Browsing by Author "Marvasti, Neda Barzegar."

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    Content based medical image retrieval
    (Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2017., 2017.) Marvasti, Neda Barzegar.; Acar, Burak.
    Fast technological developments of di erent medical imaging and data collection techniques increase the expectation of more accurate interpretations and diagnoses of radiologists. However, to carefully analyze the resulting big medical data, reliable and fast systems are needed. Content-based medical image retrieval (CBMIR) is a valuable technique to assist radiologists by identifying similar images in a large archive. However, due to the huge semantic gap between low-level image features and high-level semantic features, the challenge of retrieving similar images utilizing the high-level user speci ed semantic labels, which are closer to the users understandings and interpretations, has attracted great interest from various researches. In this dissertation, an iterative search and retrieval scheme to identify similar images from a database of 3-dimensional liver computed tomography (CT) images is proposed via utilizing the combination of lesion and liver related semantic features and patients' metadata. At each retrieval iteration, the lesion related concepts are annotated in a speci c order through a proposed computer aided medical image annotation (CMIA) scheme. The proposed radiologist-in-the-loop semi-automatic CMIA is based on a Bayesian tree structured model, linked to RadLex, to exploit the inter-dependencies between concepts to update the full annotation process and to guide the radiologist to input the most critical information at each iteration. Results show the e ectiveness of this modelbased interactive annotation scheme compared to the domain-blind models, as well as its advantage in the performance of the retrieval system, where a few number of manual annotations can signi cantly boost the retrieval accuracy. Moreover, better retrieval performance is achieved by incorporating a small contribution of the non-lesion data.