An innovative technique for contrast enhancement of computed tomography images using normalized gamma-corrected contrast-limited adaptive histogram equalization
© Al-Ameen et al.; licensee Springer. 2015
Received: 13 November 2014
Accepted: 2 March 2015
Published: 1 April 2015
Image contrast is an essential visual feature that determines whether an image is of good quality. In computed tomography (CT), captured images tend to be low contrast, which is a prevalent artifact that reduces the image quality and hampers the process of extracting its useful information. A common tactic to process such artifact is by using histogram-based techniques. However, although these techniques may improve the contrast for different grayscale imaging applications, the results are mostly unacceptable for CT images due to the presentation of various faults, noise amplification, excess brightness, and imperfect contrast. Therefore, an ameliorated version of the contrast-limited adaptive histogram equalization (CLAHE) is introduced in this article to provide a good brightness with decent contrast for CT images. The novel modification to the aforesaid technique is done by adding an initial phase of a normalized gamma correction function that helps in adjusting the gamma of the processed image to avoid the common errors of the basic CLAHE of the excess brightness and imperfect contrast it produces. The newly developed technique is tested with synthetic and real-degraded low-contrast CT images, in which it highly contributed in producing better quality results. Moreover, a low intricacy technique for contrast enhancement is proposed, and its performance is also exhibited against various versions of histogram-based enhancement technique using three advanced image quality assessment metrics of Universal Image Quality Index (UIQI), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). Finally, the proposed technique provided acceptable results with no visible artifacts and outperformed all the comparable techniques.
In the field of digital image processing, contrast enhancement plays an essential role in rendering an image clearly recognizable for different imaging applications , including computed tomography (CT). Moreover, contrast enhancement allows an easy distinction of the image components through an appropriate upsurge in its contrast . As a result, it provides a better conception of murky images to enhance visual understanding and to enable precise interpretation . Typical viewers refer to the contrast-enhanced images, as if a curtain of fog has been removed from the filtered image. In computed tomography (CT), captured images tend to be low contrast [4-7], which is a prevalent artifact that reduces the image quality and hampers the process of extracting its useful information. Many reasons have contributed to obtain low-contrast CT images, such as using a low-radiation dose during the examination , different acquisitions, transmission, storage, display devices, and varied kinds of reconstruction and enhancement algorithms . Similarly, partial volume effects may lead to reduce the contrast of the CT image . Moreover, noise can result in low-contrast images . In addition, denoising filters degrade the contrast, while reducing the noise . These factors cause the unnatural appearance of the image by concealing certain important details. Therefore, specially designed techniques should be applied to obtain a better image resolution without any information about the origin of the source degradation. These techniques are mainly classified as spatial and frequency domain techniques . The most popular contrast enhancement methods are the ones that improve the gray-levels of the image in the spatial domain. These methods consist of log and power-law transformations [14,15]; low-pass, high-pass, homomorphic filtering ; histogram equalization ; contrast stretching ; normalization ; and sigmoid function . Recently, histogram modification techniques have received significant attention from researchers because of their direct and instinctive application qualities and their ability to be applied either globally or locally to an image. However, such techniques frequently fail to provide acceptable results for a wide selection of low-contrast images . Histogram equalization (HE) is a common contrast enhancement technique that is widely used by different imaging applications due to its easiness and rapidity . Basically, equalizing the histogram is achieved through the reallocation of pixel values for a given image. However, it has performed poorly in many circumstances because of its drawbacks, such as loss of detail, over enhancement, noise amplification, and the mean shift issue, which produces a remarkable dissimilarity between the illumination of the input and the output images . To overcome the aforementioned drawbacks, various histogram modifications have been proposed to provide more efficient histogram-based contrast enhancement methods, wherein such methods are fully explained in Section 2. Therefore, this study proposes a modified contrast-limited adaptive histogram equalization (CLAHE) technique which can be used to process the low-contrast CT images efficiently. The proposed technique depends on a normalized gamma correction function to improve the unbalanced contrast and reduce the increased brightness of CLAHE. The results obtained through conducting different experiments on various images show a substantial contrast improvement in the filtered images, in which they appear better than their original versions. This article is structured as follows: In Section 2, the related works are adequately explained. In Section 3, the proposed technique is discussed in details. In Section 4, the experimental results and comparisons are exhibited with their related discussions. In Section 5, a summary of important closing remarks is provided.
