An Automated Method for Brain Tumor Segmentation Based on Level Set

 In this paper, an automatic method has been proposed for tumor segmentation. In this method, a new energy function by introducing the feature tumor is determined implemented by level set. Multi-scale Morphology Fuzzy filter is applied to the image and its output determines the tumor feature. The initial contour selection is important in active contour models. Therefor the initial contour has been selected automatically by using Hough transform and morphology function. Experimental results on MR images verify the desirable performance of the proposed model in comparison with other methods

Extraction of the best frames in coronary angiograms for diagnosis and analysis

X-ray coronary angiography has been a gold standard in the clinical diagnosis and interventional treatment of coronary arterial diseases for decades. In angiography, a sequence of images is obtained, a few of which are suitable for physician inspection. This paper proposes an automatic algorithm for the extraction of one or more frames from an angiogram sequence, which is most suitable for diagnosis and analysis by experts or processors. The algorithm consists of two stages: In the first stage, the background and illumination in the angiogram sequence are omitted. By analyzing the histogram of the sequence, a feature is attributed to each frame. These features, determining the visibility of the vessel tree, are clustered by a fuzzy c-means method. In the second stage, the cardiac phase for each frame is specified. Using the results of both stages, the best frames in an angiogram sequence are obtained. To evaluate the proposed method, it has been tested on angiogram sequences from several patients. The results demonstrate the accuracy of the method. The performance and speed of our method indicate its usefulness in clinical applications

Vesselness-guided Active Contour: A Coronary Vessel Extraction Method

Vessel extraction is a critical task in clinical practice. In this paper, we propose a new approach for vessel extraction using an active contour model by defining a novel vesselness-based term, based on accurate analysis of the vessel structure in the image. To achieve the novel term, a simple and fast directional filter bank is proposed, which does not employ down sampling and resampling used in earlier versions of directional filter banks. The proposed model not only preserves the performance of the existing models on images with intensity inhomogeneity, but also overcomes their inability both to segment low contrast vessels and to omit non-vessel structures. Experimental results for synthetic images and coronary X-ray angiograms show desirable performance of our model

Local feature fitting active contour for segmenting vessels in angiograms

An active contour model for vascular segmentation has been proposed, by defining a new, local, feature fitting, energy function. A vesselness filter is applied to the image in a directional Hessian‐based framework. The filter output, as a feature, expresses the degree of the correspondence of each pixel to the vessel structure. By using intensity information obtained from local regions, the proposed model is able to solve the problem of intensity inhomogeneity in images. In addition, by introducing this feature into the fitting process, the model exhibits greater accuracy when compared to existing models. Experimental results from synthetic images and coronary X‐ray angiograms verify the desirable performance of the proposed model