Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color Segmentation Applied to Study of the Angiogenesis. Part I
Journal of Intelligent and Robotic Systems
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vascular Shape Segmentation and Structure Extraction Using a Shape-Based Region-Growing Model
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Blood Vessel Segmentation via Neural Network in Histological Images
Journal of Intelligent and Robotic Systems
Nonparametric robust methods for computer vision
Nonparametric robust methods for computer vision
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color image segmentation for Bladder Cancer Diagnosis
Mathematical and Computer Modelling: An International Journal
The estimation of the gradient of a density function, with applications in pattern recognition
IEEE Transactions on Information Theory
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Image segmentation plays an important role in image analysis. According to several authors, segmentation terminates when the observer's goal is satisfied. For this reason, a unique method that can be applied to all possible cases does not yet exist. In this paper, we have carried out a comparison between two current segmentation techniques, namely the mean shift method, for which we propose a new algorithm, and the so-called spectral method. In this investigation the important information to be extracted from an image is the number of blood vessels (BV) present in the image. The results obtained by both strategies were compared with the results provided by manual segmentation. We have found that using the mean shift segmentation an error less than 20% for false positives (FP) and 0% for false negatives (FN) was observed, while for the spectral method more than 45% for FP and 0% for FN were obtained. We discuss the advantages and disadvantages of both methods.