The development of aid's systems for the medical diagnosis is not easy thing because of presence of inhomogeneities in the MRI, the variability of the data from a sequence to the other as well as of other different source distortions that accentuate this difficulty. A new automatic, contextual, adaptive and robust segmentation procedure by MRI brain tissue classification is described in this ar...

This paper proposes a convolutional neural network that can fuse high-level prior for semantic image segmentation. Motivated by humans’ vision recognition system, our key design is a three-layer generative structure consisting of high-level coding, middle-level segmentation and low-level image to introduce global prior for semantic segmentation. Based on this structure, we proposed a generative...

In this paper, we present an original image segmentation model based on a preliminary spatially adaptive non-linear data dimensionality reduction step integrating contour and texture cues. This new dimensionality reduction model aims at converting an input texture image into a noisy color image in order to greatly simplify its subsequent segmentation. In this latter de-texturing model, the (spa...

Introduction: Segmentation of brain images especially from magnetic resonance imaging (MRI) is an essential requirement in medical imaging since the tissues, edges, and boundaries between them are ambiguous and difficult to detect, due to the proximity of the brightness levels of the images. Material and Methods: In this paper, the graph-base...

The general image segmentation approach used in other domains may not be applicable to the remote sensing field, which is due to the following factors: remotely sensed data is multi-spectral, always very large in size, and in multi-scale as well. How to quickly and efficiently segment remotely sensed imagery is still a big issue to be solved. Based on human vision mechanism, a new hybrid multi-...

Brain segmentation in head computed tomography scans is essential for the development of computer-aided diagnostic methods for identifying the brain diseases. In this paper we present a hybrid framework to brain segmentation which joints region-based information based on watershed transform with clustering techniques. A pre-processing step is used to reduce the spatial resolution without losing...

-A new hybrid method is presented that combines the scale space filter (SSF) and Markov random field (MRF) for color image segmentation. The fundamental idea of the SSF is to use the convolution of Gaussian functions and image-histogram to generate a scale space image and then find the proper interval bounded by the local extrema of the derivatives. The Gaussian function is with zero mean and v...

a multi‑parameter quantification method was implemented to quantify retinal vascular injuries in microscopic images of clinically relevant eye diseases. this method was applied to wholemount retinal trypsin digest images of diabetic akita/+, and bcl‑2 knocked out mice models. five unique features of retinal vasculature were extracted to monitor early structural changes and retinopathy, as well ...

segmentation and three‑dimensional (3d) visualization of teeth in dental computerized tomography (ct) images are of dentists’ requirements for both abnormalities diagnosis and the treatments such as dental implant and orthodontic planning. on the other hand, dental ct image segmentation is a difficult process because of the specific characteristics of the tooth’s structure. this paper presents ...