Volume Graphics (2007)H. - C. Hege, R. Machiraju (Editors)

Quality Enhancement of Direct Volume Rendered Images

Ming-Yuen Chan, Yingcai Wu, and Huamin Qu

The Hong Kong University of Science and Technology

AbstractIn this paper, we propose a new method for enhancing the quality of direct volume rendered images. Unlike thetypical image enhancement techniques which perform transformations in the image domain, we take the volumedata into account and enhance the presentation of the volume in the rendered image by adjusting the renderingparameters. Our objective is not only to deliver a pleasing image with better color contrast or enhanced features,but also generate a faithful image with the information in the volume presented in the image. An image qualitymeasurement is proposed to quantitatively evaluate image quality based on the information obtained from theimage as well as the volumetric data. The parameter adjustment process is driven by the evaluation result using agenetic algorithm. More informative and comprehensible results are therefore delivered, compared with the typicalimage-based approaches.

Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Picture/Image GenerationI.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism

1. Introduction

The rendered images of 3D volumetric data using typical di-rect volume rendering (DVR) techniques can provide usefulinformation about the dataset. By specifying a proper trans-fer function, voxels are assigned with certain optical prop-erties and different structures are revealed in the renderedimage. The images of DVR are useful for various kinds ofscientific visualization, like medical diagnosis and life sci-ence research.

Similar to typical images, the quality of the rendered im-ages is critical for effective visual analysis on the images.Certain enhancement operations are always necessary in or-der to obtain better images for further analysis. In the imageprocessing perspective, it is an operation which forms a newimage with a certain mapping of pixel values, in the hopethat a more visually pleasing result will be generated. Moreimportantly, the information in the image should be moreeasily interpreted by viewers for quantitative analysis. For adirect volume rendered image (DVRI), the structures embed-ded in the volumetric data should be presented faithfully inthe final image.

Although numerous image processing techniques havebeen proposed to tackle the problem of image enhancement,most of them are post-processing algorithms focusing on the

image domain. Useful information in the image can be im-proved by increasing the contrast and emphasizing the fea-tures. However, such improvement is limited by the originalimage in which some essential information may be hiddenin the rendering process due to poor lighting or renderingsettings.

We propose a new enhancement method tailored forDVRIs which takes volumetric data into consideration. Byanalyzing the formation of the image during the ray-castingprocess and the constituent sample points along the rays,hidden information can be discovered. An image measure-ment is proposed to quantitatively evaluate the effectivenessof the DVRIs in conveying information in the volume. Theimage is then enhanced to reveal those information by ad-justing various rendering parameters in the transfer function,lighting and reflection models using a genetic algorithm. Ourobjectives are to deliver DVRIs which have satisfactory con-trast and to allow the existing information in the volumetricdata to be more effectively presented in the rendered images.

The rest of the paper is organized as follows. We introducethe previous work related to image enhancement in Section2. Some issues on the image quality of typical DVRIs arediscussed in Section 3. A proposed image quality assess-ment scheme is described in Section 4. The image refine-ment method is then explained in Section 5. Several sug-

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gested adaptive and interactive approaches are introduced inSection 6. Experimental results will be presented in Section7 and conclusions are drawn in Section 8.

2. Previous Work

Image enhancement is a fundamental image processing pro-cedure in computer vision and pattern recognition. To meetvarious subjective visualization expectations, different spa-tial or frequency domain methods may be applied. In prac-tice, convolution is performed on the image with certain fil-tering kernels or filtering is carried out in the frequency do-main. Various effects like smoothing, sharpening and fea-ture enhancement can be achieved. Contrast enhancement isa critical issue as it can improve the image for visual inter-pretation. Some techniques like contrast stretching and his-togram equalization [GW02] are commonly used in typicalapplications. As global histogram equalization cannot dealwith the possible variation of contrast in different parts ofthe image, local and adaptive methods [PAA∗87] have beenproposed to tackle the problem. Different from classical his-togram equalization, histograms are computed from the con-text within a small window and different mappings are ap-plied to the pixels in different parts of the image.

