| Abstract |
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Ovarian cancer position fourth in cancer deaths among women, creation it accounted for the major number of deathsinevaluation to any other cancer of the female reproductive system. A woman’s life span risk of increasingovariancancer is 1.7%.A malignant tumor of the ovary, the egg sac in a female.Ovarian cancer is complicated to identifyuntimely because there typically are no symptoms and the symptoms that do happenbe possible to be indistinct. Detection involves physical assessment,ultrasound, X-ray tests, CA 125 test, and biopsy of the ovary. Most ovarian growths in women under age 30 are benign, fluid-filled cysts. The purpose of this study is to develop a Medical Image Processing Techniquestoease the identification of ovarian cancer from ultrasound image is referred as the comprehensive study of imaging function.The goal of segmentation is to identify the correct areas and to analyze the diagnosis. Ultrasound images as texture and extracted features based on spatial-frequency content.After the extraction of feature and classification is performed have to classify the images into lesion /non lesion or benign/ malignant or normal/ abnormal classes. To improve the treatment of cancer, automated ultrasound selection techniques are used. |
