Iterative Thresholded Bi-Histogram Equalization for Medical Image Enhancement

TL;DR

This paper introduces an iterative, multi-threshold histogram equalization method with plateau limits for medical image enhancement, effectively improving contrast while preserving brightness and reducing artifacts.

Contribution

It proposes a novel multi-threshold, iterative histogram equalization technique with plateau limits that enhances contrast without amplifying noise or losing natural brightness.

Findings

Efficient noise handling in enhanced images.

Preserves natural brightness and appearance.

Reduces artifacts compared to traditional methods.

Abstract

Enhancement of human vision to get an insight to information content is of vital importance. The traditional histogram equalization methods have been suffering from amplified contrast with the addition of artifacts and a surprising unnatural visibility of the processed images. In order to overcome these drawbacks, this paper proposes interative, mean, and multi-threshold selection criterion with plateau limits, which consist of histogram segmentation, clipping and transformation modules. The histogram partition consists of multiple thresholding processes that divide the histogram into two parts, whereas the clipping process nicely enhances the contrast by having a check on the rate of enhancement that could be tuned. Histogram equalization to each segmented sub-histogram provides the output image with preserved brightness and enhanced contrast. Results of the present study showed that the proposed method efficiently handles the noise amplification. Further, it also preserves the brightness by retaining natural look of targeted image.