330 IEEE TRANSACTIONS ON MEDICAL IMAGING. VOL. IO. NO. 3, SEPTEMBER 1991

Enhancement of Chest and Breast Radiographs by Automatic Spatial Filtering

Pablo G . Tahoces, JosC Correa, Miguel Souto, Carmen Gonzalez, Lorenzo Gomez, Member, IEEE, and Juan J. Vidal

Abstract-Advances in digital chest and breast radiography require optimal image processing techniques. We present a new algorithm to enhance the edges and contrast of chest and breast radiographs while minimally amplifying image noise which consists of a linear combination of an original image and two smoothed images obtained from it using differents masks and parameters, followed by the application of a nonlinear contrast stretching. The result is an image which retains the high me- dian frequency local variations (edge and contrast-enhancing).

I. INTRODUCTION HEST Radiography still accounts for 40% of the total C number of examinations in radiology [ l ] and al-

though the success of digital techniques in several medi- cal imaging modalities has generated considerable interest in chest radiology, there are still problems of chest ra- diography as they relate to digital systems. It requires high spatial resolution to capture the fine detail of branching structures such as the pulmonary vasculature and bronchi [ 11. On the other hand, it is not possible to obtain optimal film density and image contrast in all areas of a conven- tional chest radiograph because this technique is com- promised by limited exposure latitude and as a result it cannot, in general, simultaneously optimally display pul- monary and mediastinal information [2]. Another cause of contrast degradation is scattered radiation, which may be quite intense in some areas of the thorax [ 3 ] . Although these are difficult problems to solve, the digital format would provide the opportunity to improve the image throughout the application of processing algorithms. For example, 20-30% of proven pulmonary nodules are missed on the initial reading of films of a photofluorog- raphy survey for lung cancer [4] and some kinds of image processing applied to selectivity enhance underexposed areas of conventional screen film images improve nodule detection [5]. In clinical practice, radiologists frequenctly overlook subtle abnormalities which, in retrospect, are clearly visible [6].

Manuscript received July 29, 1990; revised May 12, 1991. This work was supported by the FISss under Grants Expte 8811372 and 8910486, and by the Xunta de Galicia under Grant XUGA 842C0489.

P. G. Tahoces, J . Correa, and L. Gomez are with the Laboratory for Radiologic Image Research and Department of Electronics, University of Santiago de Compostela, Spain.

M. Souto, C . Gonzalez, and J . J . Vidal are with Laboratory for Radio- logic Image Research and Department of Diagnostic Radiology, University and Hospital of Santiago, de Compostela, Spain.

IEEE Log Number 9102 130.

Conventional mammography using film/screen or xeroradiographic techniques continues to be the only im- aging technique with the proven capability of detecting clinically occult breast cancer. For this reason, mammog- raphy may eventually constitute one of the highest volume X-ray procedures routinely interpreted by radiologies [7]. Several studies have revealed a propensity toward cancer in breasts with certain types of mammographic breast pa- renchymal patterns (MBPP) [8]-[ 101. Breast composed predominately of dense parenchyma are difficult to pene- trate with film/screen mammography which results in a significantly decreased diagnostic certainty for interpre- tation [ l l ] . Finally, between 30 and 50% of breast car- cinomas detected radiographically demonstrate microcal- cifications on mammograms, and between 60 and 80% of breast carcinomas reveal microcalcifications upon micro- scopic examination [ 121. Therefore, any increase in the capability to classify MBPP, in penetrating dense breast parenchymal patterns or in the detection of microcalcifi- cations by mammography would lead to further improve- ments in its efficacy in the detection of early breast can- cer.

The purpose of this work is to evaluate the potential utility and application of a new algorithm that we use for chest and breast images in order to investigate the impor- tant question of the effect of image processing parameters on the radiologist perception.

