In this paper, we present an enhancement of the fuzzy connectedness-based image segmentation method based on dynamic computation of adaptive weights for the homogeneity and the directional gradient energy functions. Adaptive weights enhance the performance and robustness of the conventional fuzzy connectedness-based segmentation while decreasing the degree of user interaction. The accuracy of t...

We propose an image segmentation algorithm that is based on spatially adaptive color and texture features. The features are first developed independently, and then combined to obtain an overall segmentation. Texture feature estimation requires a finite neighborhood which limits the spatial resolution of texture segmentation, while color segmentation provides accurate and precise edge localizati...

This paper presents the subsequent improvements of our adaptive regularization algorithm. The current version of the algorithm contains an additional stabilizer. This stabilizer includes the coefficient, which utilizes prior information about segmentation of an image to be processed. We demonstrate that our adaptive regularization algorithm can exploit only the observed image segmentation for b...

Automatic image over-segmentation into superpixels has attracted increasing attention from researchers to apply it as a pre-processing step for several computer vision applications. In 4D Light Field (LF) imaging, aims at achieving not only superpixel compactness and accuracy but also cross-view consistency. Due the high dimensionality of LF images, depth information can be estimated exploited ...

In this paper, we describe the application of an adaptive fuzzy clustering algorithm to the segmentation of colour biomedical images. In comparison to traditional colour image segmentation algorithms, the advantage of this algorithm is intelligent location of a range of chosen colours (ROC) based on a manually selected reference colour. The colours within the ROC have similar colour properties ...

In this paper, we present an original image segmentation model based on a preliminary spatially adaptive non-linear data dimensionality reduction step integrating contour and texture cues. This new dimensionality reduction model aims at converting an input texture image into a noisy color image in order to greatly simplify its subsequent segmentation. In this latter de-texturing model, the (spa...

BACKGROUND Segmentation is the most crucial part in the computer-aided bone age assessment. A well-known type of segmentation performed in the system is adaptive segmentation. While providing better result than global thresholding method, the adaptive segmentation produces a lot of unwanted noise that could affect the latter process of epiphysis extraction. METHODS A proposed method with anis...

Breast skin–air interface and pectoral muscle segmentation are usually first steps in all CAD applications on scanned as well as digital mammograms. Breast skin–air interface segmentation is much more difficult task when performed on scanned mammograms than on digital mammograms. In case of pectoral muscle segmentation, segmentation difficulty of analog and digital mammograms is usually similar...