A New Breast Border Extraction and Contrast Enhancement Technique with Digital Mammogram Images for Improved Detection of Breast Cancer

Document Type: Research Articles

Authors

1 Department of Computer Science, Gauhati University, Guwahati, India.

2 Central Computational and Numerical Sciences Division (CCNS), Institute of Advanced Study in Science and Technology (IASST), (An Autonomous R&D Institute under Department of Science and Technology), Guwahati, India.

Abstract

Purpose: Breast cancer can be cured if diagnosed early, with digital mammography which is one of the most
effective imaging modalities for early detection. However mammogram images often come with low contrast, high
background noises and artifacts, making diagnosis difficult. The purpose of this research is to preprocess mammogram
images to improve results with a computer aided diagnosis system. The focus is on three preprocessing methods: a breast
border segmentation method; a contrast enhancement method; and a pectoral muscle removal method. Methods: The
proposed breast border extraction method employs a threshold based segmentation technique along with a combination
of morphological operations. The contrast enhancement method presented here is divided into two phages. In phase
I, a bi-level histogram modification technique is applied to enhance the image globally and in phase II a non-linear
filter based on local mean and local standard deviation for each pixel is applied to the histogram modified image. The
pectoral muscle removal method discussed here is implemented by applying a region growing algorithm. Results:
The proposed techniques are tested with the Mini MIAS dataset. The breast border extraction method is applied to
322 images and achieved 98.7% segmentation accuracy. The contrast enhancement method is evaluated based on
quantitative measures like measure of enhancement, absolute mean brightness error, combined enhancement measure
and discrete entropy. The proposed contrast enhancement method when applied to 14 images with different types of
masses, the quantitative measures showed an optimum level of contrast enhancement compared to other enhancement
methods with preservation of local detail. Removal of the pectoral muscle from MLO mammogram images reduced
the search region while identifying abnormalities like masses and calcification. Conclusions: The preprocessing steps
proposed here show promising results in terms of both qualitative and quantitative analysis.

Keywords

Main Subjects