Cromartie and Pizer [CP91] first proposed an edge-basedenhancement approach and Caselles et al. [CLMS99] sug-gested a shape-preserving local histogrammodification tech-nique. These works indicate the importance of the topo-logical meaning in the enhancement process. Neighborhoodmetrics in [EM05] provide further refinement on histogramequalization by considering the local image properties. Morespecific enhancements based on features [BlN89] [Leu92]were also proposed. As over- and under-enhancement are thecommon drawbacks of local histogram equalization, Chenget al. [CXS03] developed a homogeneity measurement to de-fine and control the contrast. More variations on contrast en-hancement can be found in [Sta00] [DJT93].

Besides the grey scale image enhancement approaches,more works have been done on color images [PNS03][SMCD03] [NM03]. Gooch et al. [GGSC98] on the otherhand suggested a non-photorealistic lighting model anddemonstrated the importance of shape information and clearvisual distinctions in technical illustrations. However, asdifferent distinct colors are always assigned to differentstructures in transfer functions for typical DVRIs, the colorchanges in the lighting model may affect the visual percep-tion of viewers. Entropy-based methods have been proposedin [Gum02] [VFSH01] to refine such parameters to obtainbetter results.

The limitation of the existing image-based enhancementapproaches is that they can neither recover the missing de-tails due to poor rendering parameters nor enhance the struc-tures with respect to their topology and shape in the volumet-ric data. Based on the observation that image contrast is a de-

terminant psychological factor [TM99] to the cognitive abil-ity of the viewers, we propose a new enhancement methodfor effective visualization of DVRIs and the correspondingvolumetric data, by considering the existing structures in thevolume.

3. Typical Problems in DVRIs

Direct volume rendered images can be generated using thevolumetric ray-casting method. A ray is generated for eachpixel and is casted from the eye to the volume. Each pixel inthe image is the composite value along the ray where pointsare sampled in the volumetric data. The image pixel valuesare attributed to voxels of different structures and the overallimage should depict the presence of the structures.

However, the structures may not be clearly shown in therendered images. Due to various reasons like poor lightingand reflection parameters, the pixel values cannot give anyimplication on the existence of the structures. For exam-ple, a homogenous region in the image may represent thefine details of a structure. The image should have a varia-tion to indicate this. Typical image-based enhancement al-gorithms cannot solve this problem as they are merely trans-formations of images and do not have any idea on the actualscene/volumetric data and the image synthesis process. Be-sides, conventional methods only improve the quality of animage based on the existing features in the image. The basicphilosophy is that no information should be created or de-stroyed. However, with the help of the volumetric data andthe knowledge of the rendering process of DVRIs, we canfurther improve the image quality accordingly and reinforcethe hidden details about the volume in the image.

Fig. 1 demonstrates the limitation of existing image-basedsolutions by using the CT engine dataset. The shape anddetails of the engine are not clearly shown in the originalimage. By equalizing the image using image-based tech-niques, we can obtain an image with better contrast andedges are emphasized. However, the overall color is distortedand some fine details are suppressed in the dark regions.Further improvement can be achieved in the manually en-hanced image using various filtering techniques. Comparedto these results, our method can deliver a more promisingimage with clearer details and shapes. It shows that a sig-nificant enhancement cannot be achieved without consider-ing the structures and their shapes, while lighting and otherrendering parameters play an important role in the process.The improvement in image-based methods is limited by themissing information in the original images.

Typical enhancement methods attempt to strengthen thesubtle features in the images and make them visible to theviewers. Our method, on the other hand, enhances the im-ages based on existing information in the volume. The ob-jective is to detect the possible existence of structures by an-alyzing the variations involved in the ray composition. To

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(a) (b)

(c) (d)

Figure 1: An example using the CT engine dataset: (a)shows the original image with a poor contrast; (b) and (c)are the images enhanced by Photoshop and manual adjust-ment using various image filters; (d) is the result generatedby adjusting the rendering parameters.

reveal such information in the image, the final DVRI shouldalso demonstrate certain degree of variation in terms of pixelintensity.

