2012 4th International Conference on Electronics Computer Technology (ICECT 2012)
A Modified Unsharp Masking Algorithm based on Region Segmentation for Digital Mammography 1
Siddharth, 2Vikrant Bhateja
Department of Electronics and Communication Engineering Shri Ramswaroop Memorial Group of Professional Colleges Lucknow-227105 (U.P.), India. 1
[email protected], 2
[email protected] stage. Mammographic images are limited by poor radiologic resolution, especially in case of patients with denser breasts, prior surgery, previous radiation or breast implants. Inspite of high sensitivity and specificity, even the deadly tumors are missed by mammography. For accurate computer aided detection of this breast tumor, edge enhancement techniques are applied to digital mammograms. Thus, computer assisted diagnostic techniques serve to be an important tool for improving breast cancer detection. These techniques are useful in early detection of breast cancer, thus providing a remarkable reduction in disease mortality [3].Conventional enhancement techniques applicable in the spatial domain are not very effective in case of mammographic images. Histogram based enhancement techniques well preserves the lesion edges but there are losses of details outside the denser parts of images. Unsharp Masking (UM) is a very common technique used to enhance the edges of the breast tumor. Conventional linear UM method [4] is very simple but poses certain disadvantages. The usage of linear high pass filter in the linear UM method makes the system extremely sensitive to noise. Secondly, usage of a global enhancement factor for the entire image leads to over-enhancement of the high contrast areas thereby introducing some undesired artifacts in the finally processed image. Many variants of the conventional UM methods are proposed in the literature to overcome these limitations. Strobel et al. used quadratic operator in place of linear high pass filter [5] to improve the performance of UM method. However, usage of this operator introduces some visible noise depending on the enhancement factor. The properties of quadratic filters were used to generate high order polynomial operators [6] suitable for contrast enhancement using UM method. But to reduce the noise effects, the output of the high pass filter is multiplied by a control signal obtained from the output of an edge sensor. For proper sharpening the output of edge sensor should be very small and it therefore becomes very difficult to achieve such small values as edge sensor output. Mira et al. proposed a normalized non-linear approach [7] which replaced the high pass component of the conventional UM method by a fraction obtained from the quadratic filter. However, this approach amplified the noise in high contrast areas which created a very unpleasant effect in the finally enhanced images. Wang et al. [8] proposed a model of UM using fuzzy implementation, but some visible noise and breakpoints were observed in the enhanced images. Xiao et al. combined UM with the histogram equalization (HE) [9]
Abstract—A mass is a three-dimensional lesion that occupies a space within the breast. Masses can be seen in both mediolateral and craniocaudal mammographic views. This paper introduces a modified unsharp masking (UM) algorithm using an improved high pass filter. The proposed algorithm combines the conventional UM algorithm with the region segmentation approach. The conventional UM algorithm is extremely sensitive to noise because of the presence of the linear high pass filter, on the other hand region segmentation does not prove effective when dealing with the objects having multiple discontinuities. The improved high pass filter used in the proposed work provides high frequency components of the image which are insensitive to noise. Simulation results show that the proposed algorithm not only enhances the edges of the masses, but at the same time suppresses the background noise as well. Keyword s- Breast Cancer, Mammographic Masses, Unsharp Masking (UM), Region Segmentation.
I.
