پديد آورنده :
شاهدي باغ خندان ، ميثم
عنوان :
تشخيص خود كار خوشه هاي ميكرو كلسيفيكيشن در مامو گرام هاي ديجيتال با استفاده از پردازش چند دقتي
مقطع تحصيلي :
كارشناسي ارشد
گرايش تحصيلي :
﴿الكترونيك﴾
محل تحصيل :
اصفهان :دانشگاه صنعتي اصفهان،دانشكده برق وكامپيوتر
صفحه شمار :
نه، ،11،[II]ص:جدول، شكل ﴿بخش رنگي ﴾ ،مصور ،نمودار
يادداشت :
ص . ع : به فارسي و انگليسي
استاد راهنما :
رسول امير فتاحي ، فرح تركمني آذر
توصيفگر ها :
سرطان سينه , مامو گرافي , بانكهاي فيلتر , الگوريتم خوشه بندي
دانشكده :
مهندسي برق و كامپيوتر
چكيده فارسي :
به فارسي و انگليسي : قابل رويت در نسخه ديجيتالي
چكيده انگليسي :
Abstract Breast cancer is the most common form of cancer and the main reason for the mortalitycaused by cancers among women Mammography is the most effective procedure for anearly diagnosis of the breast cancer Due to low doze X ray Imaging of breast soft tissue the contrast in mammograms especially in dense breasts is low Detection ofmicrocalcifications which are one of the main features considered by radiologists fordiagnosis is so difficult especially in the first stages of forming According to physicians the early diagnosis of lesions in mammography would decrease the rate of breast cancermortality In this research an algorithm for detection of microcalcification clusters indigital mammograms based on special features extraction and multi resolution analysiswas proposed The algorithm consists of four main parts Segmentation algorithm separatesthe breast region from the background Image by using a new histogram thresholdingmethod It reduces the background noise effect by focusing the detection algorithms onbreast region The Second part is an initial detection algorithm that is applied to the Regionof Interest ROI to separate uncertain parts in which microcalcifications may be present By using this algorithm the size of ROI was reduced So the execution time and falsepositive rate will decrease in the main detection algorithm In the main detection algorithm a multi resolution analysis by means of the wavelet packet transform WPT for detectingmicrocalcifications is exploited Noise reduction and microcalcification detection isperformed using WPT coefficients analysis Finally a clustering algorithm based onmorphological methods was proposed that can separate clustered microcalcifications fromsingle ones From radiography point of view clustered microcalcifications are moreimportant than scattered ones The final results show that using WPT instead of discretewavelet transform improves the detection algorithm and reduces the false detections rate
استاد راهنما :
رسول امير فتاحي ، فرح تركمني آذر
