Histogram Specification Technique Research Articles

Optimized cross-modality guided contrast enhancement for liver tumor segmentation

Radiologists currently use CT images with intravenous contrast infusion, in order to detect lesions and vessels in the liver. This step is quite time consuming when it is done manually. There are many algorithms developed for segmentation which are based on edge or region characteristics. These are dependent on the quality of the image. The contrast of a CT or MRI image plays an important role in identifying region of interest i.e. lesion(s). Traditional enhancement methods suffer from limitations such as saturation, over-enhancement, and uneven contrast spatial distribution that may result from the uncontrolled CE process. One way to overcome such limitations is to combine the contrast enhancement approach with a quality control scheme. One way to overcome such limitations is to combine the contrast enhancement approach with a quality control scheme. Inspired by the guided filtering approach and the simplicity of context-aware histogram-based image quality enhancement, propose in this paper a cross-modality guided histogram specification technique to improve the contrast of liver CT images using MRI images as guiding input data. The proposed method is based on two concepts, namely guided image enhancement and image quality control through an optimization scheme. The proposed OPTimized Guided Contrast Enhancement (OPTGCE) scheme exploits both contextual information from the guidance image and structural information from the input image. Tumor segmentation algorithm is applied on the enhanced images to analyze the performance of the proposed method in facilitating tumor segmentation. The qualitative and quantitative analysis using metrics including entropy, MCCEE, and MIGLCM shows the superiority of the proposed method in comparison with the existing methods that do not include guidance mechanism Keywords—: Cross-modality, contrast enhancement, 2D histogram specification (HS), SSIM gradient, tumor segmentation.

Contrast Enhancement Using Inverted Gaussian Histogram Specification Technique

Contrast enhancement has become a vital process in improving the human perception as well as machine perception, due to the enormous application of digital imaging techniques in various fields like medical, aerospace, agriculture, machine vision, automation, surveillance, etc. During contrast enhancement by various existing techniques, the advent of adverse side effects like brightness degradation, intensity saturation and appearance of artifacts in the enhanced images is a critical issue, which needs more research attention. In this paper, an inverted Gaussian histogram specification technique for contrast enhancement of differently skewed images is proposed, in which the pixel concentration on either side of the histogram is increased to overcome the drawbacks in the existing methods. The proposed technique uses parameters like mean, standard deviation and inversion constant to generate various inverted Gaussian specified histograms. Further, transformation and objective functions are used to identify the best desired histogram so as to obtain the best enhanced image for the given image. The performance of the proposed method is compared with other existing techniques on the images taken from various standard databases. The experimental results show that the proposed technique is best suitable for contrast enhancement of images with differently skewed histograms.

Clinical ultrasound image standardization using histogram specification

This article presents a novel ultrasound image standardization approach. This method aims to preserve the non-linear relationship in the echo-textures while ensuring the endurance in the transformed image. This is achieved by utilizing the concept of histogram specification. A reference cumulative distribution function (CDF) of a considered distribution is used to process the test images. Initially, the shape and scale parameter of the distribution are estimated for each type of echo-texture from the reference ultrasound images of a particular organ. These parameters are used to estimate the prototype parameter set. The obtained prototype parameter set, along with a distribution function, is then used to construct a reference CDF. This CDF, in turn, is used as a transfer function in the histogram specification technique for standardizing the given input image. The efficiency and stability of the proposed approach are investigated and compared with the linear scaling technique. Four measures are used to evaluate the algorithms on three data sets. The results show that the proposed approach provides better standardization of images and is invariant to the gain of the scanning device as opposed to linear scaling.

GPU acceleration of liver enhancement for tumor segmentation

Background and objective: Medical image segmentation plays a vital role in medical image analysis. There are many algorithms developed for medical image segmentation which are based on edge or region characteristics. These are dependent on the quality of the image. The contrast of a CT or MRI image plays an important role in identifying region of interest i.e. lesion(s). In order to enhance the contrast of image, clinicians generally use manual histogram adjustment technique which is based on 1D histogram specification. This is time consuming and results in poor distribution of pixels over the image. Cross modality based contrast enhancement is 2D histogram specification technique. This is robust and provides a more uniform distribution of pixels over CT image by exploiting the inner structure information from MRI image. This helps in increasing the sensitivity and accuracy of lesion segmentation from enhanced CT image. The sequential implementation of cross modality based contrast enhancement is slow. Hence we propose GPU acceleration of cross modality based contrast enhancement for tumor segmentation.Methods: The aim of this study is fast parallel cross modality based contrast enhancement for CT liver images. This includes pairwise 2D histogram, histogram equalization and histogram matching. The sequential implementation of the cross modality based contrast enhancement is computationally expensive and hence time consuming. We propose persistence and grid-stride loop based fast parallel contrast enhancement for CT liver images. We use enhanced CT liver image for the lesion or tumor segmentation. We implement the fast parallel gradient based dynamic seeded region growing for lesion segmentation.Results: The proposed parallel approach is 104.416 ( ± 5.166) times faster compared to the sequential implementation and increases the sensitivity and specificity of tumor segmentation.Conclusion: The cross modality approach is inspired by 2D histogram specification which incorporates spatial information existing in both guidance and input images for remapping the input image intensity values. The cross modality based liver contrast enhancement improves the quality of tumor segmentation.

