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Design And Development Of Medical Image Processing Techniques and to Study their Applications Using Graphical System Design in Ovarian Cancer

机译:医学图像处理技术的设计与开发及其图形系统设计在卵巢癌中的应用研究

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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 on lesion or benign/ malignant or normal/ abnormal classes. To improve the treatment of cancer, automated ultrasound selection techniques are used.
机译:卵巢癌在女性癌症死亡中位居第四,这导致其死亡人数占女性生殖系统任何其他癌症价值的绝大部分。女性一生中罹患卵巢癌的风险为1.7%。卵巢恶性肿瘤是女性的卵囊。卵巢癌的诊断很复杂,因为通常没有任何症状,而且这种症状可能难以区分。检测包括体格检查,超声,X射线检查,CA 125检查和卵巢活检。 30岁以下女性的大部分卵巢生长是良性的,充满液体的囊肿。这项研究的目的是开发一种医学图像处理技术,以便从超声图像识别卵巢癌被称为成像功能的综合研究。分割的目的是识别正确的区域并分析诊断。超声图像是基于空间频率内容的纹理和提取的特征。在特征提取和分类之后,必须将图像分类为病变/非病变或良性/恶性或正常/异常类别。为了改善癌症的治疗,使用了自动超声选择技术。

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