4. Image Quality Assessment

The quality of DVRIs is defined as the effectiveness of therendered images in presenting the information in the volu-metric data. The basic idea is to determine whether the im-age can show a significant variation in regions where the rayscarry different information (e.g., passing through differentstructures in the volume or varying in the composition of therays). To quantitatively analyze a DVRI, we establish severalmeasurements for both image and volume data information.

4.1. Image Measure

The variation in an image can be interpreted as contrast andthe overall contrast of an image can be estimated by the Lya-pounov functional suggested in [CLMS99] [SC95]:

E(v) =|Ω|

2(b−a)Z

Ω(v(x)− b−a

2)2dx

−14

ZΩ

ZΩ|v(y)− v(z)|dydz (1)

where Ω is the image and the intensity range is from a tob and v is the mapping function of pixel values. It can indi-cate the variation of pixel value in an image. A homogeneous

region should have a low energy value. We define pixel dif-ference Δv in color images in terms of luminance L, which isan effective metric to indicate the visual variation to viewers’perception:{ |Δv| = |v(x1)− v(x2)| = |L(x1)−L(x2)|

L(x) = 0.3xR+0.59xG+0.11xB(2)

Energy function E in Eq. 1 indicates the variations in theimage and the utilization of colors for visual presentation. Itprovides useful information for better transfer function de-sign. Furthermore, we estimate the local variation in the im-age with a window of size ω using standard deviation σ (Eq.3) and entropy h (Eq. 4) [CXS03]:

σ(x) =

√1|ω| ∑ω

(v(i)−μx)2 (3)

h(x) = − 1log|ω| ∑i

pilogpi (4)

where pi is the probability of having a pixel value of i andμ is the mean pixel value in the window. These two termscan be used to estimate visual information and we generalizethem into an image measureMI as

MI(x) = σ(x)×h(x) (5)

4.2. Ray Measure

Voxels in the volume are assigned with different opacitiesbased on the transfer function specified by users for reveal-ing different structures. In the ray-casting process, the pixelintensity is the composite value of the sample points alongthe ray’s paths traversing from the viewpoint to the volu-metric data. This allows different layers of structures to bevisible in the final image. The compositing equations can bedescribed as:{

caccum = csαs(1−αaccum)+ caccumαaccum = αs(1−αaccum)+αaccum (6)

where c and α are the color and opacity values. Each samplepoint contributes to the final image in different degrees andtheir contribution can be estimated by α(1−αaccum). Thesample points with zero or insignificant contribution becomeinvisible.

We therefore estimate the information carried by the raysand their variations by considering those visible samplepoints along the rays. As the mutual information (Eq. 7) is aneffective metric for image similarity measure and the entropyterm (Eq. 8) is commonly used to represent the dependenceof information contents,

I(R1,R2) = H(R1)+H(R2)−H(R1,R2) (7)

H(R) = −∑ipilogpi (8)

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we follow this approach to design a ray information measure.The ray measure is represented as

MR(R) = −∑ipi∑

jpi( j)logpi( j) (9)

where j represents the position along the ray R and pi( j) isthe probability that a sample point is located at j given in-tensity i. Ray measure HR estimates the dependance amongthe rays by considering both the intensity and position ofthe sample points along the rays. By using this measure,we can estimate the variations in the intensity distributionand profile as well as the position of the sample points on aray with the context. As the probability terms are computedfrom all the neighboring rays within the windows, the en-tropy term can signify the information variation among therays. As noise may introduce undesired information, it hasto be suppressed in the volumetric data.

