in this study, an image backlight compensation method using adaptive luminance modification is proposed for efficiently obtaining clear images.the proposed method combines the fuzzy c-means clustering method, a recurrent functional neural fuzzy network (rfnfn), and a modified differential evolution.the proposed rfnfn is based on the two backlight factors that can accurately detect the compensat...

this paper introduces a novel methodology for the segmentation of brain ms lesions in mri volumes using a new clustering algorithm named scpfcm.  scpfcm uses membership, typicality and spatial information to cluster each voxel. the proposed method relies on an initial segmentation of ms lesions in t1-w and t2-w images by applying scpfcm algorithm, and the t1 image is then used as a mask and is ...

potato image segmentation is an important part of image-based potato defect detection. this paper presents a robust potato color image segmentation through a combination of a fuzzy rule based system, an image thresholding based on genetic algorithm (ga) optimization and morphological operators. the proposed potato color image segmentation is robust against variation of background, distance and ...

image segmentation is an important task in image processing and computer vision which attract many researchers attention. there are a couple of information sets pixels in an image: statistical and structural information which refer to the feature value of pixel data and local correlation of pixel data, respectively. markov random field (mrf) is a tool for modeling statistical and structural inf...

introduction the diagnosis and separation of cancerous tumors in medical images require accuracy, experience, and time, and it has always posed itself as a major challenge to the radiologists and physicians. materials and methods we received 290 medical images composed of 120 mammographic images, ljpeg format, scanned in gray-scale with 50 microns size, 110 mri images including of t1-wighted, t...

In the image databases, there is often a need to organize the images automatically by their content. For the content-based organization of the images, clustering operations can be applied. We present a new, efficient method for the clustering of large image databases. The method is based on hierarchical clustering of the image database using grid. In this paper, the grid-based clustering method...

Content-based image retrieval (CBIR) has been an active research topic in the last decade. In this paper we proposed an image retrieval method using global and local features. Firstly, for local features extraction, SURF algorithm produces a set of interest points for each image and a set of 64-dimensional descriptors for each interest points and then to use Bag of Visual Words model, a cluster...

Image segmentation is a fundamental step in many of image processing applications. In most cases the image’s pixels are clustered only based on the pixels’ intensity or color information and neither spatial nor neighborhood information of pixels is used in the clustering process. Considering the importance of including spatial information of pixels which improves the quality of image segmentati...

Identifying specific CT-image regions is an important process in medical diagnosis. Clustering is a simple and useful means for automatic image segmentation. However, clustering results vary with the features of image pixels and the settings of parameters of the clustering methods. This study compares the results of CT image segmentation using FCMbased clustering algorithms running with intensi...