Shape analysis for classification of malignant melanoma

Claridge, Ela; Hall, Per; Keefe, M.; Allen, John P.

doi:10.1016/0141-5425(92)90057-r

Cited by 99 publications

(52 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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

View full text Add to dashboard Cite

show abstract

mentioning

confidence: 99%

Colour histogram analysis for melanoma discrimination in clinical images

Faziloglu

Stanley

Moss

et al. 2003

Skin Research and Technology

View full text Add to dashboard Cite

show abstract

“…Texture irregularities are the small variations along the border, while structure irregularities are the global indentations and protrusions that may suggest either the unstable growth in a lesion or regression of a melanoma. An accurate measurement of structure irregularities is essential to detect the malignancy of melanoma [19]. Our extended curvature scale-space filtering technique can be used to measure the structure border irregularity of a pigmented skin lesion by locating a set of global indentation/protrusion segments along the border.…”

Section: Differentiating Malignant Melanomas From Benign Nevimentioning

confidence: 99%

Indentation and Protrusion Detection and Its Applications

Lee

Atkins

2001

Scale-Space and Morphology in Computer Vision

View full text Add to dashboard Cite

Abstract. In this paper, we investigated the mechanism of dividing a 2D-object border into a set of local and global indentation and protrusion segments by extending the classic curvature scale-space filtering method. The resultant segments, arranged in hierarchical structures, can represent the object shape. Applying this technique, we derived a border irregularity measure for pigmented skin lesions. The measure correlated well with experienced dermatologists' evaluations and may be useful for measuring the malignancy of the lesion. Furthermore, we can use the method to discover all the bays in an aerial map.

show abstract

“…The application of this measure to pigmented skin lesions showed that probability of a lesion being malignant increased with increasing fractal dimension, and hence border irregularity (Claridge et al, 1992;1998). The box counting method is one of the most popular techniques to estimate the fractal dimension of a given curve .…”

Section: Fractal Dimensionmentioning

confidence: 99%

A Prelimary Approach for the Automated Recognition of Malignant Melanoma

Zagrouba¹,

Barhoumi²

2011

Image Anal Stereol

View full text Add to dashboard Cite

In this work, we are motivated by the desire to classify skin lesions as malignants or benigns from color photographic slides of the lesions. Thus, we use color images of skin lesions, image processing techniques and artificial neural network classifier to distinguish melanoma from benign pigmented lesions. As the first step of the data set analysis, a preprocessing sequence is implemented to remove noise and undesired structures from the color image. Second, an automated segmentation approach localizes suspicious lesion regions by region growing after a preliminary step based on fuzzy sets. Then, we rely on quantitative image analysis to measure a series of candidate attributes hoped to contain enough information to differentiate melanomas from benign lesions. At last, the selected features are supplied to an artificial neural network for classification of tumor lesion as malignant or benign. For a preliminary balanced training/testing set, our approach is able to obtain 79.1% of correct classification of malignant and benign lesions on real skin lesion images.

show abstract

Shape analysis for classification of malignant melanoma

Cited by 99 publications

References 9 publications

Colour histogram analysis for melanoma discrimination in clinical images

Colour histogram analysis for melanoma discrimination in clinical images

Indentation and Protrusion Detection and Its Applications

A Prelimary Approach for the Automated Recognition of Malignant Melanoma

Contact Info

Product

Resources

About