Image Fusion, a technique which combines complimentary information from different images of the same scene so that the fused image is more suitable for segmentation, feature extraction, object recognition and Human Visual System. In this paper, a simple yet efficient algorithm is presented based on contrast using wavelet packet decomposition. First, all the source images are decomposed into low...

Label fusion is a key step in multi-atlas based segmentation, which combines labels from multiple atlases to make the final decision. However, most of the current label fusion methods consider each voxel equally and independently during label fusion. In our point of view, however, different voxels act different roles in the way that some voxels might have much higher confidence in label determi...

The paper presents the evaluation of the segmentation of MR images using the multispectral fusion approach in the possibility theory context. the process of fusion consists of three parts : (1) information extraction, (2) information aggregation, and (3) decision step. Information provided by T2-weighted and PDweighted images is extracted and modeled separately in each one using fuzzy logic, fu...

Due to their abnormal appearance, Multiple Sclerosis lesions can influence the results of various image analysis techniques such as segmentation and registration. As the multi-modal characteristic intensity of the Multiple Sclerosis lesions is different that of non-pathological tissues, a local multi-modal intensity similarity can be used to classify and segment lesions. In this work, lesions a...

In some algorithms, segmentation of the word image considers the first step of the recognition processes; the main aim of this paper is proposed new fusion equations for improving the segmentation of word image. The technique that has used is divided into two phases; at the beginning, applying the Arabic Heuristic Segmenter (AHS), AHS uses the shape features of the word image, it employs three ...

Introduction: Segmentation of brain images especially from magnetic resonance imaging (MRI) is an essential requirement in medical imaging since the tissues, edges, and boundaries between them are ambiguous and difficult to detect, due to the proximity of the brightness levels of the images. Material and Methods: In this paper, the graph-base...

We propose a simple but generally applicable approach to improving the accuracy of automatic image segmentation algorithms relative to manual segmentations. The approach is based on the hypothesis that a large fraction of the errors produced by automatic segmentation are systematic, i.e., occur consistently from subject to subject, and serves as a wrapper method around a given host segmentation...

In this paper, we advance the state of the art in variational image segmentation through the fusion of bottom-up segmentation and top-down classification of object behavior over an image sequence. Such an approach is beneficial for both tasks and is carried out through a joint optimization, which enables the two tasks to cooperate, such that knowledge relevant to each can aid in the resolution ...

Quantitative magnetic resonance analysis often requires accurate, robust, and reliable automatic extraction of anatomical structures. Recently, template-warping methods incorporating a label fusion strategy have demonstrated high accuracy in segmenting cerebral structures. In this study, we propose a novel patch-based method using expert manual segmentations as priors to achieve this task. Insp...

In this paper, we advance the state of the art in variational image segmentation through the fusion of bottom-up segmentation and top-down classification of object behavior over an image sequence. Such an approach is beneficial for both tasks and is carried out through a joint optimization, which enables the two tasks to cooperate, such that knowledge relevant to each can aid in the resolution ...