sar (synthetic aperture radar) image enhancement and segmentation is purpose of this thesis. sar image segmentation is a primary step before steps such as classification and target recognition. the main obstacle in sar image segmentation is inherent speckle noise. speckle noise is a multiplicative and highly destructive noise which results to intensity inhomogeneity. hence common segmentation m...

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 ...

Information about the retinal blood vessel network is important for diagnosis, treatment, screening, evaluation and the clinical study of many diseases such as diabetes, hypertension and arteriosclerosis. Automated segmentation and identification of retinal image structures had become one of the major research subjects in the fundus imaging and diagnostic ophthalmology. Automatic segmentation o...

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...

this paper presents a new procedure for automatic extraction of the blood vessels and optic disk (od) in fundus fluorescein angiogram (ffa). in order to extract blood vessel centerlines, the algorithm of vessel extraction starts with the analysis of directional images resulting from sub‑bands of fast discrete curvelet transform (fdct) in the similar directions and different scales. for this pur...

The extraction of blood vessels from retinal images is an important and challenging task in medical analysis and diagnosis. This paper presents a novel hybrid automatic approach for the extraction of retinal image vessels. The method consists in the application of mathematical morphology and a fuzzy clustering algorithm followed by a purification procedure. In mathematical morphology, the retin...

In this paper presents , segmentation of blood vessels in retinal images based on neural network (NN) scheme of gray-level and moment invariants-based features. In the past, rural based methods are used segment the blood vessels. It has more complexity and less accuracy of blood vessels detection in retinal images. This paper proves better performance in terms of blood vessel detection in stare...

this paper presents a new three-dimensional curvelet transform based dictionary learning for automatic segmentation of intraretinalcysts, most relevant prognostic biomarker in neovascular age-related macular degeneration, from 3d spectral-domain optical coherencetomography (sd-oct) images. in particular, we focus on the spectralis sd-oct (heidelberg engineering, heidelberg, germany) system, and...