Detection of asymmetric blotches (asymmetric structureless areas) in dermoscopy images of malignant melanoma using relative color

Stoecker, William V.; Gupta, Kapil; Stanley, R. Joe; Moss, Randy Hays; Shrestha, Bijaya

doi:10.1111/j.1600-0846.2005.00117.x

Cited by 66 publications

(30 citation statements)

References 10 publications

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“…Third, relative color is more natural from a perceptual point of view. Recent studies 10,11 have confirmed the usefulness of relative color features in skin lesion image analysis. ; ; ;…”

Section: Relative Color Featuresmentioning

confidence: 82%

“…However, feature extraction in dermoscopy images is relatively unexplored. The dermoscopic feature extraction studies to date include two pilot studies on pigment networks 21,22 and globules 21 , and three systematic studies on dots 23 and blotches 11,24 . To the best of our knowledge, there is no published systematic study on the detection of blue-white veil.…”

Section: Discussionmentioning

confidence: 99%

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Detection of blue-white veil areas in dermoscopy images using machine learning techniques

et al. 2006

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As a result of the advances in skin imaging technology and the development of suitable image processing techniques, during the last decade, there has been a significant increase of interest in the computer-aided diagnosis of skin cancer. Dermoscopy is a non-invasive skin imaging technique which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. One of the useful features in dermoscopic diagnosis is the blue-white veil (irregular, structureless areas of confluent blue pigmentation with an overlying white "ground-glass" film) which is mostly associated with invasive melanoma. In this preliminary study, a machine learning approach to the detection of blue-white veil areas in dermoscopy images is presented. The method involves pixel classification based on relative and absolute color features using a decision tree classifier. Promising results were obtained on a set of 224 dermoscopy images.

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“…Third, relative color is more natural from a perceptual point of view. Recent studies 10,11 have confirmed the usefulness of relative color features in skin lesion image analysis. ; ; ;…”

Section: Relative Color Featuresmentioning

confidence: 82%

Section: Discussionmentioning

confidence: 99%

Detection of blue-white veil areas in dermoscopy images using machine learning techniques

et al. 2006

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“…Normally, the papers present one approach to cover one or some of the "letters" of the rule, that is, some are based on detecting asymmetry [7,8], borders [9][10][11][12], colour [13][14][15] or diameter [14]. There are some papers that cover the whole ABCD criterion.…”

Section: Introductionmentioning

confidence: 98%

Pattern analysis of dermoscopic images based on Markov random fields

Serrano

Acha

2009

Pattern Recognition

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“…Blotches that are located asymmetrically within a lesion are indicative of malignant melanoma. Image processing techniques have been developed to find blotches automatically [3,4]. Fig.…”

Section: Introductionmentioning

confidence: 99%

“…al. [3] can be divided into two categories. The first employed absolute color thresholding for segmenting blotch-like regions, wherein threshold values were placed on the red, green, and blue (RGB) color planes in the digital images of skin lesions.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic techniques for blotch feature evaluation in dermoscopy images

Khan

Gupta

Stanley

et al. 2009

Computerized Medical Imaging and Graphics

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Blotches, also called structureless areas, are critical in differentiating malignant melanoma from benign lesions in dermoscopy skin lesion images. In this paper, fuzzy logic techniques are investigated for the automatic detection of blotch features for malignant melanoma discrimination. Four fuzzy sets representative of blotch size and relative and absolute blotch colors are used to extract blotchy areas from a set of dermoscopy skin lesion images. Five previously reported blotch features are computed from the extracted blotches as well as four new features. Using a neural network classifier, malignant melanoma discrimination results are optimized over the range of possible alphacuts and compared with results using crisp blotch features. Features computed from blotches using the fuzzy logic techniques based on three plane relative color and blotch size yield the highest diagnostic accuracy of 81.2%.

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Detection of asymmetric blotches (asymmetric structureless areas) in dermoscopy images of malignant melanoma using relative color

Abstract: Neural network and statistical analysis showed that the blotch detection method was somewhat more effective using relative color than using absolute color. The relative-color blotch detection method gave a diagnostic accuracy of about 77%.

Cited by 66 publications

References 10 publications

Detection of blue-white veil areas in dermoscopy images using machine learning techniques

Detection of blue-white veil areas in dermoscopy images using machine learning techniques

Pattern analysis of dermoscopic images based on Markov random fields

Fuzzy logic techniques for blotch feature evaluation in dermoscopy images

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