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. 2015 Nov;21(4):466-73.
doi: 10.1111/srt.12216. Epub 2015 Mar 22.

Real-time supervised detection of pink areas in dermoscopic images of melanoma: importance of color shades, texture and location

R Kaur  1 , P P Albano  2 , J G Cole  3 , J Hagerty  3 , R W LeAnder  1 , R H Moss  2 , W V Stoecker  3
Affiliations

Real-time supervised detection of pink areas in dermoscopic images of melanoma: importance of color shades, texture and location

R Kaur et al. Skin Res Technol. 2015 Nov.

Abstract

Background/purpose: Early detection of malignant melanoma is an important public health challenge. In the USA, dermatologists are seeing more melanomas at an early stage, before classic melanoma features have become apparent. Pink color is a feature of these early melanomas. If rapid and accurate automatic detection of pink color in these melanomas could be accomplished, there could be significant public health benefits.

Methods: Detection of three shades of pink (light pink, dark pink, and orange pink) was accomplished using color analysis techniques in five color planes (red, green, blue, hue, and saturation). Color shade analysis was performed using a logistic regression model trained with an image set of 60 dermoscopic images of melanoma that contained pink areas. Detected pink shade areas were further analyzed with regard to the location within the lesion, average color parameters over the detected areas, and histogram texture features.

Results: Logistic regression analysis of a separate set of 128 melanomas and 128 benign images resulted in up to 87.9% accuracy in discriminating melanoma from benign lesions measured using area under the receiver operating characteristic curve. The accuracy in this model decreased when parameters for individual shades, texture, or shade location within the lesion were omitted.

Conclusion: Texture, color, and lesion location analysis applied to multiple shades of pink can assist in melanoma detection. When any of these three details: color location, shade analysis, or texture analysis were omitted from the model, accuracy in separating melanoma from benign lesions was lowered. Separation of colors into shades and further details that enhance the characterization of these color shades are needed for optimal discrimination of melanoma from benign lesions.

Keywords: color analysis; color shade detection; computer-aided diagnosis; dermoscopy; image analysis; machine vision; melanoma; pink area.

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Figures

Figure 1
Figure 1
Proportion within histopathologic diagnosis of pink color by distribution pattern (n = 1290). Pink distribution was categorized into three patterns: peripheral-only pink (pink in outer portion of lesion only), central-only pink (pink in center of lesion only), and pink throughout lesion (pink in both outer portion and center of lesion). Note that the majority of lesions have at least some pink color.
Figure 2
Figure 2
Pink area detection and classification workflow.
Figure 3
Figure 3
Dermoscopy images of invasive melanomas showing detection of three pink shades. (Although three sample shade areas were found for each lesion, zoomed images do not show all samples.)
Figure 4
Figure 4
Melanoma having three shades of pink, automatically detected in real time (C++), with quintile Overlays.
Figure 5
Figure 5
Areas under the Receiver Operating Characteristic Curves resulting from the logistic regression models; c = estimated area under the curve; c=0.879, indicates that best accuracy is obtained using model that includes all features.
See this image and copyright information in PMC

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