Ovarian tumor characterization and classification using ultrasound-a new online paradigm

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.

Fig. 1

Block diagram of the proposed system for ovarian tumor characterization and classification; the blocks outside the shaded rounded rectangular box represent the flow of offline training system, and the blocks within the box represent the online real-time system

Fig. 2

Ultrasound images of the ovary: (B1–B2) benign conditions (M1–M2) malignant tumors. B1: histology: endometrioma; echo: characteristic diffuse, low-level echoes of the endometrioma giving a solid appearance. B2: histology: endometrioma; echo: characteristic diffuse, low-level echoes of the endometrioma giving a solid appearance. M1: histology: borderline malignant tumor; echo: multiloculate echo-pattern with multiple thick septa. M2: histology: serous carcinoma; echo: homogenous hyperechogen

Fig. 3

Principal domain region (Ω) used for the computation of the bispectrum for real signals