11. METHODS All of the radiographs presented in this work were stan-

dard chest and breast films from the radiology reading room. These films were digitized to 512 * 512 * 8 b with a 900 lines definition TV camera Siemens K-30 Vidicon. The digital image processor was a Data Traslation, DT2658. A Microvax I1 Computer under a VMS operat- ing system was used for all the calculations. All computer programs were written in DT/IDL. The interactive video display system consists of two monitors, one for com- munication with the computer and another Mitsubishi 780 lines for displaying images. Density and brightness levels can be varied from the control of the monitor. The digi- tized images are transferred to the image memory of the image processing system, and are viewed at a video dis- play console that allows interactive processing of the dis- played images. Digitized images may be stored on a 780

0278-0062/91$01.00 1991 IEEE

TAHOCES er al . : RADIOGRAPHS BY AUTOMATIC SPATIAL FILTERING

c t I

1 OUrPUTlYAGE

331

I 1

i a 200 ’

J w 150 5

a

a 100 2

I- 50 s 0

-190 -37.5 115 267.5 428 INPUT PIXEL VALUE

Fig. 2. Resultant histogram after filtering and modified linear contrast stretching function.

Fig. 1. The block diagram of the feature-enhancement process

Mbyte Winchester Disk System. Hardcopy of the 8 b im- ages was provided with a Matrix camera, and some of these films were subsequently photogenerated for publi- cation. The 512 by 512 matrix size is roughly according to the resolution of the other components of the system.

Our processing method is a four-step process (Fig. 1). The first step is to obtain from the original two different smoothed images, as it can be seen later. The second is to build a linear combination of these three images. The third step is to calculate the nonlinear contrast function transformer. The four step is the nonlinear contrast stretching with the transform generated in the third step. It can be described mathematically as follows:

n = 2 Zp(x, y ) = F(K; * Zj(X, y ) )

where Z p (x, y) is the value of the picture element (x, y ) on the processed image, lo@, y ) is the value of the picture element (x, y) on the original image, Zl(x, y ) and Z2(x, y ) are the values of the picture element (x, y ) on the smoothed images, KO, K , , K2 are weighting factors and F = F(k) is the nonlinear contrast stretching function.

1 ) The original image Io = Zo(x, y ) is processed to ob- tain two smooth images Z, = Zl(x, y ) and Z2 = Z2(x, y) using a low-pass filtering [13], [14] as follows:

x o + i , w + i , ~~

Zi(X0, Yo) = (1 /Wi’) * c , c Zo(x, y) x - x u - J I y = ~ u - j ,

where

Zo = Original image

Zi = Smoothed images

j ; = (Wj - 1)/2

W, = Window size.

So, we replace the gray level of each pixel by the mean of the gray levels in a neighborhood of that pixel.

2) and 3) When the linear combination of Zo(x, y), Zl(x, y ) and Z2(x, y) is built Zp(x, y ) = KO * ZO(X, y) + K I * Zl(x, y ) + K2 * Z2(x, y ) we calculate the nonlinear contrast func- tion transform from this combination (see appendix) F = F(k) with k gray level (Fig. 2).

4) Finally, image processed Zp = Z&, y) is obtained Z P k Y ) = F(I;(x, Y ) ) .

111. RESULTS AND DISCUSSION Fig. 3 shows the optical transfer function (OTF) for

three cases in order to see the effect of our algorithm in the frequency domain. Two cases (dotted line) are un- sharp masking OTF with mask size of 25 * 25 and 7 * 7. The other (continued line) is our algorithm OTF. So, we can enhance not only high frequencies (unsharp masking with 7 * 7 mask size) but also median frequencies (un- sharp masking with 25 * 25 mask size).

The image presented in Fig. 4(a) is an unprocessed chest radiograph digitized to 512 * 512 pixels with 256 gray-level resolution. It demonstrates a problem common to many radiographs of the chest, that is, when structures in the lungs are reasonably imaged, the mediastinum re- mains underexposed and structures in this region are not well seen. Therefore, one goal of image processing would be to improve the visibility of the mediastinum without compromising that achieved in the lungs.

