Contrast enhancement and brightness preserving of digital mammograms using fuzzy clipped contrast-limited adaptive histogram equalization algorithm

A novel fuzzy logic and histogram based algorithm called Fuzzy Clipped Contrast-Limited Adaptive Histogram Equalization (FC-CLAHE) algorithm is proposed for enhancing the local contrast of digital mammograms. A digital mammographic image uses a narrow range of gray levels. The contrast of a mammographic image distinguishes its diagnostic features such as masses and micro calcifications from one another with respect to the surrounding breast tissues. Thus, contrast enhancement and brightness preserving of digital mammograms is very important for early detection and further diagnosis of breast cancer. The limitation of existing contrast enhancement and brightness preserving techniques for enhancing digital mammograms is that they limit the amplification of contrast by clipping the histogram at a predefined clip-limit. This clip-limit is crisp and invariant to mammogram data. This causes all the pixels inside the window region of the mammogram to be equally affected. Hence these algorithms are not very suitable for real time diagnosis of breast cancer. In this paper, we propose a fuzzy logic and histogram based clipping algorithm called Fuzzy Clipped Contrast-Limited Adaptive Histogram Equalization (FC-CLAHE) algorithm, which automates the selection of the clip-limit that is relevant to the mammogram and enhances the local contrast of digital mammograms. The fuzzy inference system designed to automate the selection of clip-limit requires a limited number of control parameters. The fuzzy rules are developed to make the clip limit flexible and variant to mammogram data without human intervention. Experiments are conducted using the 322 digital mammograms extracted from MIAS database. The performance of the proposed technique is compared with various histogram equalization methods based on image quality measurement tools such as Contrast Improvement Index (CII), Discrete Entropy (DE), Absolute Mean Brightness Coefficient (AMBC) and Peak Signal-to-Noise Ratio (PSNR). Experimental results show that the proposed FC-CLAHE algorithm produces better results than several state-of-art algorithms. © 2016 Elsevier B.V. All rights reserved. 32 33 34 35 36 37 38 . Introduction Breast Cancer is the most frequently diagnosed cancer in women 1]. Mammography is proven to be one of the effective diagnostic ools for early detection of breast cancer. A digital mammoraphic image uses a narrow range of gray levels. The histogram tructure of digital mammograms is not well-defined. Both nonPlease cite this article in press as: S. Jenifer, et al., Contrast mograms using fuzzy clipped contrast-limited adaptive histog http://dx.doi.org/10.1016/j.asoc.2016.01.039 ancerous and cancerous breast masses appear as white regions n mammographic films. The fatty tissues appear as black regions. he other components of the breast, such as glands, connective ∗ Corresponding author. Tel.: +60 16 2669607. E-mail address: sheeba.sujit@monash.edu (S. Jenifer). ttp://dx.doi.org/10.1016/j.asoc.2016.01.039 568-4946/© 2016 Elsevier B.V. All rights reserved. 39 40 41 tissue, tumors and calcium deposits appear as shades of gray, more toward the brighter intensity on a digital mammogram [2]. These varied representations of gray levels make digital mammograms as difficult images to interpret. A large number of digital mammograms generated each year need accurate and fast interpretation of images. Non-cancerous lesions can be misinterpreted as a cancer (false-positive value), while cancers may be missed (false-negative value). As a result, radiologists fail to detect 10–30% of breast cancers [3]. Computer aided diagnostic (CAD) systems can help radiologists in accurate interpretation of digital mamenhancement and brightness preserving of digital mamram equalization algorithm, Appl. Soft Comput. J. (2016), mograms. The first step in the CAD system for the analysis of digital mammogram images involves pre-processing of the images for contrast enhancement while preserving the brightness of the images. 42 43 44 45