As the information carried in a high spatial resolution image is not represented by single pixels but by meaningful image objects, which include the association of multiple pixels and their mutual relations, the object based method has become one of the most commonly used strategies for the processing of high resolution imagery. This processing comprises two fundamental and critical steps towar...

This paper presents a novel, edge-based segmentation method for identifying the mass contour (boundary) for a suspicious mass region (Region of Interest (ROI)) in a mammogram. The method first applies a contrast stretching function to adjust the image contrast, then uses a filtering function to reduce image noise. Next, for each pixel in a ROI, the energy descriptor (one of the Haralick descrip...

Human activity datasets collected under natural conditions are an important source of data. Since these contain multiple activities in unscripted sequence, temporal segmentation of multimodal datasets is an important precursor to recognition and analysis. Manual segmentation is prohibitively time consuming and unsupervised approaches for segmentation are unreliable since they fail to exploit th...

The preoperative planning of primary tumor resections in the larynx region shall be supported by a 3D visualization of the patientspecific anatomy and pathological situation. This requires a segmentation of the larynx cartilage structures from CT datasets. In our work, we apply and adapt stable 3D mass-spring models to create a 3D deformable shape model for the segmentation of the thyroid carti...

Mass Spectrometric Imaging (MSI) is a molecular imaging technique that allows the generation of 2D ion density maps for a large complement of the active molecules present in cells and sectioned tissues. Automatic segmentation of such maps according to patterns of co-expression of individual molecules can be used for discovery of novel molecular signatures (molecules that are specifically expres...

In this work an automatic method to discover multiple sclerosis (MS) lesions is presented. The technique makes use of an original implementation of the watershed based hierarchical segmentation algorithm applied to Magnetic Resonance (MR) images of the brain. The hierarchical approach is needed because of its merging phase where lesions are isolated from the over-segmented image produced by wat...

We developed variational models for image segmentation that incorporate histogram information into level set based curve evolution techniques. The novelty is in the use of Wasserstein mass transfer metrics in order to compare histograms; we found that this improves the results significantly over previous Left: Segmentation based on average intensities via ChanVese model. Right: Proposed model b...

in analyzing a signal, especially a non-stationary signal, it is often necessarythe desired signal to be segmented into small epochs. segmentation can beperformed by splitting the signal at time instances where signal amplitude orfrequency change. in this paper, the signal is initially decomposed into signals withdifferent frequency bands using wavelet transform. then, fractal dimension of thed...

data mining techniques have been used widely in the area of customer relationship management (crm). in this study, we have applied data mining techniques to address a problem in business-to-business (b2b) setting. in a manufacturer-retailer-consumer chain, a manufacturer should improve its relationship with retailers to continue its business. segmentation is a useful tool for identifying groups...

  Introduction: The process of discriminating among benign and malignant hyperplasia begun with subjective methods using light microscopy and is now being continued with computerized morphometrical analysis requiring some features. One of the main features called Volume Percentage of Stroma (VPS) is obtained by calculating the percentage of stroma texture. Currently, this feature is calculated ...