As Johnson and Sherrier have pointed, global histo- gram equalization is not adequated to solve this problem [15], [16], [ 5 ] . Another technique for edge and contrast enhancement of radiographs have been previously sug- gested. Ishida et al. [ 171 improved detection of low-con- trast objects by using digital unsharp masking, including those of chest conventional radiographs. Sezan et al. [ 181 have used anatomically selective unsharp masking pro- duced images with higher image quality than those ob- tained by nonselective unsharp masking. Foley et al. 1191

332 IEEE TRANSACTIONS ON MEDICAL IMAGING. VOL. IO, NO. 3, SEPTEMBER 1991

t

8 i L I ~ " ' l " " I ' ~ " 1 I " ' , ' 0 0 . 0 5 0 . 1 0 . 1 5 0 . 2 0 . 2 5

SPATIAL FREQUENCY ( c y c l e s / m m )

Fig. 3 . Optical transfer function for three differents filters. (a) Unsharp- masking with 7 * 7 mask size, (b) unsharp-masking with 25 * 25 mask size, and (c) our algorithm with 7 * 7 and 25 * 25 mask sizes.

made an initial attempt to examine the effects of "high- pass filtering" technique on nodule detection, and al- though the method was not superior to conventional chest radiographs, it could be improved with increase in local image contrast and edge enhancement by varying the fil- tering parameters. ''Adaptive Filtration" improved de- tection of subtle nodules in the retrocardiac/subdiaphrag- matic areas, but this technique requires about 10-15 min of computer time [ 5 ] . McAdams et al. [20] proposed a technique for anatomically selective gray-scale modifica- tion and unsharp masking in digital chest radiography. However, their method for determining the gray-level threshold requires human intervention and, therefore, is impractical for routine application.

Because a high-pass filter emphasizes the high-fre- quency components, and because background noise typi- cally has significant high-frequency components, high- pass filtering tends to increase background noise power when it is applied to a degraded image. This is a major limitation of high-pass filtering for image enhancement and this should be realized when considering a high-pass filter to enhance an image [21]. The smaller the mask in the filter, the more the high frequency details are en- hanced and thus, not only edges but also noise are en- hanced.

Fig. 4(b) and (c) show the result of applying unsharp masking technique with 7 * 7 mask size and 25 * 25 mask size to the image in Fig. 4(a). The effect of our automatic spatial filtering is shown in Fig. 4(d) and the result of using a nonlinear function in the contrast stretching is shown in Fig. 4(e). Details in the mediastinum (azygo- esphageal line, trachea, bronchi, heart border, and retro- cardiac area) have been enhanced, while visibility of lung structures is also significantly increased, and the improve-

ment was clearly attributable to the facility of digital im- age manipulation.

The difference between unprocessed and after process- ing images can be appreciated with the plot shown in Fig. 5. This figure is a line plot (plot of pixel value versus position along one row of the image) through the central part of the chest radiograph of the original image and the filtered image.

However, there is one aspect of this study that deserves comment. For digital systems using screen films or simi- lar image receptor in chest radiography, the use of a pixel size substantially larger than 0.1 mm may result in some loss of diagnostic accuracy [22]. Paul Capp have also de- termined that there are no significant differences when the 2048 * 2048 matrix is compared with 4096 * 4096 matrix, but there are definitive differences when the 2048 * 2048 matrix is compared to the 1024 * 1024 and 512 * 512 matrix [23]. Since our system is a 512 * 512 matrix, ob- viously the digital unprocessed and processed chest image can not equal the spatial resolution performance of con- ventional radiography. With higher resolution digitizers there one expect an improvement in diagnostic accuracy because the higher detail, but the 512 by 512 resolution has proved to be sufficient for this preliminary study, in which a filter is being evaluated as a means to enhance detail, as loss in spatial resolution of the digital images is in part compensated by the gain in contrast resolution in the processed images. We are planning to increase the matrix size to 2K by 2K and pixel bit depth to 12 in the next future as to make this filter clinically useful for chest radiography.