INTRODUCTION
Breast Cancer is the most common cancer among women (after skin cancer). According to the report of American Cancer Society, breast cancer accounts for nearly 1 in 4 cancers diagnosed in US women [1]. This cancer may be invasive if it has spread from the milk duct or lobule to healthy breast tissues. A mass is a three-dimensional lesion that occupies a space within the breast. Development of mass is an outcome of various internal processes affecting the breast tissues in different ways. A detected mass always needs further investigation unless it is classified one among the well-defined types. A round, oval, or lobulated mass with sharply defined borders has a high likelihood of being benign. Masses can be broadly classified as circumscribed and spiculated, circumscribed masses grow faster than spiculated masses. A mammographic mass is said to be circumscribed, if it is a circular or oval lesion with convex margins and invariably homogeneous density. There exists a clear demarcation between the lesion boundaries and the surrounding tissues. Spiculated masses are characterized by lines radiating from the lesions’ margins. Masses with irregular boundaries are generally malignant [2]. For early diagnosis of breast cancer mammography screening is a method that has proven to be useful. Mammography is capable of detecting and locating underlying tumors which are non-palpable in nature that may be cancerous at a later
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2012 4th International Conference on Electronics Computer Technology (ICECT 2012)
leading to the normalization of the grayscales of the entire image including the low as well as high contrast areas. Because of the global treatment of the image with HE approach, this method does not yield satisfactory results for enhancement of the details of the mammographic masses. The cubic UM approach [10] was suggested by Ramponi et al. which uses a quadratic function of local gradient to suppress noise. This filter works well in some areas but may introduce some visible noise depending on the choice of enhancement factor. Adaptive UM method proposed by Polosel et al. [11] described two filters in the horizontal and vertical directions respectively and the enhancement factor is determined according to the local details of the image. As the complexity of this method is very high, therefore it cannot be applied for the edge enhancement of mammographic images. Yang et al. proposed a UM method based on region segmentation [12] to overcome the drawback of adaptive UM method [11]. However, the authors have used conventional high pass filter, hence it is not able to enhance the details and the lesion edges in region of interest (ROI) effectively. In addition to this, some over shoots are also present because of high enhancement factors. Wu et. al. proposed an improved unsharp mask method [13], but some overshoot artifacts can still be seen and the edges are also not clearly visible. This paper introduces a modified unsharp masking (UM) algorithm based on region segmentation for digital mammograms. The present work proposes a new 5x5 template to improve the performance of the high pass filter, thus providing high frequency components of the targeted ROI which are insensitive to noise. Combination of this high pass filter with the UM based on region segmentation, not only enhances the contrast of the lesion details and edges but also suppresses the background noise. The rest of the paper is organized as follows: Section II discusses the UM based on region segmentation method in the first half. The latter half of this section describes the proposed work. Results and discussions are covered in Section III, while the concluding remarks are given under Section IV. II.
pixel to any one of these three segments, a local variance vi is computed over a 3x3 pixel block using the formula: 1
⎛ m +1
n +1
⎞
(1)
2 vi ( m, n ) = ⎜ ∑ ∑ ( y (i , j ) − y ( m,n )) ⎟ ⎟ 9 ⎜ i = m −1 j = n −1 ⎝ ⎠
where: y ( m, n) is the average luminance level of the 3x3 pixel block. Let δ1 and δ2 be the two threshold values (chosen to divide the image in to three segments) such that both the values should be greater than zero and δ1 should TABLE I. CONDITIONS FOR SEGMENTATION OF PIXELS. νi(m,n)
Region
β(x,y)
Less than δ1
Low-detail
1
Between δ1 and δ2
Medium-detail
γ h (> 1)
Greater than δ2
High-detail
γl (1< γl < γ h )
γ h and γl
represent the upper and lower limits of the enhancement factor.
