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Conference Paper
Automated Single and Multi-Breast Tumor Segmentation Using Improved Watershed Technique in 2D MRI Images
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- Authors
- Mohammad Taheri, George Hamer, Seong Ho Son, Sung Y. Shin
- Issue Date
- 2016-10
- Citation
- Research in Adaptive and Convergent Systems (RACS) 2016, pp.61-66
- Language
- English
- Type
- Conference Paper
- Abstract
- Image segmentation is a challenging task in image processing. The purpose is to divide up pixels into different partitions in which members of each partition have similar characteristics and a unique label. Image segmentation and extracting the characteristics of a tumor are powerful tools that can be used in medical science. In the case of breast cancer medical treatment, segmentation methods can be used to extract and segment the tumor for better diagnoses and earlier detection of breast tumors. However, extracting and segmentation of the tumors or region of interest (RIO) can be challenging. This is due to the existence of noise in the images, along with the complicated structures of the image. Manual classification of the images is time-consuming, and needs to be done only by medical experts. Hence, using an automated medical image segmentation tool will be useful and necessary. In this paper, a method is proposed based on the wellknown watershed technique and automated thresholding for single and multi-tumor segmentation in medical images. The procedure consists of pre-processing, removing noise, elimination of unwanted objects, generating and segmentation. Segmentation involves automatic thresholding, gradient magnitude, finding regional minimums, and recognition. Experimental results show that this method performs well in segmentation with efficient execution time and can be used for medical diagnostics of breast cancer.
- KSP Keywords
- Automatic thresholding, Breast Cancer, Breast tumor segmentation, Divide up, Gradient Magnitude, Image processing(IP), MRI images, Medical Science, Pre-processing, Region of interest(ROI), Removing noise