A silhouette-based algorithm for texture registration and stitching
Graphical Models
An Optimization Approach to Unsupervised Hierarchical Texture Segmentation
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
A Measure for Objective Evaluation of Image Segmentation Algorithms
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Genetic fusion: application to multi-components image segmentation
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04
Patch-Based texture edges and segmentation
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
IEEE Transactions on Information Technology in Biomedicine
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Segmentation poses one of the most challenging problems in medical imaging. Segmentation of Magnetic Resonance Imaging (MRI) images is an important part of brain imaging research as it can facilitates the neurological diseases diagnosis. However, there are few limitations in evaluating the segmentation accuracy due to difficulties in obtaining the ground truth. This research proposes an evaluation method for brain tissue abnormalities segmentation study. Controlled experimental data called mosaic images are used as the testing data. The data is designed which that prior knowledge of the size of the abnormalities is known. It is done by cutting various shapes and sizes of various abnormalities and pasting it onto normal brain tissues, where the tissues and the background are divided into three different intensities. The knowledge of the size of abnormalities by number of pixels are then used as the ground truth to compare with the various segmentation results. The validation of segmentation was done with fifty data of each category using methods of Particle Swarm Optimization (PSO), Adaptive Network-based Fuzzy Inference System (ANFIS) and Fuzzy c-Means (FCM), where the evaluation for each technique exhibits some variation of results. Therefore, the proposed evaluation method of ground truth formation called image mosaicing is found to be reasonable and acceptable to use as it produces potential solutions to the current difficulties in evaluating the brain tissue abnormalities segmentation outcome.