Development of techniques for the detection of tumours in breast magnetic resonance imaging
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Date
2015-09-01
Authors
Ali Qusay Zahroon Al-Faris
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Abstract
Breast cancer is the leading cause of death amongst cancer patients afflicting women and the second most common cancer around the world. Magnetic Resonance Imaging (MRI) is one of the most effective radiology tools to screen breast cancer. However, image processing techniques are needed to help radiologists in interpreting the images and segmenting tumours regions to reduce the number of false-positive. In this study, a segmentation approach with automatic features is developed for breast MRI tumours. The methodology starts with data acquisition followed by pre-processing. This is then followed with breast skin-line exclusion using integrated method of Level Set Active Contour and Morphological Thinning. Next, regions of interests are detected using proposed Mean Maximum Raw Thresholding method (MMRT). In the tumour segmentation phase, two modified Seeded Region Growing (SRG) methods are proposed; i.e. Breast MRI Tumour using Modified Automatic SRG (BMRI-MASRG) and Breast MRI Tumour using SRG based on Particle Swarm Optimization Image Clustering (BMRI-SRGPSOC). The RIDER breast MRI dataset was used for evaluation and the results are compared with the ground truth of the dataset. From analysing the evaluation results, it can be noticed that the proposed approaches scored high results using various measures comparing to previous methods. The results of skin-line exclusion scored high average performance in both stages; border segmentation stage (sensitivity = 0.81 and specificity = 0.94) and removal stage (sensitivity = 0.86 and specificity = 0.97). The quality evaluation of MMRT showed improved results with average of PSNR = 69.97 and MSE = 0.01. In the tumour segmentation phase, the sensitivity results of the two proposed methods; BMRI-MASRG and BMRI-SRGPSOC showed more accurate segmentation with averages of 0.82 and 0.84 respectively. Similarly, the specificity results also scored better performance compared to previous methods. The averages of BMRI-MASRG and BMRI-SRGPSOC are 0.90 and 0.91 respectively.