Contrast-Limited Adaptive Histogram Equalization for Image Enhancement

Image enhancement refers to increasing contrast and suppressing noise in mammogram images to assist radiologists in detecting breast abnormalities. Various image enhancement methods exist, including adaptive contrast enhancement (AHE). AHE improves the local contrast and reveals more image details, making it a helpful technique for enhancing both natural and medical images [52 (link)]. However, it may also result in considerable noise. In this paper, we utilized the contrast-limited adaptive histogram equalization (CLAHE) technique, a form of AHE, to enhance image contrast [52 (link)]. A drawback of AHE is that it can over-enhance the images due to the integration process [49 (link)]. To mitigate this issue, CLAHE is used as it limits the local histogram by setting a clip level, thus controlling contrast enhancement. Figure 3 illustrates an image enhanced by the CLAHE algorithm.
Furthermore, CLAHE algorithm steps are given as follows [53 (link)]:

Split image into equal-sized contextual regions.

Apply histogram equalization to all contextual regions.

Limit the histogram to the level of the clip.

Reallocate the clipped values in the histogram.

Obtain enhanced pixel value through histogram integration.

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Rahman H., Naik Bukht T.F., Ahmad R., Almadhor A, & Javed A.R. (2023). Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network. Computational Intelligence and Neuroscience, 2023, 7717712.

Publication 2023

Abnormalities Adaptive Breast Mammogram Radiologists

Corresponding Organization : Lebanese American University

Other organizations : Al Jouf University

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Variable analysis

independent variables

Image enhancement methods
Contrast-limited adaptive histogram equalization (CLAHE) technique

dependent variables

Image contrast
Image details
Noise level

control variables

Not explicitly mentioned

controls

No positive or negative controls specified

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