In medical image processing, low contrast image analysis is a challenging problem. In digital image processing contrast enhancement techniques are an important techniques for both human and computer vision. 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. Histogram Equalization (HE) is one of the simplest and effective technique to perform contrast enhancement. In histogram equalization we reduce the number of gray levels by combining two or more less frequent neighboring gray levels in one gray level; also we stretch high frequent intensities over high range of gray levels to achieve comparatively more flat histogram. This flattering causes the overall enhancement of contrast of the input image. In histogram equalization we do not have any mechanism to control the enhancement level, due to this sometime output image have over enhanced regions. 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.

Modified Histogram Based Contrast Enhancement using Homomorphic Filtering for Medical Images

Functional Mapping of Organ Systems and Other Computer Topics

Generally the major problem is low contrast image analysis in medical Field. Low contrast digital images reduce the ability of observer in analyzing the image. This low contrast images are obtained from low radiated images. Such type of medical images are Mias mammogram images, Histogram techniques are used to enhance the contrast of all medical images, these methods are used to find exact locations of cancerous regions and for low-dose CT images.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 High pass filtering”  for medical images. This method uses two step processing. The first step is global contrast of image. This image is enhanced using histogram modification followed by histogram equalization. and then  second Step is   High filter. This filter is used for image sharpening, this filtering if followed by image normalization. To calculate the effectiveness of our method we choose Two metrics. i.e. 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.

Enhancement of the image by using Histogram Modification and High-pass Filtering Mask

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Enhancement of the image by using Histogram Modification and High-pass Filtering Mask