Browsing by Subject "Cervical cancer"
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Item An efficient approach to automated segmentation in medical image analysis(Texas Tech University, 2005-12) Gao, Fei; Mitra, Sunanda; Mitra, Sunanda; Karp, Tanja; Nutter, Brian; Nutter, Brian; Karp, TanjaAutomatic image segmentation is widely used in medical image analysis. However, an efficient way of automated segmentation is difficult to achieve. In this thesis, a survey of current image segmentation methods and their possible applications to identify Cervical Intraepithelial Neoplasia (CIN) are introduced. Approaches to Cervix image segmentation and analysis are discussed. A very efficient algorithm for segmentation of acetowhite regions is developed, verified and compared with other existing methodologies in this thesis. Several image processing methodologies and mathematical operations are exploited and applied to this research work. Although the success of the applied algorithms is highly dependent on the quality of the images used, statistical results regarding the feature extraction, running time and pattern classification are obtained and found to be quite satisfactory. Further identification and classification of some of the lesions within the acetowhite regions of the uterine cervix have also been achieved. This efficient automatic segmentation and classification methodology will greatly facilitate content-based image retrieval from digital archives of cervix images and has the potential of playing a significant role to the development of an image-based screening tool for Cervical Cancer.Item An efficient approach to automated segmentation in medical image analysis(2006-05) Gao, Fei; Mitra, Sunanda; Karp, Tanja; Nutter, BrianAutomatic image segmentation is widely used in medical image analysis. However, an efficient way of automated segmentation is difficult to achieve. In this thesis, a survey of current image segmentation methods and their possible applications to identify Cervical Intraepithelial Neoplasia (CIN) are introduced. Approaches to Cervix image segmentation and analysis are discussed. A very efficient algorithm for segmentation of acetowhite regions is developed, verified and compared with other existing methodologies in this thesis. Several image processing methodologies and mathematical operations are exploited and applied to this research work. Although the success of the applied algorithms is highly dependent on the quality of the images used, statistical results regarding the feature extraction, running time and pattern classification are obtained and found to be quite satisfactory. Further identification and classification of some of the lesions within the acetowhite regions of the uterine cervix have also been achieved. This efficient automatic segmentation and classification methodology will greatly facilitate content-based image retrieval from digital archives of cervix images and has the potential of playing a significant role to the development of an image-based screening tool for Cervical Cancer.Item Feature extraction and classification of precancerous cervix lesions(Texas Tech University, 2005-08) Hernes, Dana L.; Mitra, Sunanda; Karp, TanjaCervical cancer is the second- most common type of cancer in women worldwide. Although great strides have been made in the prevention of invasive cervical cancer, these measures are not available to all women. Because trained personnel are limited in developing countries, the death rate is significantly higher than in developed countries like the United States. Therefore it is necessary to develop a method to automatically determine the region of abnormal cervix tissue to biopsy and have the methodology available to worldwide areas. This paper discusses the techniques investigated to create a fully automated system to locate precancerous regions in an image of a cervix generated by a digital colposcope or cerviscope. Segmentation was used to first isolate the acetowhite region, the region of interest, from the remainder of the image for further processing. Automatic identification of some precancerous markers within the acetowhite region was developed by extracting significant features. Some of the precancerous markers take the form of mosaicism and punctation. Color and geometric features based classification was used to locate these regions. The k-means clustering technique was applied in classifying mosaic regions from normal regions within a digital cervix image.Item Feature extraction and classification of precancerous cervix lesions(2005-08) Hernes, Dana L.; Mitra, Sunanda; Karp, TanjaCervical cancer is the second- most common type of cancer in women worldwide. Although great strides have been made in the prevention of invasive cervical cancer, these measures are not available to all women. Because trained personnel are limited in developing countries, the death rate is significantly higher than in developed countries like the United States. Therefore it is necessary to develop a method to automatically determine the region of abnormal cervix tissue to biopsy and have the methodology available to worldwide areas. This paper discusses the techniques investigated to create a fully automated system to locate precancerous regions in an image of a cervix generated by a digital colposcope or cerviscope. Segmentation was used to first isolate the acetowhite region, the region of interest, from the remainder of the image for further processing. Automatic identification of some precancerous markers within the acetowhite region was developed by extracting significant features. Some of the precancerous markers take the form of mosaicism and punctation. Color and geometric features based classification was used to locate these regions. The k-means clustering technique was applied in classifying mosaic regions from normal regions within a digital cervix image.Item How the Rhetorical Assertion ‘Men Don’t Need to be and/or Can’t be Medically Diagnosed for High Risk Human Papillomavirus (hrHPV)’ has been Framed in Governmental and Scientific Infectious Disease Discourse(2013-05) Wainscott, Melody; Carter, Joyce L.; Koerber, Amy“Men don’t need to be and/or can’t be medically diagnosed for hrHPV”, an amalgam of assertive statements collected from the Centers Disease Control and Prevention (CDC) publication “HPV and Men – CDC Fact Sheet” and from specific scientific infectious disease discourse, is a controversial medical “theme.” A more rhetorically accurate theme is “medical diagnosis of hrHPV in males is often shown through discourse to have no value.” Determining the value of hrHPV diagnosis in males, however, is not the objective of this thesis. The objective of this thesis is to document the discussion surrounding the theme "Men don’t need to be and/or can’t be medically diagnosed for hrHPV” in selected governmental and scientific discourses, to classify how the theme is framed, to question perceived risks to men’s health because of the theme, to classify how the questions intersect with other hrHPV subjects, and to show that men are basically an afterthought in the subject of the sexually transmitted infection (STI) hrHPV that causes several forms of potentially deadly cancer in men and women and is the primary cause of cervical cancer in women.