A Framework for Cross-Modality Guided Contrast Enhancement of CT Liver Using MRI

In liver medical imaging, physicians always detect, monitor, and characterize liver diseases by visually assessing of liver medical images. Computed Tomographic (CT) imaging is considered as one of the efficient medical imaging modalities in diagnosis of various human diseases. However, imprecise visualization and low contrast are the drawbacks that limit its utility. In this paper, a novel approach of multimodal liver image contrast enhancement is proposed. The idea behind the proposed approach is utilizing MRI scan as guide to exploit the diversity information extracted to enhance the structures in CT modal of liver. The proposed enhancement technique consists of two phases of enhancement to assess local contrast of the input images. In the first phase, the two image modalities are converted to the same range as the range of MRI and CT are different. Then, we did transformation of CT image so that its histogram matches the histogram of MRI. Second, the adaptive gamma correction-based histogram modification is utilized to get enhanced CT image. The subjective and objective experimental results indicated that the proposed scheme generates significantly enhanced liver CT.

Image Enhancement Methods for Remote Sensing: A Survey

The quality of the images obtained from remote sensing devices is very important for many image processing applications. Most of the enhancement methods are based on histogram modification and transform based methods. Histogram modification based methods aim to modify the histogram of the input image to obtain a more uniform distribution. Transform based methods apply a certain transform to the input image and enhance the image in transform domain followed by the inverse transform. In this work, both histogram modification and transform domain methods have been considered, as well as hybrid methods. Moreover, a new hybrid algorithm is proposed for remote sensing image enhancement. Visual comparisons as well as quantitative comparisons have been carried out for different enhancement methods. For objective comparison quality metrics, namely Contrast Gain, Enhancement Measurement, Discrete Entropy and Average Mean Brightness Error have been used. The comparisons show that, the histogram modification methods have a better contrast improvement, while transform domain methods have a better performance in edge enhancement and color preservation. Moreover, hybrid methods which combine the two former approaches have higher potential.

Reversible Watermarking in Medical Images Using Sub-Sample and Multiple Histogram Modification

This paper proposed a reversible medical image watermarking scheme using multiple histogram modification (MHM) and redundant discrete wavelet transform (RDWT). The MHM was introduced to the proposed scheme to enhance the embedding capacity. By embedding the watermark in the RDWT coefficients, the proposed scheme exploited the visual masking property of RDWT to guarantee the visual quality. Also, the proposed scheme has better performance on embedding capacity because the RDWT has several sub-band coefficients for embedding. The experimental results on medical images suggests that the proposed scheme could meet the demand of perceptional quality with better embedding capacity than former schemes.

Novel Framework for Knee Arthroscopic Image Enhancement

An arthroscope is a tool for allowing an endoscope to be inserted directly into the inside of a joint to observe its structure, in contrast to X-rays, computed tomography, and magnetic resonance imaging, which directly capture pictures of a joint. Therefore, it can effectively treat joint diseases by identifying causes of pain that are not found by, e.g., computed tomography and magnetic resonance imaging. However, joint endoscopy has a very high cost, is very burdensome for patients, and has problems in regards to infection when being re-used. Thus, we developed disposable joint endoscopic camera modules for preventing re-use and infection, and researched approaches to reducing patient waiting times and cost burdens. In that regard, it is necessary to improve the brightness and color of the images, as they are used for compacting and disposal of the camera modules. In addition, we studied methods for improving automatic images, as image colors may vary (owing to blood or other foreign substances) when observed using the arthroscope. The proposed framework is divided into two sequences. First, we perform a histogram modification algorithm as an image enhancement technique. This results in a brightness optimization effect on the arthroscopic image. Second, we conduct a high saturation color mapping before proceeding to the next step. In particular, one of the reference points for diagnosing a disease is color information; thus, the improvement of color saturation is considered first in the color mapping. The proposed method provides better brightness values while preserving color information.

A Systematic Review of various Reversible Data Hiding Techniques in Digital Images

: Nowadays, Internet has become everything, it made things simpler like online transaction, online shopping; sharing images, videos, audios, messages on social media; uploading some important information on Google drives etc. So the very first requirement is to secure and protect digital contents from any unauthorized access. Reversible Data Hiding (RDH) is one of the ways to provide security in digital content, through which useful information can be embedded in the digital content and at the receiver end the perfect recovery of cover media as well as embedded message is possible. In this digital era, digital images are most rapidly used for communication purpose; therefore the security of digital images is in high demand. RDH in digital images has gained a lot of interest during the last few decades. This paper describes and investigates a systematic review on various RDH techniques for digital images, which can be broadly classified into five categories: Lossless Compression Based, Histogram Modification Based, Difference Expansion Based, Interpolation Based and Encrypted image Based techniques.