Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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Subject: search.filters.subject.image segmentation

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    A Learning-Based Resegmentation Method for Extraction of Buildings in Satellite Images
    (2014) Dikmen, Mehmet; Halici, Ugur; https://orcid.org/0000-0002-0584-5577; AAG-8859-2019
    This letter introduces a new method for building extraction in satellite images. The algorithm first identifies the shadow segments on an oversegmented image, and then neighboring shadow segments, which are assumed to be cast by a single building, are merged. Next, candidate regions where buildings most likely occur are detected by using these shadow regions. Along with this information, closeness to shadows in illumination direction and spectral properties of segments are used to classify them as belonging to a "building" or not. Then, a resegmentation is performed by merging only the neighboring segments, which are classified as building. Finally, postprocessing is performed to eliminate some false building segments. The approach was tested on several Google Earth images, and the results are found to be promising.
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    Automatic Glacoma Detection Using Whale Optimization and Support Vector Machines
    (2022) Ozen, Sinasi Kutay; Aksahin, Mehmet Feyzi
    Glaucoma is among the most common causes of permanent blindness in humans. The mass screening will aid in early diagnosis in a large population, as the initial symptoms are not obvious. This type of mass screening requires an automated diagnostic technique. Our proposed automation extracts feature by obtaining disk-to-cup ratio by applying histogram equalization, median filter, otsu thresholding, and whale optimization algorithm, respectively, on the optic disc region obtained by preprocessing. In addition, the optic disc circumference, optic disc area, optic cup circumference, and optic cup area values obtained from the optic disc region are given to the support vector machine model together with the cup-disc ratio, and glaucoma detection is made automatically. The proposed system has been validated on a real ophthalmological images of both normal and glaucoma cases. The results show the effectiveness of the proposed method when compared with other existing systems.
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    Automatic Vascular Segmentation on Angio Images
    (2017) Akshain, Mehmet Feyzi; Ozen, S. Kutay; Eren, Neyyir Tuncay
    Cardiovascular disease is one of today's major health problems. These diseases are the result of constriction or blockage of coronary vessels feeding heart. Diagnosis of Cardiovascular contraction is determined visually by physicians with angio imaging method. Visually defined vascular diseases may give subjective results. Vessel constrictions in angiograms are automatically determined by vascular segmentation to help physicians diagnose cardiovascular stenosis and to minimize subjective results. In present study, adaptive thresholding method and frangi filter were applied to the pre-processed angiogram images. After this application, the noise cleaning method on the determined cardiovascular image was performed and the vein structure was detected with high accuracy rate and low calculation time.