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It is challenging to develop an intelligent agent-based or robotic system to conduct long-term automatic health monitoring and robust efficient disease diagnosis as autonomous e-Carers in real-world applications. In this research, we aim to deal with such challenges by presenting an intelligent decision support system for skin lesion recognition as the initial step, which could be embedded into an...
X-ray computed tomography is an important technique for clinical diagnose and non destructive testing. In many applications a number of image processing steps are needed before the image information can be used. Obtaining a segmentation of the image is one such image processing step and also is important for applications. The conventional approach is to first reconstruct the image and conduct image...
The design of benchmark imagery for validation of image annotation algorithms is considered. Emphasis is placed on imagery that contains industrial facilities, such as chemical refineries. An application-level facility ontology is used as a means to define salient objects in the benchmark imagery. In-strinsic and extrinsic scene factors important for comprehensive validation are listed, and variability...
We propose a model-based automated approach to extracting microtubules from noisy electron tomography volume. Our approach consists of volume enhancement, microtubule localization, and boundary segmentation to exploit the unique geometric and photometric properties of microtubules. The enhancement starts with an anisotropic invariant wavelet transform to enhance the microtubules globally, followed...
The segmentation of kinetochore microtubules from electron tomography is challenging due to the poor quality of the acquired data and the cluttered cellular surroundings. We propose to automate the microtubule segmentation by extending the active shape model (ASM) in two aspects. First, we develop a higher order boundary model obtained by 3-D local surface estimation that characterizes the microtubule...
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