4.3. Composite Measure

By considering the information in the image and ray do-mains, which are represented as image measure MI and raymeasure MR, we can derive a composite measure on imagequality. The value of MI and MR are normalized to [−1..1].Composite measureMC is given by

MC = (1+ exp(−−MI +MRs ))−1 (10)

where s is the steepness of the curve. This composite mea-sure indicates the deviation between the image and ray in-formation at each pixel in the image. It produces a high re-sponse when ray measureMR is high but image measureMIis low (i.e., large variation in ray information and small vari-ation in the image). Based on this measure, we can optimizethe rendering parameters to achieve a better result by min-imizing the overall information deviation - preserving theinformation of the volume in the image domain.

5. Parameter Refinement

From the example in Section 3 (see Fig. 1), we know thata better result can be obtained by adjusting different para-meters involved in the rendering process. It can be a diffi-cult and tedious task for non-experts to adjust several para-meters simultaneously. Therefore, we propose a frameworkusing a genetic algorithm to automatically optimize the pa-rameters. The aforementioned image measures are also in-corporated into the process. The framework of the iterativeenhancement process is shown in Fig. 2. In this section, wewill cover the rendering parameters involved in our enhance-ment process and the details of the refinement process willbe explained.

5.1. Reflection/Illumination Model

Recall that the lighting effect has a major impact on thevisual perception of an image while it cannot be easily re-

DVRImage Parameter

Adjustment

Image Quality Analysis

VolumeData

ImageMeasurement

RayMeasurement

QualityMeasurement

Rendering Pipeline

Reflection ModelLighting

Transfer Function

Figure 2: A flow-chart demonstrating the image enhance-ment process.

stored or improved by typical image-based enhancement ap-proaches. Therefore, we should directly adjust lighting con-figuration. In typical shading models, the visual result is de-termined by the reflection model and its parameter settings.The lighting effect can be indirectly controlled by the am-bient, diffuse and specular coefficients. In a condition withsufficient lighting, shape perception and also the overall con-trast can be improved. Therefore, these parameters have tobe adjusted in our framework.

Different from typical rendering approaches that a globalsetting is applied to all the voxels, our refinement methodallows different reflectance values to be assigned to voxelsof different intensities. It is similar to a transfer functionon reflectance. Therefore, the lighting effect on structureswith different intensities can be adjusted individually andthis makes the structures more visually distinguishable in theimage.

5.2. Transfer Function

In DVR, users first define a transfer function for the vol-umetric data to specify different optical properties for dif-ferent structures. Color is an important property subject torefinement. However, as mentioned in many previous liter-ature [NM03] on image enhancement, the color of the im-age should not be distorted in order to preserve the originalmeaning of the image. Most researchers agree that hue mustremain unchanged during the enhancement process. Usually,only the brightness and saturation in HSV or luma informa-tion in YIQ are modified. For volume rendering, this prop-erty is ultimately important as each class of structures is as-signed a specific color. Any inconsistence in color may leadto misinterpretation in visual inspection. We can preservethis property by transforming the transfer function spacefrom original RGB to HSV or YIQ color space and manip-ulating only on the "safe" channels during the refinementprocess. To tackle the gamut problem in the transformation,clipping techniques [YR96] can be used. By adjusting thebrightness of structures, different structures can demonstratea more noticeable difference in appearance without any se-

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vere changes to the original colors assigned by the transferfunction. This can help preserve the original meaning of theDVRI during the enhancement process.

5.3. Genetic Algorithm

The adjustment of the parameters is a combinatorial opti-mization problem with a huge solution space. To efficientlysearch for an optimal solution in the parameter space, weemploy the genetic algorithm (GA) [HHKP96]. This methodhas been used in scientific visualization [WQZC06]. In theGA, the candidate solutions are encoded as genomes, inwhich the parameters are evenly sampled at different inten-sities in the transfer function of the solution and are rep-resented as an array. The image quality measure is treatedas the objective function to calculate the fitness measure.The GA is driven by the fitness measure and the genomeschanges during the evolution process to obtain a better re-sult. The GA terminates when the results converge. The fi-nal result is considered as the optimal setting with the bestimage quality.