lie between zero and δ2. The conditions which a pixel must satisfy to belong to a respective region are listed under table I. The conventional linear equation for UM can be modified and given as : (2) e( x, y ) = h( x, y ) + β ( x, y ) f ( x, y ) where: e(x, y) is the final enhanced image, h(x, y) is the original image and f(x, y) is the output of the linear high pass filter. B. Modified UM based on Region Segmentation Method UM based on region segmentation [12] uses a selective mechanism for making the choice of the enhancement factor for every pixel of the image. This should enhance the image effectively but the high pass filter used, is the conventional one. Thus, it is unable to effectively enhance the morphological details, along with the introduction of noise. The new convolution template proposed in this work effectively improves the high pass, thereby modifying the sharpening effect of the UM algorithm. The new high pass filter produces the high pass filtered image by subtracting the low pass filtered image from the original image. The background prediction process is achieved using the lowpass filter and then this image is subtracted from the original input image producing a high frequency image in which the background information is suppressed. The expression for the improved high pass filter can be given as:
PROPOSED UNSHARP MASKING ALGORITHM
A. UM Based on Region Segmentation Segmentation is the process of partitioning a digital image into multiple segments such that the set of segments cover the entire image. In region segmentation approach, the pixels corresponding to an object are grouped together and marked; it then compares the gray level variance of the pixels for segmentation and at the same time it assumes that points on the same object will project to spatially close pixels on the image. This assumption does not hold true for all the cases, thus this method fails to divide the regions efficiently for objects with multiple discontinuities. UM based on region segmentation [12] works on the principle of dividing the entire image into three different segments according to their characteristics. These three segments are low-detail, medium-detail and high-detail regions correspond to low, medium and high frequency regions respectively. To assign a
'
f ( x, y ) = h ( x, y ) − l ( x, y ) '
(3)
where: f ( x, y ) is the high pass filtered image, h(x, y) is the input image and l(x, y) is the image containing the background information obtained from a low pass filter. The low pass filtering and the background prediction is performed by convolution of the proposed template C(m, n) with the original image h(x, y) is defined as: (4) l ( x , y ) = h ( x , y ) ∗ C ( m, n ) C(m, n) is the 5x5 convolution template expressed as :
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2012 4th International Conference on Electronics Computer Technology (ICECT 2012)
⎡a ⎢ ⎢a 1 ⎢ C ( m, n ) = a 60 a ⎢ ⎢a ⎢ ⎢⎣ a
a
a
a
4 a 3a 4 a 3a 0 3a 4a
3a
4a
a
a
a
a⎤ ⎥ a⎥ ⎥ a⎥ a⎥ ⎥ a ⎥⎦
The images used in this work are mbd005 (containing a benign circumscribed mass embedded in a background of fatty breast tissues) and mdb132 (malignant circumscribed mass in a fatty background) respectively.
(5)
B. Simulation Results In this work, a pre-processing operation is performed on the input mammograms. This involves ROI extraction, where a particular section of the mammographic image is cropped, from the probable area of lesion location and then normalized. The test images include ROI of size 256 x 256 pixels extracted from mammograms containing circumscribed masses which may be benign or malignant. The proposed UM method is then applied to the preprocessed ROI. For the purpose of comparison, the results of enhanced ROI produced by linear UM method [4], UM based on region segmentation [12] employing low and high enhancement factor are also shown. Fig 2 and Fig 3 show the results of enhanced ROI produced by proposed as well as other UM methods. ROI produced by Linear UM method [4] (as in fig. 2(b) and 3(b) respectively) are generated using a constant enhancement factor equal to 3. In UM based on region segmentation [12], ROI are partitioned with thresholds values of δ1=80 and δ2=120 respectively. For region segmentation with high enhancement factor, chosen values of γ l and γ h are 7 and 9 respectively, whereas for low enhancement factor they are 3 and 5 respectively. In the proposed UM method, a=4 is used for the filter template (5). Region segmentation method is then applied with low enhancement factors.
where: a can take whole numbers between 4 and 8. Now, '
substituting the output of the high pass filter f ( x, y ) , in (2), the modified expression for the UM method based on region segmentation can be stated as: '
(6) e( x, y ) = h( x, y ) + β ( x, y ) f ( x, y ) The proposed method in (6) is applied to the ROI with low enhancement factor, in order to effectively extrude the edges of mammographic masses. The block diagram representation of the proposed UM algorithm is shown in figure 1. Input image
h(x,y)
Improved High Pass Filtered Image f’(x,y)
l(x,y) Low Pass filtering Using Proposed Template (C(m,n))
Multiplication by Enhancement factor (β(x,y))
β(x,y) f’(x,y)
h(x,y)
Final Enhanced Image (e(x,y) ) Fig 1: Block Diagram of the working of the Proposed Unsharp masking technique.
III.