Neither spatial resolution is so critical, nor scattered radiation is so intense as referred to mammograms. A conventional "normal" mammogram is shown in Fig. 6(a). Due to the excessive radiographic density of the film, it is difficult to classify it into one of the Wolfe four groups (91. It would be very important to include such mammo- gram into a specific group because breast parenchymal patterns are a risk factor for carcinoma of the breast [9]. Fig. 6(a) shows the result of appling our technique to the image shown in Fig. 6(b). The image processing allowed us to classify this mammogram into DY Wolfe pattern (high cancer risk). Another example is shown in Fig. 7(a) that corresponds to a digital unprocessed mammogram. The result of our algorithm is shown in Fig. 7(b). Details such as nodule edge, ring-like microcalcifications sur- rounding the nodule, tiny and scattered malignant calci- fications, breast skin and trabeculae architecture have been enhanced, and the improvement was again clearly attrib- utable to the facility of digital image manipulation.

Another processing techniques, such as adaptive neigh- borhood image processing, enhanced microcalcifications in mammograms and produces xeromammography-like images, but the computing time is substantial [24], [251. Computer time for our automatic edge-enhancing spatial filtering is less than 1 min, so it could be suitable for rou- tine use, but the efficacy of this technique should await

TAHOCES PI u t . . RADIOGRAPHS BY AUTOMATIC SPATIAL FILTERING 333

Fig. 4. (a) Original umprocessed chest radiograph, (b ) processed image with unsharp-masking technique and 7 * 7 mask size. (c ) processed image with unsharp-masking technique and 25 * 25 mask size (d) processed image with our processing technique. (c) processed image ui th our processing technique and nonlinear stretching contrast.

2 5 0 j4 '1

0 i I " ' I " " I " ' I " "

0 100 200 300 400 POSITION

Fig. 5 . Line plot for a row across the central part of the chest radiograph. The dotted line is from Fig. 4(a). The solid line is from Fig. 4(e).

the completion of a clinical trial which is currently in progress.

IV. CONCLUSION Radiographic imaging of the chest and breast remains

the two of the most important and most challenging prob- lems in radiology today. The wide range of information that results from the tremendous variation of radiation in- tensity behind the lungs compared to behind the medias- tinum in chest radiography, and the narrow exposure lat- itude of film and its compressed dynamic range in mammograms, create very difficult imaging problems.

The introduction and continued investigation on digital techniques may have presented yet a solution to this com- plex and challenging problem. In fact, when digital in- formation is acquired, the possibility of manipulation of the data to enhance the image becomes a real possibility.

We describe a low-cost system which performs in an automatic way digital chest and breast radiography. In this paper we show a new algorithm that enhances edges and

334 IEEE TRANSACTIONS ON MEDICAL IMAGING. VOL. IO. NO 3, SEPTEMBER 1991

(W ‘ Fig 6 (a) Conventional mammgram. @) After spatlal filtering with our

technlqub.

contrast of conventional radiographs while keeping whithin clinical acceptable levels the image noise. This automatic spatial filtering for edge and contrast-enhancing has a potentially valuable application for image process- ing in conventional radiography, which may offer clini- cally significant improvements in diagnostic accuracy. It is suitable for routine use because It is less than one min- ute time consuming.

As in the method developed by Sherrier we have im- plemented an algorithm for processing chest radiographs that enhances substantially edges and contrast in the me- diastinum increasing also that in the lungs without intro- ducing unwanted artifacts. The true utility of this algo- rithm awaits both higher technology (pixel size) imple- mentation and observer-performance evaluation which are currently under way.