2 Related works
In this section, many renowned histogram-based techniques are elaborated briefly. After HE, an improved technique was proposed, known as CLAHE  to provide a better contrast for the processed images. However, this algorithm also has drawbacks in that it failed to process some CT images properly and produced unsatisfactory results as the images suffered from unbalanced contrast and increased brightness. Such limitations reduced the reliability of CLAHE to be used as a trustworthy enhancement technique for modern clinical routines. In addition, a brightness-preserving bi-histogram equalization (BBHE) technique was proposed , which separates the processed image into two sub-images depending on the average of the input image. Then, the two sub-images are equalized individually using the HE method. Related to BBHE, dualistic sub-image histogram equalization (DSIHE) technique was offered , which also splits the processed image into two sub-images, but instead of using the average of the input image, it uses its median to increase the entropy of the resulted image. Afterwards, minimum mean brightness error bi-histogram equalization (MMBEBHE) was suggested  to reach a greater level of brightness preservation without revealing the unwanted artifacts by using a minimum absolute mean brightness error (AMBE) function. This function achieves the absolute difference between the input and the output mean values to calculate the threshold that disconnect the input histogram. Due to the time-consuming feature of this algorithm, a specific estimation method was used to calculate the values of AMBE recursively to facilitate its implementation. Simultaneously, a recursive mean-separate histogram equalization (RMSHE) technique  was proposed by the same authors of MMBEBHE. In this method, the mean of every histogram is calculated iteratively (r) times to produce (2 r ) sub-histograms. As a result, the resulting image brightness will increase as the iteration number increases. Similarly, a recursive sub-image histogram equalization (RSIHE) technique was offered , which splits the histogram using a median split-up method rather than the mean split-up one utilized by the RMSHE. The RMSHE and RSIHE are considered to be improved versions of BBHE and DSIHE. However, they invoke two issues: firstly, the amount of sub-histograms must be to the power of two and, secondly, concerning the manner that the optimum value of (r) is chosen. Likewise, a brightness-preserving dynamic fuzzy histogram equalization (BPDFHE)  was proposed. This employs the image fuzzy statistics resulting in a better handling of the gray-level imprecise values to produce an improved image contrast. After that, a non-parametric modified histogram equalization (NMHE) was introduced , which owns an independent parameter setting for an image dynamic range. In addition, it employs an amended histogram function to produce an improved image quality. Lately, an exposure-based sub-image histogram equalization (ESIHE) technique was presented , which utilizes exposure thresholds to split a given image into a group of sub-images. Next, their histogram is clipped by a calculated threshold from the average number of the available gray-levels. Finally, every sub-image is equalized individually and then, these sub-images are combined together to form the complete image. All the aforementioned methods were developed to be used in many scientific applications except for CLAHE, which was developed to be used for medical applications [31,32]. The early application of CLAHE was on low-contrast CT medical images to improve their poor contrast, in which  clarified that it is possible to use this technique for clinical purposes. Therefore, ameliorating the performance of the standard CLAHE is highly desirable since it has a great potential to be applied with modern clinical routines including CT scans.
3 Proposed normalized gamma-corrected contrast-limited adaptive histogram equalization (NGCCLAHE)
As a final point, adding the NGC function to CLAHE can significantly improve its performance, wherein this function helps to reduce the brightness and enhance the contrast of the degraded image. Hence, when applying the CLAHE technique, it can further improve the contrast and increase the brightness of the image. As a consequence, the increased brightness and the unbalanced contrast of CLAHE are adjusted and an adequate visual quality for the processed images is attained. The proposed NGCCLAHE improves the contrast of a given image in seven separate steps as proposed in Algorithm 1, in which steps 2–7 have been established based on [38,39].
Algorithm 1: NGCCLAHE
Step 1: Use the NGC function in Equation 4 to adjust the image contrast as an initial processing phase.
Then, for each region, perform the following:
where h i,j (k) is the histogram of pixel k, and n = 0, 1, 2,…, N − 1.
where α is the clip factor, in which its value can be between 0 and 100. s max is the maximum allowable slope, in which its value can be between 1 and s max.
Step 5: Clip the histogram that exceeds its related clip limit: This step modifies the histogram based on the obtained clip limit by limiting the maximum number of counts, for every pixel to β. This can be archived by retaining the histograms that are less or equal to β, while clipping the ones that exceed β.
where the other corner regions are mapped in a similar way. Finally, the newly obtained pixel values are stored in a new array that the size of which is similar to the original image to form the new enhanced image.
Algorithm 2: The processes of pixel clipping and redistribution.
4 Results and discussion
The recorded accuracy and time of the previous comparison
Based on the obtained results, the proposed technique performed the best in terms of UIQI, SSIM, FSIM, and image visual quality as it scored the highest accuracy values for all the used images. Likewise, the images enhanced by CLAHE had a relatively unbalanced contrast. Additionally, HE gave the worst performance as the resulting images were over-enhanced, had unrealistic contrast, and were affected by visual flaws. Moreover, the output of the BBHE method possesses a bad contrast and is somehow different to the original images. In addition, the MMBEBHE, DSIHE, RMSHE, and RSIHE methods fail to process the input CT images as the results have a comparatively low unrealistic contrast with visual errors appearing on the processed images. Besides, the BPDFHE, NMHE, and ESIHE methods provided a minor contrast improvement without generating any unwanted artifacts. However, they did not reach the desired level of enhancement. As known, the histogram-based techniques involve many calculations. Therefore, the proposed technique is compared to the other techniques in terms of consumed time, wherein all the methods were executed using a 2.3 GHz Core i5 processor and an 8 GB of memory. As seen in Table 1 and Figure 12, the proposed technique required a moderate operation time compared to the other comparative techniques as its results are obtained in an average of 0.3 second. Moreover, the performance of NGCCLAHE was extremely satisfactory as the resulting images appeared more natural and had a better contrast than the other comparative techniques.
An innovative technique for contrast enhancement is proposed in this article, which is convenient for low-contrast CT images. The novelty of the proposed technique lies in the use of a neatly designed NGC function to improve the enhancement ability of CLAHE. Therefore, the enhanced images have a natural appearance without generating the unwanted processing flaws that reduce their visual quality. The experimental results exhibit the efficiency of the proposed technique in comparison to ten well-known state-of-the-art contrast enhancement techniques by using three advanced image quality assessment metrics of UIQI, SSIM, and FSIM. The HE, BBHE, DSIHE, RMSHE, MMBEBHE, and RSIHE techniques produced visible flaws with unrealistic contrast. Moreover, the CLAHE, BPDFHE, NMHE, and ESIHE produced unbalanced contrast with less visual errors than the six aforementioned methods. Finally, the proposed technique performed the best in terms of accuracy metrics and visual quality, as it provided the highest accuracy values and adequate quality with natural appearance results.
The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group no. RGP-264.
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