Segmentation of torso CT images

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Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü, Türkiye

Tezin Onay Tarihi: 2006

Öğrenci: ONUR ALİ DEMİRKOL

Danışman: YEŞİM SERİNAĞAOĞLU DOĞRUSÖZ

Özet:

Medical imaging modalities provide effective information for anatomic or metabolic activity of tissues and organs in the body. Therefore, medical imaging technology is a critical component in diagnosis and treatment of various illnesses. Medical image segmentation plays an important role in converting medical images into anatomically, functionally or surgically identifiable structures, and is used in various applications. In this study, some of the major medical image segmentation methods are examined and applied to 2D CT images of upper torso for segmentation of heart, lungs, bones, and muscle and fat tissues. The implemented medical image segmentation methods are thresholding, region growing, watershed transformation, deformable models and a hybrid method; watershed transformation and region merging. Moreover, a comparative analysis is performed among these methods to obtain the most efficient segmentation method for each tissue and organ in torso. Some improvements are proposed for increasing accuracy of some image segmentation methods.