Identification of Ovarian Mass: A Survey

Ovarian Cancer is leading cause of cancer deaths in women today. Early detection of the cancer can reduce mortality rate. Studies have shown that radiologists can miss the detection of a significant proportion of abnormalities in addition to having high rates of false positives. To detect malignancy, methods of pattern recognition and image processing are used. Pattern recognition in image processing requires the extraction of features from regions of the image and the processing of these features with a pattern recognition algorithm. In recent age, cases of ovarian cancer are increasing day by day, so diagnosis of ovarian cancer should be appropriate and up to the mark. Ultrasound imaging is widely used for diagnosis over the other imaging modalities like Positron Emission Tomography (PET), Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) due to its noninvasive nature, portable, accurate, harmless to the human beings and capability of forming real time imaging. In this paper we have presented some of the techniques for diagnosis ovarian malignancy. Generally there are main three phases to detect the malignancy. First is pre-processing in that need to smooth the image, second is feature extraction from the image and third is classification of the features.