C. Evaluation Method Used The degree of contrast or edge enhancement provided by any particular algorithm for mammographic images is adjudged by its ability to enhance the difference between the average gray scale values lying in the in the background and the foreground regions. S. Singh and K. Bovis proposed three different quantitative measures [15] for evaluation of the enhanced image quality. Combined Enhancement Measure (CEM) is used as an evaluation parameter for the ROI processed by different algorithms. It combines DSM, TBCe and TBCs for a particular algorithm by representing each value within a three dimensional Euclidean space. The algorithm giving the smallest value of CEM is selected as the best enhancement algorithm for ROI. These parameters can be calculated as under: E E O) DSM = ( μT − μ B ) − ( μTO − μ B E O O ⎫ ⎧ E ⎪ μT μ B − μT μ B ⎪ TBCs = ⎨ ⎬ ⎪ ⎪ σTE σ TO ⎩ ⎭ ⎧ μ E μ E − μO μO ⎫ ⎪ T B T B ⎪ TBCe = ⎨ ⎬ E O ⎪ ⎪ ε T εT ⎩ ⎭
RESULTS AND DISCUSSION
A. Database of Digital Mammograms The images used in this paper for simulations are taken from the Mammographic Image Analysis Society (MIAS) database [14]. It is publically available and one of the most easily accessed databases. MIAS is an organization of UK research groups interested in the understanding of mammograms and has generated a database of 322 digital mammograms. The original images (which are digitized at 50 micron pixel edge) are rescaled to 200 micron pixel edge and padded so that all the images are of 1024x1024 pixels.
(7)
(
)(
)
(8)
(
)(
)
(9)
2
2
CEM = (1 − DSM ) + (1 − TBC s ) + (1 − TBCe )
2
(10)
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2012 4th International Conference on Electronics Computer Technology (ICECT 2012)
(a)
(b)
(c)
(d)
(c)
(e) Figure 3: (a) Pre-processed ROI (mdb005) containing a circumscribed mass. Enhanced ROI obtained using (b) Linear UM [4]. (c) UM based on region segmentation with high enhancement factor [12]. (d) UM based on region segmentation with low enhancement factor [12]. (e) Proposed UM algorithm.
D. Comparison of Results It can be observed from the results obtained in table II that the Linear UM method [4] enhances the contrast of the edges but there are many overshoots present in the enhanced image and noise is enhanced as well, yielding a high value of CEM. UM based on region segmentation [12] yields lower values of CEM in comparison to linear UM [4]. It can be observed in fig. 2(c) and (d) that region segmentation method provides some overshoots in case of high enhancement factors whereas these are not present for low enhancement factor. However, some background noise can still be seen for both high and low values of enhancement factor in region segmentation. The proposed UM method (as shown in fig. 2 (e) and 3(e)) produces the ROI with enhanced edges along with due suppression of background noise, thereby yielding the lowest value of CEM in comparison to other algorithms. The results are even better as compared to the recent proposed UM method by Wu et al. [13].
(e) Figure 2: (a) Pre-processed ROI (mdb132) containing a circumscribed mass. Enhanced ROI obtained using (b) Linear UM [4]. (c) UM based on region segmentation with high enhancement factor [12]. (d) UM based on region segmentation with low enhancement factor [12]. (e) Proposed UM algorithm.
O O O O where: μ B , μT , σ T , εT are the mean, standard deviation and entropy of the gray scales comprising the background and the target area, of the original image before E E E E enhancement whereas, μ B , μT , σ T , ε T are the mean , standard deviation of the gray scales after enhancement. CEM values of the digital mammograms processed by various algorithms are enlisted under table II.
(a)
(d)
IV.
