TY - GEN
T1 - A new fourier-based approach to measure irregularity of breast masses in mammograms
AU - Zhang, Gensheng
AU - Shin, Sung
AU - Wang, Wei
AU - Hruska, Carrie
AU - Choi, Hyung D.
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2012
Y1 - 2012
N2 - Morphologic appearance is one of intuitive diagnosis factors of mass lesions in breast imaging, and irregular shape is one of the most frequent appearances for malignant masses. Thus, an effective measure of morphological irregularity will provide a helpful reference to determine malignancy of breast masses. In this paper, a new measure based on Fourier Transform, named Fourier Irregularity Index (FII), was developed to provide a reliable malignant/benign classification factor. The experiment was conducted with 418 breast masses, including 190 malignant cases and 218 benign cases. Performance was assessed and compared among various methods using Receiver Operating Characteristics (ROC) analysis. The proposed measure in this study achieved malignant/benign classification accuracy of 96% with an area (Az) of 0.99 under the receiver operating characteristics (ROC) curve, which outperformed typical traditional approaches, such as Compactness (accuracy of 90%, Az = 0.96), Fractal Dimension (accuracy of 90%, Az = 0.95), Fourier Factor (accuracy of 90%, Az = 0.97), and Fractional Concavity (accuracy of 75%, A z = 0.65).
AB - Morphologic appearance is one of intuitive diagnosis factors of mass lesions in breast imaging, and irregular shape is one of the most frequent appearances for malignant masses. Thus, an effective measure of morphological irregularity will provide a helpful reference to determine malignancy of breast masses. In this paper, a new measure based on Fourier Transform, named Fourier Irregularity Index (FII), was developed to provide a reliable malignant/benign classification factor. The experiment was conducted with 418 breast masses, including 190 malignant cases and 218 benign cases. Performance was assessed and compared among various methods using Receiver Operating Characteristics (ROC) analysis. The proposed measure in this study achieved malignant/benign classification accuracy of 96% with an area (Az) of 0.99 under the receiver operating characteristics (ROC) curve, which outperformed typical traditional approaches, such as Compactness (accuracy of 90%, Az = 0.96), Fractal Dimension (accuracy of 90%, Az = 0.95), Fourier Factor (accuracy of 90%, Az = 0.97), and Fractional Concavity (accuracy of 75%, A z = 0.65).
KW - Fourier irregularity index
KW - Irregularity measure
KW - Shape factor
UR - http://www.scopus.com/inward/record.url?scp=84871637087&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84871637087&partnerID=8YFLogxK
U2 - 10.1145/2401603.2401638
DO - 10.1145/2401603.2401638
M3 - Conference contribution
AN - SCOPUS:84871637087
SN - 9781450314923
T3 - Proceeding of the 2012 ACM Research in Applied Computation Symposium, RACS 2012
SP - 153
EP - 157
BT - Proceeding of the 2012 ACM Research in Applied Computation Symposium, RACS 2012
T2 - 2012 ACM Research in Applied Computation Symposium, RACS 2012
Y2 - 23 October 2012 through 26 October 2012
ER -