An automated algorithm for lesion identification in dynamic contrast enhanced MRI. (1st January 2015)

• Record Type: Journal Article
• Authors: Malu, G.; Sherly, Elizabeth; Koshy, Sumod Mathew
• Is Part Of: International journal of computer applications technology. Volume 51:Number 1(2015)
• Page Start: 23
• Page End: 30
• Publication Date: 2015-01-01
• Subjects: dynamic MRI -- contrast enhancement -- MRI–CAE system -- intensity time kinetic graphs -- volume rendering -- GPU–based CUDA system -- breast cancer -- magnetic resonance imaging -- mammograms -- image segmentation -- lesions -- lesion identification -- region of interest -- ROI automation; Technology -- Data processing -- Periodicals; 620.00285
• DOI: 10.1504/IJCAT.2015.068396
• Languages: English
• ISSNs: 0952-8091

Abstract

Breast cancer is one of the most common cancers found in women around the world. Dynamic contrast enhanced magnetic resonance imaging (DCE–MRI) is nowadays used in many suspected cases that may not be visible in a mammogram. This work concentrates on algorithmic development for segmentation of lesions, automating region of interest (ROI), and to delineate the malignancy on DCE–MRI. This has been tested against the intensity time kinetic curve method. The curve pattern and its distribution shows the likelihood of malignancy, which can be automatically coloured based on the assessment. The system helps to compute volume rendering by forming a 3D structure of the lesion area. The proposed system enables doctors to improve the diagnostic accuracy. The development is carried out using graphic processing unit (GPU) based high end parallel computing with NVIDIA CUDA software, which enables to process more than 5000 images of a single patient with better speed and managed memory.