International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Open Access | Fully Refereed | Peer Reviewed

ISSN: 2319-7064




Downloads: 109

Survey Paper | Computer Science & Engineering | India | Volume 3 Issue 11, November 2014


Identification of Ovarian Mass: A Survey

Hemita Pathak, Vrushali Kulkarni, Sarika Bobde


Abstract: 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.


Keywords: Ultrasound images, Pre-processing, Feature extraction, Classification


Edition: Volume 3 Issue 11, November 2014,


Pages: 965 - 969


How to Cite this Article?

Hemita Pathak, Vrushali Kulkarni, Sarika Bobde, "Identification of Ovarian Mass: A Survey", International Journal of Science and Research (IJSR), https://www.ijsr.net/get_abstract.php?paper_id=OCT14958, Volume 3 Issue 11, November 2014, 965 - 969

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