An automatic lesion detection using dynamic image enhancement and constrained clustering

Jean M. Vianney Kinani; Alberto J. Rosales-Silva; Francisco J. Gallegos-Funes; Alfonso Arellano

doi:10.1117/12.2054467

Translator Disclaimer

15 May 2014 An automatic lesion detection using dynamic image enhancement and constrained clustering

Jean M. Vianney Kinani, Alberto J. Rosales-Silva, Francisco J. Gallegos-Funes, Alfonso Arellano

Author Affiliations +

Proceedings Volume 9139, Real-Time Image and Video Processing 2014; 91390S (2014) https://doi.org/10.1117/12.2054467
Event: SPIE Photonics Europe, 2014, Brussels, Belgium

Abstract

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.

Citation Download Citation

Jean M. Vianney Kinani, Alberto J. Rosales-Silva, Francisco J. Gallegos-Funes, and Alfonso Arellano "An automatic lesion detection using dynamic image enhancement and constrained clustering", Proc. SPIE 9139, Real-Time Image and Video Processing 2014, 91390S (15 May 2014); https://doi.org/10.1117/12.2054467

ACCESS THE FULL ARTICLE