Automatic detection of asymmetry in skin tumors

Stoecker, William V.; Li, William Weiling; Moss, Randy Hays

doi:10.1016/0895-6111(92)90073-i

Cited by 87 publications

(47 citation statements)

References 4 publications

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“…Numerous computer-based techniques have been applied in the past to pigmented lesion images for investigating features to detect malignant melanoma (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25). In the research presented here, skin lesion colour analysis is examined.…”

mentioning

confidence: 99%

Colour histogram analysis for melanoma discrimination in clinical images

Faziloglu

Stanley

Moss

et al. 2003

Skin Research and Technology

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confidence: 99%

Colour histogram analysis for melanoma discrimination in clinical images

Faziloglu

Stanley

Moss

et al. 2003

Skin Research and Technology

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“…In Ref. [35] the authors use the principal component decomposition of a binary mask of the lesion to compute two orthogonal symmetry axes. An asymmetry index is computed for both axes using the following definition:…”

Section: Quantification Of the Degree Of Symmetrymentioning

confidence: 99%

“…In Ref. [35] only the minimum value is kept. Another approach based on the principal component decomposition is used in Ref.…”

Section: Quantification Of the Degree Of Symmetrymentioning

confidence: 99%

Towards a computer-aided diagnosis system for pigmented skin lesions

Schmid-Saugeona

Guillodb

Thiran

2003

Computerized Medical Imaging and Graphics

192

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This paper presents a computer-aided diagnosis system for pigmented skin lesions, with solutions for the lesion boundary detection and for the quantification of the degree of symmetry. Lesion detection results were validated by expert dermatologists, who also provided handdrawn boundaries of the lesions. These reference boundaries were not used as a gold standard, but were allowed to statistically determine the accuracy of the boundaries provided by computerized techniques. We could show that the dermatologists were not able to reproduce their results, and that the boundaries of any expert taken alone showed higher divergence from those of the set of remaining experts than the automatic techniques we developed. Feature extraction is restricted in this paper to the quantification of degree of symmetry, even though it is clear that many other features will be necessary for a complete diagnosis system. The symmetry quantification step provides a sixdimensional feature vector that can be used to classify pigmented skin lesions as being benign or malignant. We demonstrate that our scheme outperforms methods based on the principal component decomposition, which is widely used for this kind of application. q

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“…Clinical images are acquired using 35 mm camera shots of skin lesions. Image analysis of clinical images has been shown to improve diagnostic sensitivity and specificity of detecting malignant melanoma [13,19,20]. Fig.…”

Section: Clinical Image Datamentioning

confidence: 99%

“…Let | α B| denote the cardinality of the α-cut, i.e. the number of lesion pixels where µ B (C (x,y) ) ≥ α, for a specified α on B [19]. Let S(B) refer to the support of B, where S(B) = {(x, y) : µ B (C (x,y) ) > 0 for (x, y) contained in the skin lesion}, and let |S(B)| denote the cardinality of S(B), that is, the number of pixels within the skin lesion with nonzero membership in B [23].…”

Section: 72mentioning

confidence: 99%

A fuzzy-based histogram analysis technique for skin lesion discrimination in dermatology clinical images

Stanley

Moss

Stoecker

et al. 2003

Computerized Medical Imaging and Graphics

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A fuzzy logic-based color histogram analysis technique is presented for discriminating benign skin lesions from malignant melanomas in dermatology clinical images. The approach utilizes a fuzzy set for benign skin lesion color, and alpha-cut and support set cardinality for quantifying a fuzzy ratio skin lesion color feature. Skin lesion discrimination results are reported for the fuzzy ratio and fusion with a previously determined percent melanoma color feature over a data set of 258 clinical images. For the fusion technique, alpha-cuts for the fuzzy ratio can be chosen to recognize over 93.30% of melanomas with approximately 15.67% false positive lesions.

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Automatic detection of asymmetry in skin tumors

Cited by 87 publications

References 4 publications

Colour histogram analysis for melanoma discrimination in clinical images

Colour histogram analysis for melanoma discrimination in clinical images

Towards a computer-aided diagnosis system for pigmented skin lesions

A fuzzy-based histogram analysis technique for skin lesion discrimination in dermatology clinical images

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