CONCLUSION
A novel algorithm for contrast as well as edge enhancement of mammographic masses is presented in this work. Conventional UM algorithms are limited because of being extremely sensitive to noise and also there are many overshoots present in the finally enhanced images. This happens because of the presence of the linear high pass filter. On the other hand UM based on region segmentation does not prove effective when dealing with the objects having multiple discontinuities. This paper proposes a modified UM algorithm for image enhancement based on region segmentation. It uses an improved high pass filter template to improve the performance of the high pass filter, and
(b)
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2012 4th International Conference on Electronics Computer Technology (ICECT 2012)
combines the result with the region segmentation algorithm. Experimental results shown in this paper demonstrate that the modified approach not only suppresses the noise in the background areas, but also enhances the edges of the lesions very effectively. Results obtained with the proposed UM algorithm are superior in comparison to other UM algorithms both qualitatively as well quantitatively; thus, contributing to better visualization of mammographic masses. The resulting enhanced image could further serve to accomplish better segmentation of masses and their classification. TABLE II: VALUES OF EVALUATION PARAMETERS OF DIGITAL
[4]
[5]
[6]
[7]
MAMMOGRAMS PROCESSED BY VARIOUS ALGORITHMS. Algorithm ROI DSM TBCs TBCe CEM
[8]
mdb005
0.0602
0.1098
0.1248
1.5626
mdb132
0.0582
0.1120
0.1360
1.5506
RS with high enhancement factors [12]
mdb005
0.0740
0.1028
0.1503
1.5442
mdb132
0.0820
0.0712
0.2682
1.4969
[10]
RS with low enhancement factors [12]
mdb005
0.0940
0.0905
0.2799
1.4719
[11]
mdb132
0.0804
0.1106
0.1603
1.5302
mdb005
0.1904
0.1967
0.9340
1.1424
mdb132
0.1782
0.1727
0.9240
1.1685
mdb132
0.1628
0.1624
0.7230
1.2162
Linear UM [4]
[9]
[12] Proposed Method (a=4)
[13]
[14]
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[15]
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J Rogowska, K Preston and D Sashin, “Evaluation of digital unsharp masking and local contrast stretching as applied to chest radiology,” IEEE Transactions on Biomedical Engineering, vol.35, issue 10, pp 817-827, Oct 1988. N.Strobel, “Quadratic Filters for Image Contrast enhancement,” Dept. of Electrical Engineering, University of California, Santa Barbara,June 1994. G.Ramponi, N.Stroble,S.K.Mitra and T.Yu, “Nonlinear Unsharp Masking methods for image contrast enhancement,” Electron Image , vol. 5, pp. 353-366, July 1996. T.H.Yu and S.K.Mitra, “Unsharp masking with monlinear filters,” in Proc. of seventh European Signal processing conf., EUSIPCO-94, Edinburgh, Scotland, September, 1994, pp. 1485-1488. Yanxia Wang, Qiuqi Ruan,” An Improved Unsharp Masking method for Palmprint Image Enhancement”, in proceedings of the First International Conference on Innovative Computing, Information and Control (ICICIC'06),Oct 2006 . X.Xiao and X.Zhang, “An improved unsharp maskingmethod for borehole image enhancement,” in proc. of second International Conference on Industrial Mechatronics and Automation, pp. 349-352, May 2010. G.Ramponi, ”A cubic unsharp masking technique for contrast enhancement,”Signal Process., vol. 67, pp 211-222, June 1998. A.Polosel, G.Ramponi, and V.John Mathews, “Image Enhancement Via Adaptive Unsharp Masking,” IEEE Transactions on Image Processing, vol. 9, pp. 505-510, Mar 2000. Y.B.Yang, H.B.Shang, G.c.Jia and L.Q.huang, ”Adaptive unsharp masking method based on region segmentation,” Optics and Precision Engineering, vol. 11, pp. 188-191,Apr 2003 (in Chinese ). Z. Wu, J. Yuan, B. Lv and X. Zheng, “Digital mammography image enhancement using improved unsharp masking approach,” in Proc. of 3rd International Conference on Image and Signal Processing, pp. 668-671, Dec 2010. J. Suckling, et al., “The Mammographic Image Analysis Society Mammogram Database,” Proc. 2nd Int. Workshop Digital Mammography, York, U.K. , pp. 375–378, 1994.S. Singh, K. Bovis “An evaluation of contrast enhancement techniques for mammographic breast masses.” IEEE Transactions on Information Technology in Biomedicine, 2005, vol. 9, pp 109-119. S. Singh, K. Bovis, "An Evaluation of Contrast Enhancement Techniques for Mammographic Breast Masses," IEEE Transactions on Information Technology in Biomedicine, vol. 9, no.1, pp.109-119, 2005.