As in the method developed by Dhawan, we can pro- duce xeromammography-like images with enhanced edge and contrast to detect mammographic features which are not easily seen in conventional film mammography. The door is open for clinical evaluation in order to know if it could help us to classify MBPP, its utility for penetrating dense areas in breast mammography or if the detection of microcalcifications is increased. These techniques and further development of technology (pixel size) should im-

(b)

Fig. 7 . (a) Conventional mammogram. (b) After spatial filtering.

prove the diagnosis of early breast cancer without increas- ing the radiation dose as digital radiographs (either from digitization of conventional radiographs or direct from a photostimulable phosphor plate), are associated to a sig- nificant lower dosis of radiation as compared to the high- est sensitivity xeromammographs.

The technique as employed in this study is suitable for routine use because as it was said it is less than one minute time consuming, it works in an automatic way, and does not require more radiation dose once the conventional im- age is acquired. Hence, it provides further subjective im- provement in image appearance, and may provide addi- tional benefits in diagnostic accuracy. Our method can be too widely applied to other medical images and general image processing problems.

Beyond this, more advanced algorithms should open conventional radiography to “quantitative” analysis (re- duction spatial noise, computer-aided diagnosis, pattem recognition, function exploration) as radiology advances toward the total electronic department in the future.

APPENDIX

The nonlinear contrast stretching function F = F(k) for displaying the image is automatically performed as fol- lows.

1) We calculate the maximum (MAX) and minimum (MIN) gray level value of the resultant image.

2) A low-pass filtering is applied to the resultant image and a maximum (MAXS) and minimum (MINS) values are calculated for it. Then we find an auxiliary function F ’ = F ’ ( k ) as follows:

TAHOCES el a l . : RADIOGRAPHS BY AUTOMATIC SPATIAL FILTERING 335

BLOW * (K-MIN) /(MINSN-MINN)

0 < K < MINSN

BLOW + (BHI-BLOW) * (K-MINSN) / (MAXN-MAXSN)

F’(k) = \ I

MINSN < K < MAXSN

BHI + (255-BHI) * (K-MAXSN)/

(MAXN-MAXSN)

MAXSN < K < MAXN

Where k gray level

MAXN MAX-MIN

MAXSN = MAXS-MIN

MINSN = MINS-MIN

MINN = 0

BLOW, BHI 6 [0,255].

3) We smooth the F ’ = F’(k) function according to k + l

F(k) = (1 /W) * c F‘(m)

where F gray-level transformation function 111 = k - I

W window size

1 = ( W - 1)/2.

REFERENCES

[ I ] G. A . Johnson and C . E. Ravin, “A survey of digital chest radiog- raphy,” Radiology Clin. North Amer., vol. 21, pp. 655-665, 1983.

[2] R . G. Fraser, C . Sanders, G. T . Barnes. H . MacMahon. M. L. Giger, K. Doi, A. W. Templeton. G. G . Cox, S. J. Dwyer, C . R. B. Mer i t . and J . P. Jones, “Digital imaging of the chest.” Radiology, vol. 171.

[31 L. T. Nicklason, J. A. Sorenson, and J . A . Nelson, “Scattered ra- diation in chest radiography,” Med. Phys.. vol. 8, pp, 677-681. 1981.

141 L. W. Guiss and P. Kuenstler, “A retrospective view of survey pho- tofluorography of persons with lung cancer.” Cancer. vol. 13. pp. 91-95, 1960.

[SI R. H. Sherrier, C . Chiles. W. E. Wilkinson, G . A. Johnson, and C . E. Ravin, “Effects of image processing on nodule detection rates in digitized chest radiographs: ROC study of observer performance.” Radiology, vol. 166, pp. 447-450, 1988.

161 Muhn Jr . , W. E. Miller, R . S . Fontana, Dr. Sanderson, and M. A.

pp. 297-307, 1989.

Uplenhopp, “Lung cancer detected during a screening program using four-month chest radiographs,” Radiology. vol. 148, pp. 609-615. 1983.

