TY - GEN
T1 - An automatic lesion detection using dynamic image enhancement and constrained clustering
AU - Vianney Kinani, Jean M.
AU - Rosales-Silva, Alberto J.
AU - Gallegos-Funes, Francisco J.
AU - Arellanob, Alfonso
PY - 2014
Y1 - 2014
N2 - In this work, we present a fast and robust method for lesions detection, primarily, a non-linear image enhancement is performed on T1 weighted magnetic resonance (MR) images in order to facilitate an effective segmentation that enables the lesion detection. First a dynamic system that performs the intensity transformation through the Modified sigmoid function contrast stretching is established, then, the enhanced image is used to classify different brain structures including the lesion using constrained fuzzy clustering, and finally, the lesion contour is outlined through the level set evolution. Through experiments, validation of the algorithm was carried out using both clinical and synthetic brain lesion datasets and an 84%-93% overlap performance of the proposed algorithm was obtained with an emphasis on robustness with respect to different lesion types.
AB - In this work, we present a fast and robust method for lesions detection, primarily, a non-linear image enhancement is performed on T1 weighted magnetic resonance (MR) images in order to facilitate an effective segmentation that enables the lesion detection. First a dynamic system that performs the intensity transformation through the Modified sigmoid function contrast stretching is established, then, the enhanced image is used to classify different brain structures including the lesion using constrained fuzzy clustering, and finally, the lesion contour is outlined through the level set evolution. Through experiments, validation of the algorithm was carried out using both clinical and synthetic brain lesion datasets and an 84%-93% overlap performance of the proposed algorithm was obtained with an emphasis on robustness with respect to different lesion types.
KW - Fuzzy clustering
KW - Image enhancement
KW - Level set methods
KW - Magnetic resonance images
UR - http://www.scopus.com/inward/record.url?scp=84902449548&partnerID=8YFLogxK
U2 - 10.1117/12.2054467
DO - 10.1117/12.2054467
M3 - Contribución a la conferencia
AN - SCOPUS:84902449548
SN - 9781628410877
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Real-Time Image and Video Processing 2014
PB - SPIE
T2 - Real-Time Image and Video Processing 2014
Y2 - 16 April 2014 through 17 April 2014
ER -