Research Papers: Imaging

Utilizing spatial and spectral features of photoacoustic imaging for ovarian cancer detection and diagnosis

[+] Author Affiliations
Hai Li

University of Connecticut, Department of Electrical and Computer Engineering, 371 Fairfield Way, U-4157, Storrs, Connecticut 06269-4157, United States

Patrick Kumavor

University of Connecticut, Biomedical Engineering Department, 371 Fairfield Way, U-4157, Storrs, Connecticut 06269-4157, United States

Umar Salman Alqasemi

University of Connecticut, Biomedical Engineering Department, 371 Fairfield Way, U-4157, Storrs, Connecticut 06269-4157, United States

Quing Zhu

University of Connecticut, Department of Electrical and Computer Engineering, 371 Fairfield Way, U-4157, Storrs, Connecticut 06269-4157, United States

University of Connecticut, Biomedical Engineering Department, 371 Fairfield Way, U-4157, Storrs, Connecticut 06269-4157, United States

J. Biomed. Opt. 20(1), 016002 (Jan 02, 2015). doi:10.1117/1.JBO.20.1.016002
History: Received July 6, 2014; Accepted December 2, 2014
Text Size: A A A

Abstract.  A composite set of ovarian tissue features extracted from photoacoustic spectral data, beam envelope, and co-registered ultrasound and photoacoustic images are used to characterize malignant and normal ovaries using logistic and support vector machine (SVM) classifiers. Normalized power spectra were calculated from the Fourier transform of the photoacoustic beamformed data, from which the spectral slopes and 0-MHz intercepts were extracted. Five features were extracted from the beam envelope and another 10 features were extracted from the photoacoustic images. These 17 features were ranked by their p-values from t-tests on which a filter type of feature selection method was used to determine the optimal feature number for final classification. A total of 169 samples from 19 ex vivo ovaries were randomly distributed into training and testing groups. Both classifiers achieved a minimum value of the mean misclassification error when the seven features with lowest p-values were selected. Using these seven features, the logistic and SVM classifiers obtained sensitivities of 96.39±3.35% and 97.82±2.26%, and specificities of 98.92±1.39% and 100%, respectively, for the training group. For the testing group, logistic and SVM classifiers achieved sensitivities of 92.71±3.55% and 92.64±3.27%, and specificities of 87.52±8.78% and 98.49±2.05%, respectively.

Figures in this Article
© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Hai Li ; Patrick Kumavor ; Umar Salman Alqasemi and Quing Zhu
"Utilizing spatial and spectral features of photoacoustic imaging for ovarian cancer detection and diagnosis", J. Biomed. Opt. 20(1), 016002 (Jan 02, 2015). ; http://dx.doi.org/10.1117/1.JBO.20.1.016002


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.