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Proceedings Paper

Recognizing ovarian cancer from co-registered ultrasound and photoacoustic images
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Paper Abstract

Unique features in co-registered ultrasound and photoacoustic images of ex vivo ovarian tissue are introduced, along with the hypotheses of how these features may relate to the physiology of tumors. The images are compressed with wavelet transform, after which the mean Radon transform of the photoacoustic image is computed and fitted with a Gaussian function to find the centroid of the suspicious area for shift-invariant recognition process. In the next step, 24 features are extracted from a training set of images by several methods; including features from the Fourier domain, image statistics, and the outputs of different composite filters constructed from the joint frequency response of different cancerous images. The features were chosen from more than 400 training images obtained from 33 ex vivo ovaries of 24 patients, and used to train a support vector machine (SVM) structure. The SVM classifier was able to exclusively separate the cancerous from the non-cancerous cases with 100% sensitivity and specificity. At the end, the classifier was used to test 95 new images, obtained from 37 ovaries of 20 additional patients. The SVM classifier achieved 76.92% sensitivity and 95.12% specificity. Furthermore, if we assume that recognizing one image as a cancerous case is sufficient to consider the ovary as malignant, then the SVM classifier achieves 100% sensitivity and 87.88% specificity.

Paper Details

Date Published: 4 March 2013
PDF: 12 pages
Proc. SPIE 8581, Photons Plus Ultrasound: Imaging and Sensing 2013, 85812Q (4 March 2013); doi: 10.1117/12.2002514
Show Author Affiliations
Umar Alqasemi, Univ. of Connecticut (United States)
Patrick Kumavor, Univ. of Connecticut (United States)
Andres Aguirre, Univ. of Connecticut (United States)
Quing Zhu, Univ. of Connecticut (United States)

Published in SPIE Proceedings Vol. 8581:
Photons Plus Ultrasound: Imaging and Sensing 2013
Alexander A. Oraevsky; Lihong V. Wang, Editor(s)

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