Image processing of digital mammograms for breast cancer detection and classification
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Date
2018-06
Authors
Ahmad Nabil Mohd Nizom
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Abstract
Due to an increase in number of breast cancer screening worldwide, development
of accurate CAD is needed for tumour detection in mammograms. This study aims to
develop an image processing algorithm that can produce lesser errors than human
operators. The algorithms to be developed will consist of pre-processing, enhancement
and image segmentation. This study also aims to develop an algorithm that uses
conversion of greyscale image into RGB as an approach to image processing for
greyscale image. For the image processing, the pre-processing is done by removal of
artefacts and pectorals muscle using image segmentation and selection by region area
and region ID respectively. Then, the process begins with the image enhancement using
CLAHE to improve the details and contrast in the image. After that, the greyscale image
undergo conversion into RGB by changing the colourmap. The image is segmented based
on colour then translated into a circle which centroid is same with the cluster and the
number of pixel is same to the tumour detected for comparison with the ground truth
data. The accuracy of the algorithm developed in detecting tumour is 94.38% showing
that it is relevant for use by radiologists. The algorithm may be developed for application
in other field that uses greyscale image as well.