This paper presents the region based segmentation method for ultrasound images using local statistics. In this segmentation approach the homogeneous regions depends on the image granularity features, where the interested structures with dimensions comparable to the speckle size are to be extracted. This method uses a look up table comprising of the local statistics of every pixel, which are con...

This work presents a region-growing image segmentation approach based on superpixel decomposition. From an initial contour-constrained over-segmentation of the input image, the image segmentation is achieved by iteratively merging similar superpixels into regions. This approach raises two key issues: (1) how to compute the similarity between superpixels in order to perform accurate merging and ...

Starink, J.P.P. and I.T. Young, Localization of circular objects, Pattern Recognition Letters 14 ( 1993 ) 895-905. Localization of circular objects is easy when all objects are isolated, but becomes more complicated when two or more objects are touching or overlapping. In this paper we discuss a method to accurately localize the centroids and obtain estimates on the sizes. The method consists o...

This paper presents a parallel version for the Propagation Algorithm which belongs to the region growing family of algorithms. The main goal of our implementation is to decrease de Propagation Algorithm execution time in order to allow its use on image interpolation applications. Our solution is oriented to low cost high performance platforms such as clusters of workstations. Four different inp...

In this paper we present a region growing approach for color images using an online learning algorithm that aims for content-based image retrieval systems. Our learning algorithm follows a variation of Bayesian estimation procedure to characterize each region as it is grown. The resulting image growing procedure is simple to implement, robust to initial parameters and has a linear complexity. R...

The technique which is presented here is based on careful attention to image cleaning in order to achieve robust automatic filament detection and avoid confusion between filaments and image defects, due to particular shapes of filaments. The main part of the detection process is based on seed selection and region growing. The procedures developed have been tested on four months of full-disk ima...

In this paper, we propose a new reflectance separation model to separate the diffuse and specular reflection components. The model is based on a two-dimensional space called Ch-CV space, which is spanned by maximum chromaticity and the coefficient of variation (CV) of RGB color. The space exhibits a more direct correspondence to diffuse and specular reflection components than the RGB color spac...

In this work, a new approach to region growing problem is presented. The region growing procedure is modified in order to apply a fuzzy knowledge based system to evaluate the distance between a pixel and the region. This fuzzy colour distance is based on a set of rules that evaluate the three distance components (R, G, B) jointly. The results presented show a improvement with the traditional cr...

Decomposing sensory measurements into coherent parts is a fundamental prerequisite for scene understanding that is required for solving complex tasks, e.g., in the field of mobile manipulation. In this article, we describe methods for efficient segmentation of range images and organized point clouds. In order to achieve real-time performance in complex environments, we focus our approach on sim...

This study attempted to accurately segment tumors in mammograms. Although this task is considered to be a preprocessing step in a computer analysis program, it plays an important role for further analysis of breast lesions. The region of interest (ROI) was segmented using the pixel aggregation and region growing techniques combined with maximum likelihood analysis. A fast segmentation algorithm...