A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of intensity connectedness for image processing
Pattern Recognition Letters
Graphical Models and Image Processing
Connectivity in Digital Pictures
Journal of the ACM (JACM)
Scale-based fuzzy connected image segmentation: theory, algorithms, and validation
Computer Vision and Image Understanding - Special issue on analysis of volumetric image
Automated 3D Segmentation Using Deformable Models and Fuzzy Affinity
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
Automatic Quantification of MS Lesions in 3D MRI Brain Data Sets: Validation of INSECT
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Recognizing Deviations from Normalcy for Brain Tumor Segmentation
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
Image-based ventricular blood flow analysis
Image-based ventricular blood flow analysis
Automatic Segmentation of MR Images Based on Adaptive Anisotropic Filtering
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Level-set segmentation of brain tumors using a threshold-based speed function
Image and Vision Computing
Nonlinear image labeling for multivalued segmentation
IEEE Transactions on Image Processing
Image segmentation based on fuzzy connectedness using dynamic weights
IEEE Transactions on Image Processing
IEEE Transactions on Neural Networks
A modified fuzzy C-means algorithm for MR brain image segmentation
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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Uncontrollable and unlimited cell growth leads to tumor genesis in the brain. If brain tumors are not diagnosed early and cured properly, they could cause permanent brain damage or even death to patients. As in all methods of treatments, any information about tumor position and size is important for successful treatment; hence, finding an accurate and a fully automated method to give information to physicians is necessary. A fully automatic and accurate method for tumor region detection and segmentation in brain magnetic resonance (MR) images is suggested. The presented approach is an improved fuzzy connectedness (FC) algorithm based on a scale in which the seed point is selected automatically. This algorithm is independent of the tumor type in terms of its pixels intensity. Tumor segmentation evaluation results based on similarity criteria (similarity index (SI), overlap fraction (OF), and extra fraction (EF) are 92.89%, 91.75%, and 3.95%, respectively) indicate a higher performance of the proposed approach compared to the conventional methods, especially in MR images, in tumor regions with low contrast. Thus, the suggested method is useful for increasing the ability of automatic estimation of tumor size and position in brain tissues, which provides more accurate investigation of the required surgery, chemotherapy, and radiotherapy procedures.