Abstract
Among gynecological malignancies, ovarian cancer is the most frequent cause of death. Preoperative determination of whether a tumor is benign or malignant has often been found to be difficult. Because of such inconclusive findings from ultrasound images and other tests, many patients with benign conditions have been offered unnecessary surgeries thereby increasing patient anxiety and healthcare cost. The key objective of our work is to develop an adjunct computer-aided diagnostic (CAD) technique that uses ultrasound images of the ovary and image mining algorithms to accurately classify benign and malignant ovarian tumor images. In this algorithm, we extract texture features based on Local Binary Patterns (LBP) and Laws Texture Energy (LTE) and use them to build and train a support vector machine (SVM) classifier. Our technique was validated using 1,000 benign and 1,000 malignant images, and we obtained a high accuracy of 99.9 % using a SVM classifier with a radial basis function (RBF) kernel. The high accuracy can be attributed to the determination of the novel combination of the 16 texture-based features that quantify the subtle changes in the images belonging to both classes. The proposed algorithm has the following characteristics: cost-effectiveness, complete automation, easy deployment, and good end-user comprehensibility. We have also developed a novel integrated index, Ovarian Cancer Index (OCI), which is a combination of the texture features, to present the physicians with a more transparent adjunct technique for ovarian tumor classification.
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Abbreviations
- CA125:
-
Cancer antigen 125
- CAD:
-
Computer-aided diagnosis
- DICOM:
-
Digital Imaging and Communications in Medicine
- FN:
-
False-negatives
- FP:
-
False-positives
- LBP:
-
Local Binary Pattern
- LTE:
-
Laws Texture Energy
- MS:
-
Mass spectrometry
- OCI:
-
Ovarian Cancer Index
- PPV:
-
Positive predictive value
- RBF:
-
Radial basis function
- SD:
-
Standard deviation
- SVM:
-
Support vector machine
- TEM:
-
Texture energy measurements
- TN:
-
True-negatives
- TP:
-
True-positives
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None of the authors have any financial or personal conflict of interest that could inappropriately influence the writing or publication of this manuscript.
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Acharya, U.R., Krishnan, M.M.R., Saba, L., Molinari, F., Guerriero, S., Suri, J.S. (2013). Ovarian Tumor Characterization Using 3D Ultrasound. In: Saba, L., Acharya, U., Guerriero, S., Suri, J. (eds) Ovarian Neoplasm Imaging. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-8633-6_25
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