The advantages of the GA are that the stochastic searchcan avoid local optima and the computation time is not di-rectly related to the number of parameters used. A tradeoffcan be made between the performance and quality by chang-ing the GA’s parameters. The process terminates when theresult becomes stable, with an optimal image quality.

Although a better result can also be obtained by man-ual adjustment on the parameters, the process may be time-consuming. The GA provides an automatic method to refinethe parameters with respect to image quality. A more de-tailed adjustment on different parameters is performed andvoxels of different intensities are assigned with different op-timal values. This is difficult to achieve manually.

6. Adaptive Enhancement and User Interactions

Although global parameter adjustment can help deliver animproved configuration with a better overall image quality,adaptive enhancement can also be exerted to different partsof the image. Following the same argument for conventionaladaptive image enhancement approaches, small details maybe under-enhanced in the global configuration and certainstructures in the image may have to be further enhanced forspecific purposes. A flexible adaptive enhancement methodwith user interactions is necessary to achieve various visual-ization goals.

In our image enhancement framework, user manipulationsare supported and the regions for further enhancement canbe specified in the image and/or data domain. By manu-ally highlighting in the image, the rays in the selected re-gions are analyzed and refined together. This allows moreaccurate refinement to recover those fine details which areinsignificant and may be easily ignored in the overall im-age enhancement process. Similarly, users can select certain

classes of structures at an intensity level using the histogramor transfer function, and perform enhancement on the cor-responding structures in the image. The sample points thatfall within the selected intensity range will be further pre-served or improved in the process. Moreover, users can referto the response image of the measures (Fig. 3) to locate theregions where information exists and can be improved. Forexample, with reference to the composite measure response,users can determine regions with strong response where theinformation in the volume is not preserved well in the DVRIand select them for further enhancement.

7. Experimental Results

To evaluate our proposed method, experiments have beenconducted on several volumetric datasets and the perfor-mance and effectiveness of the results are discussed. Theexperiments were carried out on a standard PC machine(Pentium Core2Duo 6300, 2GB RAM) equipped with anNVIDIA GeForce 8800GTS graphics card.

To evaluate the quality of a DVRI, image and ray measureresponses are first computed based on the proposed qualitymeasurements (Eq. 5 and 9). Fig. 3 shows the response im-ages generated from a CT head dataset. The image measureresponse (Fig. 3(b)) represents the variation of the pixel val-ues in terms of entropy and standard deviation. It capturesthe features (e.g., edges and silhouettes) and color intensityvariations in the image. A high response value implies a bet-ter visual awareness of the image information to viewers.The ray measure response (Fig. 3(c)) on the other hand cap-tures the variation of the rays. Such variation can reveal thestructural information in the volumetric data, which shouldbe clearly shown in the rendered image. By analyzing theimage and ray measure responses, we can derive a compos-ite measure response (Fig. 3(d)) using the sigmoid function(Eq. 10). The quality of the image is determined by whetherthe information present in the volumetric data (i.e., ray in-formation) can be effectively presented in the image. A highresponse indicates that the variation in the volume cannot bereflected in the DVRI. Therefore, we have to minimize theoverall response in order to obtain a DVRI with more infor-mation preserved. In this example, several features on theface are not clearly shown and they result in a relatively highresponse in the response image.

The genetic algorithm is then applied and the renderingparameters are continuously refined and different DVRIs aregenerated in the evolution process. Some intermediate re-sults are shown in Fig. 4. During the evolution process, theintermediate results are evaluated using our image measureand only good results are selected for further processing. Fig.5 shows the final result generated by our method. Comparedwith the original DVRI, the overall image measure responseis higher and this implies that the image variation on featuresare improved. This can be reflected in our enhanced DVRI,in which features on the face are better preserved. This con-

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(a) (b) (c) (d)

Figure 3: An experiment on a CT head dataset: The features in the original DVRI (a) is not clear and the contrast is notsatisfactory. These are reflected in the image measure responses of the DVRI (b). (c) is the ray measure response image whichindicates the presence of information in the volume. (d) is the composite measure response which shows a high response at theregions where information in the volume is not preserved well in the DVRI.

