Abstract
<p>Images that are obtained in the real world in low contrast are inappropriate for human eyes to read the medical images. Enhancement and segmentation have an important role to play in digital image processing, pattern recognition, and the computer vision. Here, this paper presents an effective way of changing histograms and improving contrast in digital images. Segmentation is done on AGCWD enhanced images. Histogram equalization is an important technique for contrast enhancement. Nevertheless, modern Histogram Equalization commonly results in unnecessary contrast enhancement, which in turn offers an un-natural presence to the processed image and produces visual artifacts. We present an automated transformation technique that helps boost dimmed image brightness by gamma correction and weighted distribution, commonly known as Adaptive Gamma Correction Weighted Distribution (AGCWD). The contrast enhancement level can be modified using this technique; noise robustness, white or black stretching, and the protection of medium brightness can be easily integrated into the optimization process. Finally, a contrast enhancement algorithm with low complexity is introduced. All the process of enhancement will be done during the process of pre-processing the image. Later, in post-processing, we introduce a specific level set method known as ORACM for better segmentation of an enhanced AGCWD image, and it is compared with the traditional level set method.</p>
Highlights
Medical images are a special type of image that can be used to diagnose the disease in patients
Medical images are a distinct kind of image that can be used for the diagnostics of disease in the patients
Results of the proposed methods are contrasted with the techniques such as Histogram equalization (HE), Adaptive gamma correction via weighted distribution-Adaptive Gamma Correction Weighted Distribution (AGCWD), ACM with SBGFRLS, Online Region based Active Contour Model (ORACM)
Summary
Medical images are a special type of image that can be used to diagnose the disease in patients. There are several methods for obtaining these images, just like computed tomography scan, magnetic resonance imaging and X-ray imaging. These types of imaging methods result in poor contrast to medical images. Medical images are commonly categorized by a low signal to noise ratio (SNR) in addition to low contrast to noise ratio, along with multiple and discontinuous edges. Various methods of image enhancement have been suggested in the literature [5] to improve the appearance of images for better visual interpretation, understanding, and for analysis
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