BACKGROUND/ PURPOSE: Clinically, it is difficult to differentiate the early stage of malignant melanoma and certain benign skin lesions due to similarity in appearance. Our research uses image analysis of clinical skin images and relative color-based pattern recognition techniques to enhance the images and improve differentiation of these lesions. METHODS: First, the relative color images were created from digitized photographic images. Then, they were segmented into objects and morphologically filtered. Next, the relative color features were extracted from the objects to form two different feature spaces; a lesion feature space and an object feature space. The two feature spaces serve as two data models to be analyzed to determine the best features. Finally, we used a statistical analysis model of relative color features, which better classifies the various types of skin lesions. RESULTS/ CONCLUSIONS: The best features found for differentiation of melanoma and benign skin lesions from this study are area, mean in the red and blue bands, standard deviation in the red and green bands, skewness in the green band, and entropy in the red band. With the relative color feature algorithm developed, the best results were obtained with a multi-layer perceptron neural network model. This showed an overall classification success of 79%, with 70% of the benign lesions successfully classified, and 86% of malignant melanoma successfully classified.
BACKGROUND/ PURPOSE: Clinically, it is difficult to differentiate the early stage of malignant melanoma and certain benign skin lesions due to similarity in appearance. Our research uses image analysis of clinical skin images and relative color-based pattern recognition techniques to enhance the images and improve differentiation of these lesions. METHODS: First, the relative color images were created from digitized photographic images. Then, they were segmented into objects and morphologically filtered. Next, the relative color features were extracted from the objects to form two different feature spaces; a lesion feature space and an object feature space. The two feature spaces serve as two data models to be analyzed to determine the best features. Finally, we used a statistical analysis model of relative color features, which better classifies the various types of skin lesions. RESULTS/ CONCLUSIONS: The best features found for differentiation of melanoma and benign skin lesions from this study are area, mean in the red and blue bands, standard deviation in the red and green bands, skewness in the green band, and entropy in the red band. With the relative color feature algorithm developed, the best results were obtained with a multi-layer perceptron neural network model. This showed an overall classification success of 79%, with 70% of the benign lesions successfully classified, and 86% of malignant melanoma successfully classified.
Authors: Barbara Rosado; Scott Menzies; Alexandra Harbauer; Hubert Pehamberger; Klaus Wolff; Michael Binder; Harald Kittler Journal: Arch Dermatol Date: 2003-03
Authors: Scott W Menzies; Leanne Bischof; Hugues Talbot; Alex Gutenev; Michelle Avramidis; Livian Wong; Sing Kai Lo; Geoffrey Mackellar; Victor Skladnev; William McCarthy; John Kelly; Brad Cranney; Peter Lye; Harold Rabinovitz; Margaret Oliviero; Andreas Blum; Alexandra Varol; Alexandra Virol; Brian De'Ambrosis; Roderick McCleod; Hiroshi Koga; Caron Grin; Ralph Braun; Robert Johr Journal: Arch Dermatol Date: 2005-11
Authors: M Elbaum; A W Kopf; H S Rabinovitz; R G Langley; H Kamino; M C Mihm; A J Sober; G L Peck; A Bogdan; D Gutkowicz-Krusin; M Greenebaum; S Keem; M Oliviero; S Wang Journal: J Am Acad Dermatol Date: 2001-02 Impact factor: 11.527
Authors: Ahmedin Jemal; Rebecca Siegel; Elizabeth Ward; Taylor Murray; Jiaquan Xu; Michael J Thun Journal: CA Cancer J Clin Date: 2007 Jan-Feb Impact factor: 508.702
Authors: Jana M Kainerstorfer; Martin Ehler; Franck Amyot; Moinuddin Hassan; Stavros G Demos; Victor Chernomordik; Christoph K Hitzenberger; Amir H Gandjbakhche; Jason D Riley Journal: J Biomed Opt Date: 2010 Jul-Aug Impact factor: 3.170
Authors: Pelin Guvenc; Robert W LeAnder; Serkan Kefel; William V Stoecker; Ryan K Rader; Kristen A Hinton; Sherea M Stricklin; Harold S Rabinovitz; Margaret Oliviero; Randy H Moss Journal: Skin Res Technol Date: 2012-10-01 Impact factor: 2.365
Authors: Mounika Lingala; R Joe Stanley; Ryan K Rader; Jason Hagerty; Harold S Rabinovitz; Margaret Oliviero; Iqra Choudhry; William V Stoecker Journal: Comput Med Imaging Graph Date: 2014-04-03 Impact factor: 4.790
Authors: Nikita V Orlov; Ashani T Weeraratna; Stephen M Hewitt; Christopher E Coletta; John D Delaney; D Mark Eckley; Lior Shamir; Ilya G Goldberg Journal: Cytometry A Date: 2012-03-29 Impact factor: 4.355
Authors: S Pelin Guvenc; Robert W Leander; Serkan Kefel; Ryan K Rader; Kristen A Hinton; Sherea M Stricklin; William V Stoecker Journal: Skin Res Technol Date: 2013-06-01 Impact factor: 2.365
Authors: Jana M Kainerstorfer; Jason D Riley; Martin Ehler; Laleh Najafizadeh; Franck Amyot; Moinuddin Hassan; Randall Pursley; Stavros G Demos; Victor Chernomordik; Michael Pircher; Paul D Smith; Christoph K Hitzenberger; Amir H Gandjbakhche Journal: Biomed Opt Express Date: 2011-04-01 Impact factor: 3.732