Ovarian masses are categorised into different types of malignant and benign. In order to optimize patient treatment, it is necessary to carry out pre-operational characterisation of the suspect ovarian mass to determine its category. Ultrasound imaging has been widely used in differentiating malignant from benign cases due to its safe and non-intrusive nature, and can be used for determining the number of cysts in the ovary. Presently, the gynaecologist is tasked with manually counting the number of cysts shown on the ultrasound image. This paper proposes, a new approach that automatically segments the ovarian masses and cysts from a static B-mode image. Initially, the method uses a trainable segmentation procedure and a trained neural network classifier to accurately identify the position of the masses and cysts. After that, the borders of the masses can be appraised using watershed transform. The effectiveness of the proposed method has been tested by comparing the number of cysts identified by the method against the manual examination by a gynaecologist. A total of 65 ultrasound images were used for the comparison, and the results showed that the proposed solution is a viable alternative to the manual counting method for accurately determining the number of cysts in a US ovarian image.
Ultrasound imagery has been widely used for medical diagnoses. Ultrasound scanning is safe and non-invasive, and hence used throughout pregnancy for monitoring growth. In the first trimester, an important measurement is that of the Gestation Sac (GS). The task of measuring the GS size from an ultrasound image is done manually by a Gynecologist. This paper presents a new approach to automatically segment a GS from a static B-mode image by exploiting its geometric features for early identification of miscarriage cases. To accurately locate the GS in the image, the proposed solution uses wavelet transform to suppress the speckle noise by eliminating the high-frequency sub-bands and prepare an enhanced image. This is followed by a segmentation step that isolates the GS through the several stages. First, the mean value is used as a threshold to binarise the image, followed by filtering unwanted objects based on their circularity, size and mean of greyscale. The mean value of each object is then used to further select candidate objects. A Region Growing technique is applied as a post-processing to finally identify the GS. We evaluated the effectiveness of the proposed solution by firstly comparing the automatic size measurements of the segmented GS against the manual measurements, and then integrating the proposed segmentation solution into a classification framework for identifying miscarriage cases and pregnancy of unknown viability (PUV). Both test results demonstrate that the proposed method is effective in segmentation the GS and classifying the outcomes with high level accuracy (sensitivity (miscarriage) of 100% and specificity (PUV) of 99.87%).