Fuzzy logic techniques for blotch feature evaluation in dermoscopy images
Introduction
Dermoscopy is a noninvasive imaging technique that uses optical magnification and fluid immersion or cross-polarized lighting to allow better clinical assessment of skin lesions [1]. Dermoscopy has been shown to improve diagnostic accuracy of pigmented lesions in those with formal training [2]. Blotches are a dermoscopic feature defined as dark structureless areas within pigmented lesions [1]. 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. 1 shows a dermoscopy image of a melanoma with a blotch found automatically.
Blotch extraction algorithms reported by Stoecker et 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. Because structureless areas such as dots, blotches and globules have similar color characteristics, size constraints were applied to the thresholded regions to find blotch-like areas. One of the difficulties in performing melanoma discrimination based on absolute color thresholds was that blotch colors were inconsistent due to variations in lighting and slide processing techniques. In order to compensate for this problem, a second relative color approach was developed for segmenting blotch-like areas [3]. For this technique, the average red, green and blue (RGB) color of the background skin surrounding the lesion was subtracted from the RGB values of each pixel inside the lesion boundary. This technique helped equalize color changes due to different skin types as well as due to lighting and image processing techniques [5]. In the previous blotch study, it was determined that the second technique, blotch determination by the relative color technique, provided better results [3].
In this research, variations of new and existing blotch features are investigated for melanoma discrimination using a feed-forward artificial neural network classifier. The following experiments are performed. First, fuzzy logic techniques are examined for extracting blotches based on blotch size [6]. The ‘crisp’ blotch detection technique of [3] with a fixed minimum blotch size is ‘fuzzified’ so that a dark area extracted from a dermoscopy image qualifies as a blotch only if its size exhibits a certain degree of association with a fuzzy set representative of blotch size. Second, a relative color histogram representative of colors associated with melanoma lesions is used to generate a fuzzy set which provides the basis for differentiating between melanoma and benign skin lesions [6]. Third, relative color histogram techniques using the individual color planes are investigated for segmentation of blotch-like regions. Fuzzy sets computed from relative color histograms, constructed separately for the red, green and blue planes, are used for melanoma discrimination. Fourth, blotch asymmetry measures are explored where the skin lesion border mask and the associated blotch mask are both moment normalized and the lesion is split into four quadrants along its axis of symmetry and the perpendicular line through its center point [7]. Blotch asymmetry is computed in each of the four lesion quadrants. The final experiment examines the role of blotch eccentricity in melanoma discrimination. The goal of this paper is to compare the malignant melanoma discrimination results based on features extracted from blotch-like regions for the different approaches discussed above from dermoscopy images of skin lesions. The remainder of the paper is presented as follows: (1) methodology including an overview of blotches and the different blotch feature algorithms, (2) experiments performed, (3) experimental results, and (4) discussion and conclusions.
Section snippets
Description of experimental data set
The data set used for blotch detection, feature investigation, and melanoma discrimination consisted of 134 melanoma images and 290 benign images (dysplastic nevi and nevocellular nevi) with biopsy confirmation of diagnoses were obtained from three sources, including the EDRA Interactive Atlas of Dermoscopy [8], Skin and Cancer Associates, Plantation, Florida, and Dermatology Associates of Tallahassee, Florida. Skin lesion borders, referred to as border masks, were manually segmented for the
Fuzzy set-based blotch feature extraction
Two fuzzy set-based approaches were investigated for extracting blotches, including using: (1) fuzzy sets A and B representative of blotch size and (2) fuzzy sets C and H representative of relative RGB and individual color planes. In order to evaluate the impact of blotch extraction on blotch features and melanoma discrimination, alpha-cut analysis was performed. The alpha-cut on a designated fuzzy set is the set of members (pixels or objects) whose membership value for that fuzzy set is equal
Results and discussion
In this section, experimental results are presented for melanoma discrimination using a neural network over the 424 dermoscopy image set. In order to compare the melanoma discrimination capability for the different blotch features, the mean area under the ROC curves is computed at each alpha-cut.
