title: Real-time supervised detection of pink areas in dermoscopic images of melanoma: importance of color shades, texture and location.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  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.
authors: Department of Electrical and Computer Engineering, Southern Illinois University, Edwardsville, IL, USA.
authors: R
authors: R
authors: Kaur
authors: Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA.
authors: P P
authors: PP
authors: Albano
authors: Stoecker & Associates, Rolla, MO, USA.
authors: J G
authors: JG
authors: Cole
authors: Stoecker & Associates, Rolla, MO, USA.
authors: J
authors: J
authors: Hagerty
authors: Department of Electrical and Computer Engineering, Southern Illinois University, Edwardsville, IL, USA.
authors: R W
authors: RW
authors: LeAnder
authors: Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA.
authors: R H
authors: RH
authors: Moss
authors: Stoecker & Associates, Rolla, MO, USA.
authors: W V
authors: WV
authors: Stoecker
mesh_terms: D000465:Algorithms; D003116:Color; D003124:Colorimetry / Q000379:methods*; D003199:Computer Systems; D046169:Dermoscopy / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008545:Melanoma / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000473:pathology*; D012880:Skin Pigmentation; D000069553:Supervised Machine Learning
keywords: color analysis; color shade detection; computer-aided diagnosis; dermoscopy; image analysis; machine vision; melanoma; pink area