Application of Coupled Neural Oscillators for Image Texture Segmentation and Modeling of Biological Rhythms

نویسندگان

  • PAWEŁ STRUMIŁŁO
  • MICHAŁ STRZELECKI
چکیده

The role of relaxation oscillator models in application fields such as modeling dynamic systems and image analysis is discussed. A short review of the Van der Pol, Wilson-Cowan and Terman-Wang relaxation oscillators is given. The key property of such nonlinear oscillators, i.e., the oscillator phase shift (called the Phase Response Curve) as a result of external pulse stimuli is indicated as a fundamental mechanism to achieve and sustain synchrony in networks of coupled oscillators. It is noted that networks of such oscillators resemble a variety of naturally occurring phenomena (e.g., in electrophysiology) and dynamics arising in engineering systems. Two types of oscillator networks exhibiting synchronous behaviors are discussed. The network of oscillators connected in series for modeling a cardiac conduction system is used to explain causes of important cardiac abnormal rhythms. Finally, it is shown that a 2D network of coupled oscillators is an effective tool for segmenting image textures in biomedical images.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Modeling of Texture and Color Froth Characteristics for Evaluation of Flotation Performance in Sarcheshmeh Copper Pilot Plant, Using Image Analysis and Neural Networks

Texture and color appearance of froth is a discreet qualitative tool for evaluating the performance of flotation process. The structure of a froth developed on the flotation cell has a significant effect on the grade and recovery of copper concentrate. In this work, image analysis and neural networks have been implemented to model and control the performance of such a system. The result reveals...

متن کامل

Unsupervised Texture Image Segmentation Using MRFEM Framework

Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientatio...

متن کامل

Unsupervised Texture Image Segmentation Using MRFEM Framework

Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientatio...

متن کامل

Performance Analysis of Segmentation of Hyperspectral Images Based on Color Image Segmentation

Image segmentation is a fundamental approach in the field of image processing and based on user’s application .This paper propose an original and simple segmentation strategy based on the EM approach that resolves many informatics problems about hyperspectral images which are observed by airborne sensors. In a first step, to simplify the input color textured image into a color image without tex...

متن کامل

An Automated MR Image Segmentation System Using Multi-layer Perceptron Neural Network

Background: Brain tissue segmentation for delineation of 3D anatomical structures from magnetic resonance (MR) images can be used for neuro-degenerative disorders, characterizing morphological differences between subjects based on volumetric analysis of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF), but only if the obtained segmentation results are correct. Due to image arti...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2006