(a) (b) (c) (d) (e)

Figure 4: Results generated in the evolution process using the genetic algorithm: (a) original DVRI; (b)-(d) intermediate resultsand (e) final result.

forms with the result that the composite measure responseis reduced. A result enhanced by the image processing toolsin Photoshop is also shown for comparison. Our result canpreserve the fine details to a larger extent.

The performance of the process depends on various fac-tors. As the DVRIs are repeatedly generated and analyzed,the rendering speed and the image measurement computa-tion become the critical factors. However, as the commoditygraphics hardware nowadays can obtain a sufficiently highframe-rate (about 20-30 FPS), the rendering speed issue be-comes less significant. In the image measurement, the ray in-formation measure has to be computed only once. Althoughthe image information measure has to be re-computed forevery intermediate DVRI, it only takes about 0.3 second fora 512×512 DVRI with a window size of 3.Under the GA framework, we can always make a trade-

off between the DVRI quality and the performance. By low-ering the gene population, mutation and crossing rate, theresult converges in a shorter time. This may, however, dete-riorate the optimality of the final result if the complexity ofthe problem is high. In our experiment, an optimal result isdelivered in about 60 seconds by setting the population to5 and the mutation and crossing rate to 0.2 and 0.3, respec-

tively. The quality of the result is similar even with highervalue GA parameters.

Fig. 6 shows a comparison between our results with thoseof image-based enhancement. It can be found that the finedetails are better preserved in our results. The improvementof the image-based enhancement approaches is limited bythe original image, in which the details may be hidden or un-recognizable due to the insignificant variation in color. Ourmethod takes both the image and volumetric data into ac-count and, therefore, can reveal more hidden features. More-over, with a proper rendering setting, not only are the vari-ations in color of the image emphasized, the variations dueto the structure shapes in the volume are also amplified. Theperception of 3D shapes and layers is better preserved as aresult in the final image.

8. Conclusion

This paper presented a new enhancement method tailoredfor DVRIs. Different from the typical image-based trans-formation approaches, the proposed enhancement method isdriven by the existing information in both the image and thevolume. We are not only seeking for aesthetic results, butalso delivering faithful DVRIs which can effectively convey

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(a) (b) (c) (d)

Figure 5: Final results of the CT head experiment: (a) is an enhanced DVRI using our method. As shown in the image measureresponse (b), the overall contrast is improved and the details are better preserved on the face. The composite measure response(c) is reduced as a result. (d) is the enhanced result using various image processing tools in Photoshop.

(a) (b) (c)

(d) (e) (f)

Figure 6: Experiments on different datasets: (a) (d) original DVRIs; (b) (e) enhanced images using Photoshop; (c) (f) ourresults. Features are better presented in our results, as shown in the red boxes.

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the information in the volume. We proposed an image qual-ity assessment scheme with regard to the information in therendered image and volumetric data. It measures the effec-tiveness of the image in conveying the information about thevolumetric data. By adjusting the rendering parameters us-ing genetic algorithm, a more pleasing and informative re-sult is delivered. The GA efficiently solves this parameteroptimization problem and provides an optimal rendering set-ting and thus the best DVRI quality. The proposed measure-ment can also assist users in performing adaptive and inter-active enhancement on DVRIs to achieve different visual-ization purposes. Although the computation is more compli-cated comparing with the typical image-based enhancementapproaches, the performance can be improved by adjustingthe GA parameters and optimizing the rendering pipeline us-ing GPU.

Acknowledgement

We would like to thank Ms Denise Tong for proofreadingthe paper and the anonymous reviewers for their valuablecomments. This work was partially supported by RGC grantCERG 618705.

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c© The Eurographics Association 2007.

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