Polycystic Ovarian Syndrome (peOS) is one of the most common hormonal disorder present in females in reproductive age group. Early detection and treatment of peos is important since it is often associated with obesity, type 2 diabetes mellitus, and high cholesterol levels. In this paper, automated detection of peos is done by calculating no of follicles in ovarian ultrasound image and then incorporating clinical, biochemical and imaging parameters to classify patients in two groups i.e. normal and peos affected. Number of follicles are detected by ovarian ultrasound image processing using preprocessing which includes contrast enhancement and filtering, feature extraction using Multiscale morphological approach and segmentation. Support Vector Machine algorithm is used for classification which takes into account all the parameters such as body mass index (BMI), hormonal levels, menstrual cycle length and no of follicles detected in ovarian ultrasound image processing. The results obtained are verified by doctors and compared with manual detection. The accuracy obtained for the proposed method is 95%.
Breast cancer is the most common cancer in women.Findings show that early detection of breast cancer can improve survival rate.Mammography is the standard method of diagnosing breast cancer;but Infrared Breast thermography is an imaging technique that provides information based on the temperature changes in breast.This information is precancerous alarms that may lead to tumor.Thermography helps to detect cancer in its early stages,thus survival is possible.Thermal or Infrared radiations emitted from human body are higher around the regions where an tumour is present essentially due to increased cell activity. The thermal information can be shown in a pseudo coloured image where each colour represents a specific range of temperature.Clinical interpretation of breast thermogram is primarily based on colour analysis of the heat patterns visually and subjectively.This study endeavours to present analysis of breast thermogram based on segmentation of region of interest which is extracted a hot region followed by color analysis.The shape, size and borders of the hottest regions of the images can help to determine features which are used to detect abnormalities.The abnormality of breast thermograms is indicated by these features and it is confirmed by comparing with doctors diagnosis.Hence suitability of infrared thermography as a diagnostic tool in breast cancer detection is establihed through this study.
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