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...

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 hybrid clustering algorithm and feed-forward neural network classifier for land-cover mapping of trees, shade, building and road. It starts with the single step preprocessing procedure to make the image suitable for segmentation. The pre-processed image is segmented using the hybrid geneticArtificial Bee Colony(ABC) algorithm that is developed by hybridizing the ABC and ge...

In last few decades, the application of biological methods and systems to the study and design of engineering systems and modern technologies have fascinated many researchers. Numerous mathematical and meta-heuristic algorithms for solving optimization problems have been developed and widely used in both theoretical study and practical applications. To realize the application of such meta-heuri...

normal 0 false false false en-us x-none fa background:  regarding the importance of right diagnosis in medical applications, various methods have been exploited for processing medical images solar. the method of segmentation is used to analyze anal to miscall structures in medical imaging. objective:  this study describes a new method for brain magnetic resonance image (mri) segmentation via a ...

Image segmentation is one of the most common steps in digital image processing. The area many image segmentation algorithms (e.g., thresholding, edge detection, and region growing) employed for classifying a digital image into different segments. In this connection, finding a suitable algorithm for medical image segmentation is a challenging task due to mainly the noise, low contrast, and steep...

Effective image segmentation remains a challenging process as it constitutes a critical step to higher level image processing applications such as pattern recognition. In this paper,we present bio-inspired formulationto perform unsupervised image segmentation. Specifically,we used the Quantum PSO, the hybrid Gravitational PSO algorithm, a cooperative gravitational approach and the bees approach...

This paper presents a new image segmentation approach based on distributed agents. In this approach, the segmentation is performed trough a simulation of the hybrid architecture of P2P networks by introducing the concept of “super agent”. Each super agent corresponds to an area of the image. The segmentation process within that zone is done by ordinary agents supervised by the super agent. Segm...

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...

Image segmentation is one of the most common steps in digital image processing. The area many image segmentation algorithms (e.g., thresholding, edge detection, and region growing) employed for classifying a digital image into different segments. In this connection, finding a suitable algorithm for medical image segmentation is a challenging task due to mainly the noise, low contrast, and steep...