Experts have used ultrasound imaging to manually determine follicle count and perform measurements, especially in cases of polycystic ovary syndrome (PCOS). However, due to the laborious and error-prone process of manual diagnosis, researchers have explored and developed medical image processing techniques to help with diagnosing and monitoring PCOS. This study proposes a combination of Otsu’s thresholding with the Chan–Vese method to segment and identify follicles in the ovary with reference to ultrasound images marked by a medical practitioner. Otsu’s thresholding highlights the pixel intensities of the image and creates a binary mask for use with the Chan–Vese method to define the boundary of the follicles. The acquired results were compared between the classical Chan–Vese method and the proposed method. The performances of the methods were evaluated in terms of accuracy, Dice score, Jaccard index and sensitivity. In overall segmentation evaluation, the proposed method showed superior results compared to the classical Chan–Vese method. Among the calculated evaluation metrics, the sensitivity of the proposed method was superior, with an average of 0.74 ± 0.12. Meanwhile, the average sensitivity for the classical Chan–Vese method was 0.54 ± 0.14, which is 20.03% lower than the sensitivity of the proposed method. Moreover, the proposed method showed significantly improved Dice score (p = 0.011), Jaccard index (p = 0.008) and sensitivity (p = 0.0001). This study showed that the combination of Otsu’s thresholding and the Chan–Vese method enhanced the segmentation of ultrasound images.

Computed Tomography (CT) image is commonly used for medical diagnosis, to monitor disease progression as well in radiotherapy planning and treatment. In basis, image registration aims to accurately align two or more monomodal or multimodal images taken at different time or places. In order to accurately register two CT images, an accurate and reliable registration algorithm is required. This paper proposes an improved Demon registration technique that uses sum of conditional variance (SCV) and multi-modality independent neighborhood descriptive (MIND) similarity metrics instead of the conventional sum squared difference (SSD) demon method to register whole body CT (PET/CT) and thoracic CT images that are acquired separately. We tested our proposed method on 9 whole body CT (PET/CT) and CT images of Non-small Cell Lung Cancer (NSCLC). Apart from visual observation, the proposed method is compared with the free form deformation (FFD) and standard demon methods. The registration accuracy was justified by measuring the lung volumes overlap between the two images post registration in terms of the Jaccard and Dice coefficients. The quality of the registered images was measured using three image quality metrics; structural similarity index (SSIM), peak signal to noise ratio (PSNR) and correlation coefficient (CC). In overall, the performance of the proposed demon is double than FFD and is superior than the standard demon. The average Jaccard and Dice coefficients are 0.83 and 0.90 respectively. Results of SSIM, PSNR and CC metrics also indicate that the improved demon method is the best, followed by FFD and standard demon.

Polycystic ovary syndrome is a common medical problem affecting women worldwide. It is attributed to excessive hormone production that causes an increase in the number of follicles in the ovaries. To detect this condition, specialist doctors examine ultrasound images by calculating the number and size of follicles in the ovaries. However, this procedure can yield inaccurate findings and endanger patients. Another tool used in this field is an ultrasonogram, but it is considered inaccurate because the noise from application processing can produce blurred images that appear to be clear contact segments. In this study, a segmentation method for detecting follicles in the ovaries is proposed to analyse the image quality of an ovarian segment. The watershed method and a combination of the edge enhancement method and Kirsch's template are implemented to create and efficiently examine a segmented image of an ovary. Results show that the proposed method with the addition of a histogram on the threshold image can be effectively used to extract the morphological closing image of follicles. Follicles can be detected more clearly, and segmentation can be performed more efficiently. The mean square error and the peak signal-to-noise ratio of the combined methods and the watershed method have no significant differences. Thus, the combined method can produce clear images and reveal the number of follicles more quickly and easily than the watershed method.