Modified Histogram based contrast enhancement using Homomorphic Filtering for medical images

Abstract

In medical image processing, low contrast image analysis is a challenging problem. Low contrast digital images reduce the ability of observer in analyzing the image. Histogram based techniques are used to enhance contrast of all type of medical images. They are mainly used for all type of medical images such as for Mias-mammogram images, these methods are used to find exact locations of cancerous regions and for low-dose CT images, these methods are used to intensify tiny anatomies like vessels, lungs nodules, airways and pulmonary fissures. The most effective method used for contrast enhancement is Histogram Equalization (HE). Here we propose a new method named “Modified Histogram Based Contrast Enhancement using Homomorphic Filtering” (MH-FIL) for medical images. This method uses two step processing, in first step global contrast of image is enhanced using histogram modification followed by histogram equalization and then in second step homomorphic filtering is used for image sharpening, this filtering if followed by image normalization. To evaluate the effectiveness of our method we choose two widely used metrics Absolute Mean Brightness Error (AMBE) and Entropy. Based on results of these two metrics this algorithm is proved as a flexible and effective way for medical image enhancement and can be used as a pre-processing step for medical image understanding and analysis.