Computer tomography imaging Technique plays an important role in medical imaging research for diagnosis of liver diseases. Automatic detection of liver is the most essential parts in computer-aided diagnosis for liver CT as well as computer-aided surgery. CAD is used by radiologists as a second opinion in detecting tumors, accessing the extent of diseases and making diagnostic decision. Automat...

BACKGROUND Intensity inhomogeneity occurs in many medical images, especially in vessel images. Overcoming the difficulty due to image inhomogeneity is crucial for the segmentation of vessel image. METHODS This paper proposes a localized hybrid level-set method for the segmentation of 3D vessel image. The proposed method integrates both local region information and boundary information for ves...

This paper presents a hybrid segmentation algorithm, which provides a synthetic image description in terms of regions. This method has been used to segment images of outdoor scenes. We have applied our segmentation algorithm to color images and images encoding 3D information. 5 different color spaces were tested. The segmentation results obtained with each color space are compared.

Segmentation methods for images often have cost functions which evaluate the (dis)similarity between pixels or segments. Thresholds on cost values are then used to decide whether or not to grow, join or split segments. The results for a given image critically depend on the selection of the threshold values. In remote sensing, a too low threshold will split up regions of constant ground cover an...

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...

Image segmentation is a classical problem in computer vision and is of paramount importance to medical imaging. The segmentation is complicated by lack of clarity, the overlap of intensities and many other factors. We present a hybrid algorithm for obtaining segmentation of images that are subject to noise and multiplicative intensity nonuniformity. The algorithm is formulated by the combinatio...

This paper presents a new model to perform a supervised image segmentation task. The proposed model is called segmentation and classification with receptive fields (SCRF) which is based on the concept of receptive fields that analyzes pieces of an image considering not only a pixel or group of them, but also the relationship between them and their neighbors. In order to work with the SCRF model...

In this paper a hybrid fractal and Discrete Cosine Transform (DCT) coder is developed. Drawing on the ability of DCT to remove inter-pixel redundancies and on the ability of fractal transforms to capitalize on long-range correlations in the image, the hybrid coder performs an optimal, in the Rate-Distortion sense, bit allocation among coding parameters. An orthogonal basis framework is used wit...

In this paper range image segmentation is cast in the framework of Bayes inference and Markov random field modeling. To facilitate the inference from distance measurement to labeling set, we introduce the set of surface function parameters as another estimation and construct a novel model accordingly. Subsequent study shows that range image segmentation can be formulated as a combinatorial opti...