Yazılım Mühendisliği Bölümü Yayın Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/2147
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Article A Combined Spatial and Frequency Based Texture Model for Organ Segmentation in Computed Tomography Examinations(Amer Scientific Publishers, 2014) Hassanpour, Reza; Shahbahrami, Asadollah; Wong, Stephan; Jafari, ArefThe organ segmentation in computed tomography (CT) examination is a tedious and error prone task. The local similarity of the pixels from different organs, and the differences between the pixels of the same organ observed in different examinations are two most challenging problems affecting the segmentation process. In this study, statistical and spectral texture properties are combined with the a-priori knowledge about the human body to develop a model for reliably segmenting organs in CT examinations. The main goal of the developed model is fusing local and global statistics to support spatial-frequency analysis and to maximize the simultaneous localization of energy in both spatial and frequency domains. The feature space dimension is reduced by means of a wrapper technique applied as a pre-processing filter. The proposed classifier utilizes a linear combination (ensemble) of two support vector machines (SVM) where the first SVM classifies the input samples according to their textural information and the second one correct the results of the first classifier by searching the spatial information of those samples in a statistical atlas.Article A Two-Stage Matching Method for Multi-Component Shapes(Univ Suceava, Fac Electrical Eng, 2015) Hassanpour, RezaIn this paper a shape matching algorithm for multiple component objects has been proposed which aims at matching shapes by a two-stage method. The first stage extracts the similarity features of each component using a generic shape representation model. The first stage of our shape matching method normalizes the components for orientation and scaling, and neglects minor deformations. In the second stage, the extracted similarity features of the components are combined with their relative spatial characteristics for shape matching. Some important application areas for the proposed multi-component shape matching are medical image registration, content based medical image retrieval systems, and matching articulated objects which rely on the a-priori information of the model being searched. In these applications, salient features such as vertebrae or rib cage bones can be easily segmented and used. These features however, show differences from person to person on one hand and similarities at different cross-sectional images of the same examination on the other hand. The proposed method has been tested on articulated objects, and reliable registration of 3-dimensional abdominal computed tomography images.