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Data mining techniques are used for mining useful trends or patterns from textual and image data sets. Medical data mining is very important field as it has significant utility in healthcare domain in the real world. The mining techniques can help the healthcare industry to improve quality of services and grow faster with state-of-the-art technologies. Technology usage is not limited to decision making...
In this paper, an image segmentation method is presented to analyze the clusters of Computed Tomography (CT) image. Target image is divided to small parts called as observation screens. Principal Component Analysis (PCA) is used for better representation of features about observation screens. The optimal number of component related with observation screen is determined by Horn's Parallel Analysis...
State-of-the-art Computed Tomography Angiography (CTA) scanners are capable of acquiring rigorous 3D vasculature information. Blood filled vessels are extracted from the data cloud for pathological analysis on the basis of intensity value, measured in Hounsfield units. Setting a hard threshold in CTA images for differentiating coronaries from fatty muscles of heart could be misleading as it lacks...
In this study, fuzzy input data has been considered for processing of Poisson medical image matting. Image matting is actually known as an image segmentation approach. But, fine detail information can be extracted from the background of the given image in the image matting. Although global Poisson image matting approach is applied to smoothed images, successful matting results can be obtained from...
This work proposes a context-aware middleware for medical workflow organization and efficiency improvement. In hospitals, laboratories and teleradiology companies, each physician or technician is specialized in a specific kind of diagnosis or analysis. Therefore, certain types of medical images are often forwarded to a certain physician or a certain group. This forwarding is time consuming. That is,...
In this work we present a composite method for image parameter evaluation using Scale-Invariant Feature Transform (SIFT) descriptors and bag of words representation applied to pre-selected image parameters, with potential applications to solar data and other domains. As one of the main challenges in computer vision, image parameter evaluation has been approached from supervised and unsupervised perspectives...
In this paper we propose a new hybrid image segmentation algorithm that integrate the region-based method with the boundary-based method. More specifically we take an information fusion approach based on the Tensor Voting framework that seamlessly fuse the information from the region-based Mean Shift method with the boundary-based Canny Edge Detection algorithm. We have tested our algorithm on several...
We present a novel method to automatically extract panels from figures in biomedical articles. Our method consists of figure (or panel) classification and panel segmentation. Figure classification determines the existence of photograph in a figure. A Gaussian model is constructed for photographs and plots. Figures and panels are evaluated based on the model to determine their class. If it contains...
The analysis and characterization of biomedical image data is a complex procedure involving several processing phases, like data acquisition, preprocessing, segmentation, feature extraction and classification. The proper combination and parameterization of the utilized methods are heavily relying on the given image data set and experiment type and may thus require advanced image processing and classification...
In the field of quantitative imaging, the creation of accurate volumes of interest (VOIs) is often of central importance. However, the process of creating these VOIS for multiple subjects can be time-intensive and there are many chances to introduce variability on inter- and intra-investigator levels. Although previous work has shown that image normalization through cortical surface mapping can be...
A novel method based on mixed graph structure is proposed for image representation and matching. The mixed graph structure is constructed according to the spatial relation of the regions within an image. This structure does not require redundant information to describe images. An image matching process focuses on evaluating region attributes and relationships contained in the corresponding mixed graph...
Optimization of the similarity measure is an essential theme in medical image registration. In this paper, a novel continuous medical image registration approach (CMIR) is proposed. The CMIR, considering the feedback from users and their preferences on the trade-off between global registration and local registration, extracts the concerned region by user interaction and continuously optimizing the...
Knowledge about the status of the female reproductive system is important for fertility problems and age related family planning. The volume of these fertility requests in our emancipated society is steadily increasing. Transvaginal ultrasound imaging of the follicles in the ovary gives important information about the ovarian aging, i.e., number of follicles, size, position and response to hormonal...
The effective segmentation of moving impurity in medicinal liquid plays a key role in automatic detection. An adaptive threshold segmentation algorithm of visual impurity in medicinal liquid based on fuzzy logic algorithm is proposed. At first, the grey difference image acquired from 5 continuous frames is partitioned into 5??5 blocks, and then the mean, variance and fourth-order statistics of the...
Mammograms can depict most of the significant changes of breast disease. The primary radiographic signs of cancer are masses (its density, site, shape, borders), spicular lesions and calcification content.These features may be extracted using various detection system .The common are Neural network, wavelet, fuzzy logic, evolutionary approach and finally hybrid system ,which employs integration of...
MRI medical image segmentation is one of important problem in medical image processing. It is more challenging compared to other image processing problems due to the large variability in shapes, complexity of medical structures. In the paper, a new segmentation approach of magnetic resonance brain tissues image based on support vector machines (SVM) and level set method is presented. Firstly, reduced...
Traumatic pelvic injuries are complex and difficult to treat, due to the high risk of complications. Prompt and accurate medical treatment is therefore vital. Computer-aided decision-making systems can assist physicians in this task, but none of those proposed so far incorporate features extracted from medical images. The study in this paper uses demographic information, standard medical measurements,...
A new method for visualisation and segmentation of vessel structures in 3D magnetic resonance angiography (MRA) images is presented. This method uses a simple statistical model of the information stored along parallel rays within the data set to derive a 2D projection image. Although similar to the maximum image projection (MIP) method, the new method uses a single parameter to achieve a higher contrast-to-noise...
New imaging devices provide image data at very high spatial resolution acquisition and throughput rate. In satellite or medical two-dimensional images, high-content and large image issues plead for more high semantic level interactions between the computer vision systems and the end-users in order to leverage the cognitive symbiosis between both systems for practical tasks such as clinical disease...
Extracting hepatic vasculature from three dimensional imagery is important for diagnosis of liver disease and planning of liver surgery. In this paper we propose a method for generation of 3D skeletal graph of liver vessels using thinning algorithm and graph theory. First of all, basic methodology in the proposed method is introduced. Secondly, the skeletonization method together with a pre-processing...
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