Fully Automatic Segmentation of Gynaecological Abnormality Using a New Viola-Jones Model

• Published in: Computers, materials & continua materials & continua, 2021, Vol.66 (3), p.3161-3182
• Main Authors: Jasim Hussein, Ihsan, Burhanuddin, M.A., Abed Mohammed, Mazin, Elhoseny, Mohamed, Garcia-Zapirain, Begonya, Suliman Maashi, Marwah, S. Maashi, Mashael
• Format: Article
• Language: English
• Subjects: Accuracy; Breast cancer; Computer aided decision processes; Datasets; Decision support systems; Image segmentation; Medical imaging; Ovarian cancer; Sensitivity; Task complexity; Tumors; Ultrasonic imaging; Ultrasound
• ISSN: 1546-2226; 1546-2218; 1546-2226
• DOI: 10.32604/cmc.2021.012691

One of the most complex tasks for computer-aided diagnosis (Intelligent decision support system) is the segmentation of lesions. Thus, this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound images. The main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer cases. In addition, proposed an approach that can efficiently generate region-of-interest (ROI) and new features that can be used in characterizing lesion boundaries. This study uses two databases in training and testing the proposed segmentation approach. The breast cancer database contains 250 images, while that of the ovarian tumor has 100 images obtained from several hospitals in Iraq. Results of the experiments showed that the proposed approach demonstrates better performance compared with those of other segmentation methods used for segmenting breast and ovarian ultrasound images. The segmentation result of the proposed system compared with the other existing techniques in the breast cancer data set was 78.8%. By contrast, the segmentation result of the proposed system in the ovarian tumor data set was 79.2%. In the classification results, we achieved 95.43% accuracy, 92.20% sensitivity, and 97.5% specificity when we used the breast cancer data set. For the ovarian tumor data set, we achieved 94.84% accuracy, 96.96% sensitivity, and 90.32% specificity.