Improving image quality around subtle lung nodules by reducing artifacts in similar subtraction imaging.

Similar subtraction imaging is useful for the detection of lung nodules; however, some artifacts on similar subtraction images reduce their utility. The authors attempted to improve the image quality of similar subtraction images by reducing artifacts caused by differences in image contrast and sharpness between two images used for similar subtraction imaging. Image contrast was adjusted using the histogram specification technique. The differences in image sharpness were compensated for using a pixel matching technique. The improvement in image quality was evaluated objectively based on the degree of artifacts and the contrast-to-noise ratio (CNR) of the lung nodules. The artifacts in similar subtraction images were reduced in 94% (17/18) of cases, and CNR was improved in 83% (15/18) of cases. The results indicate that the combination of histogram specification and pixel matching techniques is potentially useful in improving image quality in similar subtraction imaging.

A histogram specification technique for dark image enhancement using a local transformation method

Traditional image enhancement techniques produce different types of noise such as unnatural effects, over-enhancement, and artifacts, and these drawbacks become more prominent in enhancing dark images. To overcome these drawbacks, we propose a dark image enhancement technique where local transformation of the pixels have been performed. Here, we apply a transformation method of different parts of the histogram of an input image to get a desired histogram. Afterwards, histogram specification technique has been done on the input image using this transformed histogram. The performance of the proposed technique has been evaluated in both qualitative and quantitative manner, which shows that the proposed method improves the quality of the image with minimal unexpected artifacts as compared to the other techniques.

정확성을 향상시킨 히스토그램 명세화 방법

The histogram specification turns the shape of a histogram into that we want to specify. This technique can be applied usefully in various image processing fields such as machine vision. However, the histogram specification technique has its basic limits. For instance, the histogram does not have location information of pixels. Also, the accuracy of the specification drops because of quantization errors of the digitized image. In this paper, we proposed a multiresolution histogram specification method in order to improve the accuracy of specification in terms of resemblance between destination and source image. The experimental results show that the proposed method enhances the accuracy of the specification compared to the conventional methods.

Image Enhancement by Histogram Specification Using Multiple Target Images

Shape and characteristics of the histogram plays a major role in finding the quality of an image. Histogram Specification is an image enhancement technique, where the histogram of the input image is transformed to a pre-specified histogram derived from a high resolution image, called target image. In this paper, the classical histogram specification technique is extended by using a target image which is obtained by fusing multiple high resolution images. A set of Quality Metrics were identified to assess the quality of the output enhanced image. The paper addresses the following issues: a) Effect of varying the number of target images on the quality of the output enhanced image b) Role of using different methods of fusion on the quality of the output enhanced image c) Category of the target image on the quality of the output enhanced image. If the input image is from a forest, whether in order to obtain an enhanced image, all target images has to be selected from the forest category d) Effect of preprocessing of target image on the quality of the output enhanced image.

Automatic Exact Histogram Specification for Contrast Enhancement and Visual System Based Quantitative Evaluation

Histogram equalization, which aims at information maximization, is widely used in different ways to perform contrast enhancement in images. In this paper, an automatic exact histogram specification technique is proposed and used for global and local contrast enhancement of images. The desired histogram is obtained by first subjecting the image histogram to a modification process and then by maximizing a measure that represents increase in information and decrease in ambiguity. A new method of measuring image contrast based upon local band-limited approach and center-surround retinal receptive field model is also devised in this paper. This method works at multiple scales (frequency bands) and combines the contrast measures obtained at different scales using L(p)-norm. In comparison to a few existing methods, the effectiveness of the proposed automatic exact histogram specification technique in enhancing contrasts of images is demonstrated through qualitative analysis and the proposed image contrast measure based quantitative analysis.

Dynamic contrast enhancement based on histogram specification

A novel contrast enhancement algorithm is proposed. The proposed approach enhances the contrast without losing the original histogram characteristics, which is based on the histogram specification technique. It is expected to eliminate the annoying side effects effectively by using the differential information from the input histogram. The experimental results show that the proposed dynamic histogram specification (DHS) algorithm not only keeps the original histogram shape features but also enhances the contrast effectively. Moreover, the DHS algorithm can be applied by simple hardware and processed in real-time system due to its simplicity.

Image salvage in panoramic radiography using histogram optimization.

Optical density and contrast recovery from poor quality panoramic radiographs was studied using the histogram modification technique. A series of experimental images of varying density and contrast were obtained by exposing a phantom in 4 kV steps between 50 and 94 kV. A standard of good image quality was selected by 10 observers grading the radiographs. Images were processed using the histogram specification technique and re-evaluated. All the processed images were found to be of acceptable quality. This technique was also shown to be applicable for salvaging unacceptable clinical radiographs.