Fuzzy logic techniques for blotch feature evaluation in dermoscopy images

Azmath Khan; Kapil Gupta; R. J. Stanley; William V. Stoecker; Randy H. Moss; Giuseppe Argenziano; H. Peter Soyer; Harold S. Rabinovitz; Armand B. Cognetta

doi:10.1016/j.compmedimag.2008.10.001

Fuzzy logic techniques for blotch feature evaluation in dermoscopy images

Azmath Khan, Kapil Gupta, R. J. Stanley, William V. Stoecker, Randy H. Moss, Giuseppe Argenziano, H. Peter Soyer, Harold S. Rabinovitz, Armand B. Cognetta

Research output: Contribution to journal › Article › peer-review

28 Scopus citations

Abstract

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 alpha-cuts 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%.

Original language	English (US)
Pages (from-to)	50-57
Number of pages	8
Journal	Computerized Medical Imaging and Graphics
Volume	33
Issue number	1
DOIs	https://doi.org/10.1016/j.compmedimag.2008.10.001
State	Published - Jan 2009
Externally published	Yes

Keywords

Asymmetric blotches
Dermoscopy
Fuzzy logic
Image analysis
Malignant melanoma
Neural network

ASJC Scopus subject areas

Radiological and Ultrasound Technology
Radiology Nuclear Medicine and imaging
Computer Vision and Pattern Recognition
Health Informatics
Computer Graphics and Computer-Aided Design

Access to Document

10.1016/j.compmedimag.2008.10.001

Cite this

@article{ccaabd4e6d2b4aab9215334e5abb38f9,

title = "Fuzzy logic techniques for blotch feature evaluation in dermoscopy images",

abstract = "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 alpha-cuts 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%.",

keywords = "Asymmetric blotches, Dermoscopy, Fuzzy logic, Image analysis, Malignant melanoma, Neural network",

author = "Azmath Khan and Kapil Gupta and Stanley, {R. J.} and Stoecker, {William V.} and Moss, {Randy H.} and Giuseppe Argenziano and Soyer, {H. Peter} and Rabinovitz, {Harold S.} and Cognetta, {Armand B.}",

note = "Funding Information: 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. Funding Information: 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. ",

year = "2009",

month = jan,

doi = "10.1016/j.compmedimag.2008.10.001",

language = "English (US)",

volume = "33",

pages = "50--57",

journal = "Computerized Medical Imaging and Graphics",

issn = "0895-6111",

publisher = "Elsevier Limited",

number = "1",

}

TY - JOUR

T1 - Fuzzy logic techniques for blotch feature evaluation in dermoscopy images

AU - Khan, Azmath

AU - Gupta, Kapil

AU - Stanley, R. J.

AU - Stoecker, William V.

AU - Moss, Randy H.

AU - Argenziano, Giuseppe

AU - Soyer, H. Peter

AU - Rabinovitz, Harold S.

AU - Cognetta, Armand B.

N1 - Funding Information: 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. Funding Information: 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.

PY - 2009/1

Y1 - 2009/1

N2 - 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 alpha-cuts 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%.

AB - 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 alpha-cuts 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%.

KW - Asymmetric blotches

KW - Dermoscopy

KW - Fuzzy logic

KW - Image analysis

KW - Malignant melanoma

KW - Neural network

UR - http://www.scopus.com/inward/record.url?scp=57449101617&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=57449101617&partnerID=8YFLogxK

U2 - 10.1016/j.compmedimag.2008.10.001

DO - 10.1016/j.compmedimag.2008.10.001

M3 - Article

C2 - 19027266

AN - SCOPUS:57449101617

SN - 0895-6111

VL - 33

SP - 50

EP - 57

JO - Computerized Medical Imaging and Graphics

JF - Computerized Medical Imaging and Graphics

IS - 1

ER -

Fuzzy logic techniques for blotch feature evaluation in dermoscopy images

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this