013

Pattern Recognition

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The boundary irregularity of skin lesions is of clinical significance for the early detection of malignant melanomas and to distinguish them from other lesions such as benign moles. The structural components of the contour are of particular importance. To extract the structure from the contour, wavelet decomposition was used as these components tend to locate in the lower frequency sub-bands. Lesion contours were modeled as signatures with scale normalization to give position and frequency resolution invariance. Energy distributions among different wavelet sub-bands were then analyzed to extract those with significant levels and differences to enable maximum discrimination.Based on the coefficients in the significant sub-bands, structural components from the original contours were modeled, and a set of statistical and geometric irregularity descriptors researched that were applied at each of the significant sub-bands. The effectiveness of the descriptors was measured using the Hausdorff distance between sets of data from melanoma and mole contours.The best descriptor outputs were input to a back projection neural network to construct a combined classifier system. Experimental results showed that thirteen features from four sub-bands produced the best discrimination between sets of melanomas and moles, and that a small training set of nine melanomas and nine moles was optimum.

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“…In recent decade, there are many studies on the boundary irregularity with geometric measure [2,7,8] and local fractal measure [5]. There are two types of irregularities on lesion boundaries: textual and structural irregularities corresponding to finer changes and obvious convexo-concaves respectively [2], while structural irregularity has clinical importance on melanoma diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Multi-scale descriptors for contour irregularity of skin lesion using wavelet decomposition

Qin

et al. 2010

2010 3rd International Conference on Biomedical Engineering and Informatics

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Boundary irregularity of skin lesions is of clinical significance for early detection of malignant melanomas, while structural components of lesion contour have clinical significance in particular. To extract effective features of structural components of skin lesions, an integrated approach is proposed in this paper using wavelet decompositions and inter-class distance analysis (Hausdorff Distance, HD for short) among wavelet subbands. Firstly, lesion contours are modeled as signatures with scale normalization to reach position and frequency resolution invariant. Energy distributions among different wavelet sub-bands are then analyzed to extract those significant levels with large discrimination capability. Based on significant subbands extracted, structural components of original contours are generated and irregularity descriptors at the multi-significant subbands are presented. Finally experimental results give effectiveness of proposed methods.

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Analysis of Pigmented Skin Lesion Border Irregularity Using the Harmonic Wavelet Transform

Cited by 18 publications

References 14 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

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

Multi-scale descriptors for contour irregularity of skin lesion using wavelet decomposition

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