1,345 research outputs found

    TEXTURE SEGMENTATION METHODS FOR SATELLITE RADAR IMAGES

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    Segmentation for Synthetic Aperture Radar (SAR) is a very important aspect for satellite radar images. It is important to separate areas that be clustered based on characteristics or features of the image. Nowadays, there have a lot of segmentation techniques of SAR images. In this thesis, the techniques being investigated are edge adaptive smoothing, watershed transform, mean shft segmentation and region merging via boundary melting techniques which is the best among segmentation techniques. The comparison or evalution among the techniques is in term of number of edges retained in the segmented images and also in visual inspection. In this research, we use two different type of images, which is real SAR image and non-real SAR image (house). Results generated from this research has shown that edge adaptive smoothing is the best one compared to the other segmentation techniques

    Co-Segmentation Methods for Improving Tumor Target Delineation in PET-CT Images

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    Positron emission tomography (PET)-Computed tomography (CT) plays an important role in cancer management. As a multi-modal imaging technique it provides both functional and anatomical information of tumor spread. Such information improves cancer treatment in many ways. One important usage of PET-CT in cancer treatment is to facilitate radiotherapy planning, for the information it provides helps radiation oncologists to better target the tumor region. However, currently most tumor delineations in radiotherapy planning are performed by manual segmentation, which consumes a lot of time and work. Most computer-aided algorithms need a knowledgeable user to locate roughly the tumor area as a starting point. This is because, in PET-CT imaging, some tissues like heart and kidney may also exhibit a high level of activity similar to that of a tumor region. In order to address this issue, a novel co-segmentation method is proposed in this work to enhance the accuracy of tumor segmentation using PET-CT, and a localization algorithm is developed to differentiate and segment tumor regions from normal regions. On a combined dataset containing 29 patients with lung tumor, the combined method shows good segmentation results as well as good tumor recognition rate

    IMCAD: Computer Aided System for Breast Masses Detection based on Immune Recognition

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    Computer Aided Detection (CAD) systems are very important tools which help radiologists as a second reader in detecting early breast cancer in an efficient way, specially on screening mammograms. One of the challenging problems is the detection of masses, which are powerful signs of cancer, because of their poor apperance on mammograms. This paper investigates an automatic CAD for detection of breast masses in screening mammograms based on fuzzy segmentation and a bio-inspired method for pattern recognition: Artificial Immune Recognition System. The proposed approach is applied to real clinical images from the full field digital mammographic database: Inbreast. In order to validate our proposition, we propose the Receiver Operating Characteristic Curve as an analyzer of our IMCAD classifier system, which achieves a good area under curve, with a sensitivity of 100% and a specificity of 95%. The recognition system based on artificial immunity has shown its efficiency on recognizing masses from a very restricted set of training regions

    Effects of Noninhibitory Serpin Maspin on the Actin Cytoskeleton: A Quantitative Image Modeling Approach

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    Recent developments in quantitative image analysis allow us to interrogate confocal microscopy images to answer biological questions. Clumped and layered cell nuclei and cytoplasm in confocal images challenges the ability to identify subcellular compartments. To date, there is no perfect image analysis method to identify cytoskeletal changes in confocal images. Here, we present a multidisciplinary study where an image analysis model was developed to allow quantitative measurements of changes in the cytoskeleton of cells with different maspin exposure. Maspin, a noninhibitory serpin influences cell migration, adhesion, invasion, proliferation, and apoptosis in ways that are consistent with its identification as a tumor metastasis suppressor. Using different cell types, we tested the hypothesis that reduction in cell migration by maspin would be reflected in the architecture of the actin cytoskeleton. A hybrid marker-controlled watershed segmentation technique was used to segment the nuclei, cytoplasm, and ruffling regions before measuring cytoskeletal changes. This was informed by immunohistochemical staining of cells transfected stably or transiently with maspin proteins, or with added bioactive peptides or protein. Image analysis results showed that the effects of maspin were mirrored by effects on cell architecture, in a way that could be described quantitatively
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