Ovarian Tumor Characterization using 3D Ultrasound.
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 1000 benign and 1000 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.
- SourceAvailable from: U Rajendra Acharya[Show abstract] [Hide abstract]
ABSTRACT: Among gynecological malignancies, ovarian cancer is the most frequent cause of death. Image mining algorithms have been predominantly used to give the physicians a more objective, fast, and accurate second opinion on the initial diagnosis made from medical images. The objective of this work is to develop an adjunct computer-aided diagnostic technique that uses 3D ultrasound images of the ovary to accurately characterize and classify benign and malignant ovarian tumors. In this algorithm, we first extract features based on the textural changes and higher-order spectra information. The significant features are then selected and used to train and evaluate the decision tree (DT) classifier. The proposed technique was validated using 1,000 benign and 1,000 malignant images, obtained from ten patients with benign and ten with malignant disease, respectively. On evaluating the classifier with tenfold stratified cross validation, the DT classifier presented a high accuracy of 97 %, sensitivity of 94.3 %, and specificity of 99.7 %. This high accuracy was achieved because of the use of the novel combination of the four features which adequately quantify the subtle changes and the nonlinearities in the pixel intensity variations. The rules output by the DT classifier are comprehensible to the end-user and, hence, allow the physicians to more confidently accept the results. The preliminary results show that the features are discriminative enough to yield good accuracy. Moreover, the proposed technique is completely automated, accurate, and can be easily written as a software application for use in any computer.Journal of Digital Imaging 11/2012; · 1.10 Impact Factor
Dataset: JDI Ovarian Tumor Published Paper