[7] H.-P. Chan, K . Doi, S. Galhote, C. J . Vyborny. and H. MacMahon, “Image feature analysis and computer-aided diagnosis in digital ra- diography, Automated detection of microcalcifications in mammog- raphy,” Med. Phys.. vol. 14, pp. 538-548, 1987.

[SI J. N. Wolfe. “Breast parenchymal patterns: Prevalent and incident carcinoma.” Radiology, vol. 131. pp. 267-268. 1979.

191 J . N. Wolfe. S. Albert, S. Belle. M. S. Hyg, and M. Salane. “Breast parenchymal patterns and their relationship to risk for having or de- veloping carcinoma,” Radio/. Clin. North-Amer.. vol. 21, pp. 127- 136. 1983.

[IO] J . E. Martin, Atlas of mammography: Histologic and Mammographic Correlarion, USA: Golden’s Diagnostic Rtrdiology. Baltimore, MD: Williams-Wilkins, 1988, pp. 22-23.

[ I I ] C. Kimme-Smith, L. W. Basset, R. H. Gold, and L. Gormley, “Dig- ital mammography. A comparison of two digitization methods,” In- vest. Radiol., vol. 24, pp. 869-875. 1989.

[ 121 J . N. Wolfe, Amer. J . Roentgenol., vol. 121, p. 846, 1974. [ 131 W. R. Brody. Digital Radiography. New York: Raven, pp. 45-49. 1141 R. C . Gonzalez and P. Wintz, Digital Image Processing. New York:

Addison-Wesley, pp. 16 1 - 165. [I51 R . H. Sherrier and G. A. Johnson, ”Regionally adaptive histogram

equalization of the chest,” IEEE Trans. Med. Imaging. vol. MI-6, pp. 1-7, 1987.

[I61 G. A. Johnson, N. Dankly. and C . E. Ravin, “Processing altema- tives for digital chest imaging,” Radio/. Clin. North Amer., vol. 23, pp. 335-340, 1985.

1171 M. Ishida, P. H. Frank, K. Doi, J . L. Lehr, “High quality digital radiographic images: Improved detection of low-contrast objects and preliminary clinical studies,” RadioCraph., vol. 3 , no. 2, pp. 325- 338, 1983.

[ 181 M. I . Sezan, A. M. Tekalp, and R. Schaetzing, “Automatic anatom- ically selective image enhancement in digital chest radiography,” IEEE Trans. Med. Imaging, vol. 8 , pp. 154-162, 1989.

[ 191 W. D. Foley, C. R. Wilson. and M. San Dretto, “The effect of vary- ing high-pass filter parameters on the detectability of isolated pul- monary nodule in digitized chest images,” Invest. Radiol., vol. 21, pp. 305-310, 1986.

[20] H. P. McAdams, G . A . Johnson. S. A. Suddarth, and C . E. Ravin, “Histogram-directed processing of digital chest images,” Invest. Ra- diol., vol. 21, pp. 253-259. 1986.

[21] M. P. Ekstrom. Digital Image Processing Techniques. New York: Academic. 1984, p. 24.

1221 H. MacMahon. C . J . Vyborny. C . E. Metz, K. Doi, V. Sabeti. and S. L. Solomon, “Digital radiography of subttle pulmonary abnor- malities: An ROC study of the effect of pixel size on observer per- formance.” Radiology, vol. 158. pp. 21-26. 1986.

[23] M. P. Capp. H. Roehrig, G . W. Seeley, and H. D. Fisher. “The digital radiology department of the future. ” Radiol. Clin. North Amer., vol. 23, pp. 349-355, 1985.

[24] A. P. Dhawan, G . Buelloni, and R. Gordon, ”Enhancement of mam- mographic features by optimal adaptive neighborhood image process- ing,” IEEE Trans. Med. Imaging. vol. MI-5, pp. 8-15, 1986.

[25] A. P. Dhawan and E. Le Royer, “Mammographic feature enhance- ment by computerized image processing.” Comput. Methods Pro- grams Biomed.. vol. 27, pp. 23-35, 1988.