The first blotch extraction experiment is based on the fuzzy set A representative of blotch size. The results are summarized in Table 1. The first column shows the alpha-cuts ranging from 0 to 1 in
Summary
In summary, for dermoscopy images of melanoma and benign lesions, we obtained a diagnostic accuracy of 81.0%, at an alpha-cut of 0, by applying fuzzy logic techniques based on blotch size. All of the fuzzy color plane experiments gave a lower diagnostic accuracy than the blotch size experiments. The experiment based on the eccentricity of individual blotches also resulted in a lower diagnostic accuracy, a maximum of 78.1% at an eccentricity threshold of 0.8 using the four asymmetry features.
Acknowledgments
This publication was made possible by Grant Number SBIR R44 CA-101639-02A2 of the National Institutes of Health (NIH). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of NIH.
Azmath U. Khan was born in Hyderabad, India, on July 1, 1983. In December 2004, he received his B.S. in Computer Engineering with Magna Cum Laude from University of Missouri-Rolla. He has been enrolled in the graduate school of University of Missouri-Rolla since January 2005. He has been a graduate research assistant in the area of Image Processing. He was also a graduate teaching assistant for Circuits I and II labs. He received his M.S. degree in Computer Engineering in August 2006.
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Azmath U. Khan was born in Hyderabad, India, on July 1, 1983. In December 2004, he received his B.S. in Computer Engineering with Magna Cum Laude from University of Missouri-Rolla. He has been enrolled in the graduate school of University of Missouri-Rolla since January 2005. He has been a graduate research assistant in the area of Image Processing. He was also a graduate teaching assistant for Circuits I and II labs. He received his M.S. degree in Computer Engineering in August 2006.
Kapil Gupta received his primary and secondary education in Calcutta, India. He received his Bachelor's degree in Electronics and Telecommunications Engineering in July 2001 from Amravati University, India. He received his Master's degree in Electrical Engineering from the University of Missouri-Rolla in December 2003. He received his Ph.D. in Electrical Engineering at the University of Missouri-Rolla. His is currently serving as a postdoctoral fellow at Stoecker & Associates in Rolla, MO.
Ronald Joe Stanley is an Associate Professor in the Department of Electrical and Computer Engineering at Missouri University of Science and Technology. His research interests include signal and image processing, pattern recognition and automation. He is a Senior Member of the IEEE and a member of NAFIPS. He received the B.S.E.E. and M.S.E.E. degrees in electrical engineering and a Ph.D. degree in Computer Engineering and Computer Science from the University of Missouri-Columbia. As a graduate student at the University of Missouri-Columbia, he worked under training grants from the National Library of Medicine and the National Cancer Institute. Upon completing his doctoral study, he served as Principal Investigator for the Image Recognition program at Systems & Electronics, Inc. in St. Louis, MO.
William V. Stoecker received the B.S. in mathematics from Caltech in 1968, the M.S. in systems science from U.C.L.A. in 1970, and the M.D. from University of Missouri-Columbia in 1977. He is Clinical Assistant Professor of Internal Medicine-Dermatology at University of Missouri-Columbia and Adjunct Assistant Professor of Computer Science at Missouri University of Science and Technology. He is past president of the International Society for Digital Imaging of the Skin (ISDIS), past vice-president of the Sulzberger Institute for Dermatologic Education, and has been Chairman of the American Academy of Dermatology Task Force on Computer Data Bases, which has developed diagnostic and therapeutic software for dermatologists. He is president of Stoecker & Associates, developers of dermatology application software. His interests include intelligent systems and computer vision in dermatology and diagnostic problems in dermatology.
Randy H. Moss received the B.S.E.E. and M.S.E.E. degrees in electrical engineering from the University of Arkansas where he was a National Merit Scholar and the Ph.D. degree from the University of Illinois, where he was an NSF Graduate Fellow. He is currently a Professor of Electrical and Computer Engineering at Missouri University of Science and Technology. His research interests are in the areas of image processing, pattern recognition and computer vision. He is especially interested in medical and industrial applications of machine vision techniques. He serves as an Associate Editor of Pattern Recognition and Computerized Medical Imaging and Graphics. Dr. Moss is a member of Sigma Xi, the Pattern Recognition Society, Eta Kappa Nu, Tau Beta Pi and Phi Kappa Phi.