Contextual information and a priori knowledge play important roles in image segmentation based on neural networks. This paper proposed a method for including contextual information in a model-based neural network (MBNN) that has the advantage of combining a priori knowledge. This is achieved by including Markov random field (MRF) into the MBNN and this novel neural network is termed as MRF-MBNN...

A bilevel image can be modeled by the Markov random field (MRF). Let G = (V,E) be an 8-connected graph, where V and E denote the nodes (vertices) and edges, modeling the pixels and the connectivities in an image, respectively. The MRF with 2-point cliques is defined on the 8-connected graph. The 2-point cliques consist of horizontal, vertical and diagonal neighboring nodes (Figure 1a). A node i...

Markov random field MRF is a widely used probabilistic model for expressing interaction of different events. One of the most successful applications is to solve image labeling problems in computer vision. This paper provides a survey of recent advances in this field. We give the background, basic concepts, and fundamental formulation of MRF. Two distinct kinds of discrete optimization methods, ...

Recently evidence has accumulated that there are various interactions between the different levels of the central reticular core of the brain stem. (DELL, BONVALLET and HUGELIN 1961 ; MORUZZI 1964). The present study was initiated to know how the mesencephalic reticular formation (MRF) acts to regulate activity of the pontine reticular formation (PRF). This MRF-originated, descending effect con...

Sensor networks are often used in environment monitoring. We consider uncertain reasoning in sensor network-based monitoring, in particular, in detecting and tracking plumes under heavy noise. We extend Markov random field to a new time-dynamic Markov random field (TD-MRF) and use it to model the environment. We provide an algorithm for inferring, based on TD-MRF, the plume situation in the env...

In this paper we develop a novel MRF formulation for calculating sparse features correspondence in image pairs. Our MRF terms can include cliques of variable sizes, and solve these using Loopy Belief Propagation. To calculate our MRF topology we develop a variant of the K-means algorithm which we call the KN-means algorithm (where each mean has a specified number of neighbours). The method is c...

Magnetic resonance (MR) medical image segmentation plays an increasingly important role in computer-aided detection and diagnosis (CAD) of abnormalities. MRI segmentation manually is time consuming and consumes valuable human resources. Hence a great deal of efforts has been made to automate this process. Markov Random Field (MRF) has been one of the most active research areas of MRI brain segm...

One of the classical optimization models for image segmentation is the well known Markov Random Fields (MRF) model. MRF formulates many total variation and other optimization criteria used in image segmentation. In spite of the presence of MRF in the literature, the dominant perception has been that the model is not effective for image segmentation. We show here that the reason for the non-effe...

Abstract PURPOSE The utility of post-contrast magnetic resonance fingerprinting (MRF) for brain tumor assessment has not been assessed. aim this work is to report differences in MRF-derived delta-relaxometry metrics between metastases and glioblastoma, offering a new strategy differentiation. METHODS Post- pre-contrast MRF (T1, T2 proton density maps) were acquired from 29 patients (14 (Met), 1...

BACKGROUND A number of lasers and light-based devices have been reported as promising treatment options for acne vulgaris. OBJECTIVE To evaluate the efficacy and safety of fractional microneedle radiofrequency (MRF) device treatment compared to CO(2) fractional laser system (FS) for the treatment of acne vulgaris. METHODS Twenty healthy subjects underwent full-face treatment for acne vulgar...