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.

Ovarian Tissue Characterization in Ultrasound

“…The different steps of CAD techniques are image acquisition, preprocessing, feature extraction, feature selection, classifier development, and classifier validation. 18 Figure 3 depicts the general block diagram of the proposed CAD technique for ovarian cancer detection. The acquired US image database is split into training and test sets.…”

“…Our group has developed a few CAD systems for ovarian cancer detection. [18][19][20][21] In these studies, we evaluated 20 nonconsecutive women with previous diagnosis of ovarian mass (10 malignant, 10 benign; 9 postmenopausal, 11 premenopausal; age: 29-74 years). First B-mode ultrasonography was used to characterize the morphology of the adnexal masses.…”

“…Two-dimensional (2D) scanning is most commonly used, as the vascularity of the tumor can be captured by 2D power Doppler with proper settings so as to achieve high sensitivity to detect even low-velocity flow. 18 Threedimensional (3D) techniques can capture the 3Dimages of the ovary, presenting the characteristics of the tumor volume such as thick papillary projections and solid areas in an effective manner. Thus, 3D US, power, and color Doppler can be selectively used in cancer-probable regions where sensitive scan is required.…”

“…Depending on the size of the volume box, the volume acquisition time ranges from 2 to 6 seconds. 18 Gray scale as well as color Doppler images can be used to study the morphological features. Our group has developed a few CAD systems for ovarian cancer detection.…”

“…In fact, for benefitting the common people, research is aimed at developing an affordable, harmless, and easily deployable modality that produces very accurate results. Automated CAD system using US images of ovarian tumor [15][16][17][18][19][20][21] have all these desirable features and hence is the focus of this review.…”