The process of retinal vessel segmentation is important for detection of various eye conditions including the effect of diabetes on eyes, or diabetic retinopathy. As we know, the retinal microvascu...

VEA: Vessel Extraction Algorithm by Active Contour Model and a Novel Wavelet Analyzer for Diabetic Retinopathy Detection

Skin cancer is one of the most common types of cancer in the white populations and the incidence of skin cancer has reached epidemic proportions. This paper proposes a new approach for automatic segmentation and classification for skin lesion. The segmentation is based on a pre-processing using the color structure texture image decomposition. Geometrical component is used in the lesion segmentation and the texture component is used to extract the lesion texture features. Feature classification is performed using the Support Vector Machine (SVM) classifier. The efficiency and the performance of the proposed approach are evaluated in comparison with recent and robust dermoscopic approaches from literature.

An improved approach for skin lesion analysis based on multiscale decomposition

In this paper, we present two models for supervised scalar image segmentation based on the active contours and information theory. First we propose to carry out a region competition by optimizing an energy designed to be minimal when the entropy of the inside and outside regions of the evolving active contour are close to those of a reference image. The probability density functions (pdfs) used by this model can be computed in a preprocessing step on a reference image. This substantially reduces the computational complexity making this model fast. On the other hand, this implies that the reference image and the image to segment have similar pdfs. When the pdfs are too different or both images are not from the same modality we propose a second segmentation model computationally more expensive but more robust to intensity differences. This second model is based on an information measure extensively used for image registration, the joint entropy. The performance of both models is demonstrated on a variety of 2D synthetic data and medical images. They are also compared in term of segmentation accuracy and computational cost with an entropy-based unsupervised segmentation model recently proposed.

Active contours and information theory for supervised segmentation on scalar images

This paper is a research on unsupervised texture segmentation technique using Gabor filters and multi-PCNN. The partitioning method based on Gabor filters gets the good segmentation result, but causes the huge data. PCNN is a parallel way and can be easily realized with hardware, especially VLSI, which makes PCNN process image fast. We present an algorithm which uses Gabor filters to extract image texture character which has been inputted into PCNNs to segment the image. This method can get good segmentation result and improve the algorithm's processing speed.

Unsupervised Texture Image Segmentation Based on Gabor Wavelet and Multi-PCNN

In this paper we explore the use of the cluster analysis in segmentation problems, that is, identifying image points with an indication of the region or class they belong to. The proposed algorithm uses the well known agglomerative hierarchical cluster analysis algorithm in order to form clusters of pixels, but modified so as to cope with the high dimensionality of the problem. The results of different stages of the algorithm are saved, thus retaining a collection of segmented images ordered by degree of segmentation. This allows the user to view the whole collection and choose the one that suits him best for his particular application.

/pdf/dynamic-image-segmentation-method-using-hierarchical-4i2tu5nkhm.pdf

Dynamic Image Segmentation Method Using Hierarchical Clustering

A new method for texture image segmentation is proposed which has three advantageous. Firstly, it has excellent texture discrimination and low complexity, for uniform LBP/C (local binary patterns and pattern contrasts) operator which reduces the number of texture pattern to 23% is used to extract texture features from a texture image. Secondly, active contours without edges are improved so that the LBP/C histogram can be used to evolve the initial curve during the segmentation process. A multiple object texture image can be segmented, because the proposed model is expanded to multiphase segmentation by the hierarchical method which can avoid the problem due to the choice of initial conditions. Finally, the speed of image segmentation is higher, for AOS scheme is applied for the numerical solution of the proposed method. Experiments on both synthetic and remote sensing images show that the proposed method is more accurate and faster.

Radar image segmentation using active contour

Texture is often divided into lots of isolate areas when a texture image is segmented. This paper develops a new method for texture image segmentation. It uses Gabor wavelet to extract texture features, and then Chan and Vese model is employed to segment a texture image. Compared to other texture segmentation methods using Chan and Vese models, the proposed method has two advantages. First, by combining the gray levels of pixels and texture information of an image, this method can be used for segmentation of a texture image or a none-texture image. Second, the hierarchical method proposed in this paper can avoid the problem due to the choice of initial conditions. The segmentation tests for remote sensing and natural texture images prove the proposed segmentation method is accurate and efficient.

Texture Image Segmentation Based on Gabor Wavelet Using Active Contours Without Edges

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Radar image segmentation using active contour

Texture Image Segmentation Based on Gabor Wavelet Using Active Contours Without Edges