<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Interpreting Dynamic Contrast-Enhanced (DCE) MR images for signs of breast cancer is time consuming and complex, since the amount of data that needs to be examined by a radiologist in breast DCE-MRI to locate suspicious lesions is huge. Misclassifications can arise from either overlooking a suspicious region or from incorrectly interpreting a suspicious region. The segmentation of breast DCE-MRI for suspicious lesions in detection is thus attractive, because it drastically decreases the amount of data that needs to be examined. The new segmentation method for detection of suspicious lesions in DCE-MRI of the breast tissues is based on artificial fishes swarm clustering algorithm is presented in this paper. Artificial fish swarm optimization algorithm is a swarm intelligence algorithm, which performs a search based on population and neighborhood search combined with random search. The major criteria for segmentation are based on the image voxel values and the parameters of an empirical parametric model of segmentation algorithms. The experimental results show considerable impact on the performance of the segmentation algorithm, which can assist the physician with the task of locating suspicious regions at minimal time.</p></abstract>

Interpreting Dynamic Contrast-Enhanced (DCE) MR images for signs of breast cancer is time consuming and complex, since the amount of data that needs to be examined by a radiologist in breast DCE-MRI to locate suspicious lesions is huge. Misclassifications can arise from either overlooking a suspicious region or from incorrectly interpreting a suspicious region. The segmentation of breast DCE-MRI for suspicious lesions in detection is thus attractive, because it drastically decreases the amount of data that needs to be examined. The new segmentation method for detection of suspicious lesions in DCE-MRI of the breast tissues is based on artificial fishes swarm clustering algorithm is presented in this paper. Artificial fish swarm optimization algorithm is a swarm intelligence algorithm, which performs a search based on population and neighborhood search combined with random search. The major criteria for segmentation are based on the image voxel values and the parameters of an empirical parametric model of segmentation algorithms. The experimental results show considerable impact on the performance of the segmentation algorithm, which can assist the physician with the task of locating suspicious regions at minimal time.

Automatic segmentation of lesion from breast DCE-MR image using artificial fish swarm optimization algorithm

Polish Journal of Medical Physics and Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Interpreting Dynamic Contrast-Enhanced (DCE) MR images for signs of breast cancer is time consuming and complex, since the amount of data that needs to be...

Automatic segmentation of lesion from breast DCE-MR image using artificial fish swarm optimization algorithm

Article Category: Scientific Paper

Publicado en línea: 28 jun 2017

Páginas: 29 - 36

Recibido: 07 abr 2017

Aceptado: 19 may 2017

DOI: https://doi.org/10.1515/pjmpe-2017-0006

Palabras clave
breast DCE-MRI, segmentation, artificial fishes swarm optimization algorithm

© Polish Society of Medical Physics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Automatic segmentation of lesion from breast DCE-MR image using artificial fish swarm optimization algorithm

Article Category: Scientific Paper

Publicado en línea: 28 jun 2017

Páginas: 29 - 36

Recibido: 07 abr 2017

Aceptado: 19 may 2017

DOI: https://doi.org/10.1515/pjmpe-2017-0006

Palabras clavebreast DCE-MRI, segmentation, artificial fishes swarm optimization algorithm

© Polish Society of Medical Physics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Palabras clave
breast DCE-MRI, segmentation, artificial fishes swarm optimization algorithm