Developing a Multi-Stage CAD Technique for Robust

JAUES Al Azhar University Journal, ISSN 1110-6409, Vol. 5, No. 15, Cairo, Egypt, April 2010.

Developing a Multi-Stage CAD Technique for Robust Auto Detection of Calcification in Digital Mammogram Hussien Ahmed Konber1, Ashraf Aboshosha2, M. Ragaa1 1

Department of Communications, Faculty of Engineering, Al-Azhar University, Nasr City, Cairo, Egypt 2 National Research and Technology Center, Atomic Energy Authority, Nasr City, Cairo, Egypt Abstract: This paper presents multi-stage automatic technique to satisfy the needs early detection of breast classification. We focused on the preprocessing phase to improve the capability of detecting the calcification. We developed a universal filter which is capable of removing all types of noise. Extraction of the breast region and delineation of the breast contour is an essential step in our technique. Primarily it allows the search for abnormalities to be limited to the region of the breast without undue influence from the background of the mammogram. Moreover, the results of the auto detection of the classification have been presented in 3D model to enable the physician to track the anomalism simply. We employ a precise segmentation technique to suppress artifacts and accentuate the breast region, followed by a detection algorithm to classify the breast tissue region. To demonstrate the capability of our multi-stage computer aided design (CAD) system it is extensively tested on mammograms from the Digital Database for Screening Mammography “DDSM” of the University of South Florida.

1. Introduction Breast cancer is one of the major causes of mortality increase to middle-aged women, especially in developed countries. Doctors cannot recognize early breast cancer due to the properties of human eye. Auto detection of calcification in digital mammogram can satisfy the early detection which is the key to improve prognosis of breast cancer. Currently, it is well known that mammography is the most effective method for early detection of breast cancer. However, it is very difficult to interpret the X-ray mammograms because of the small differences in image density of various breast tissues, in particular, for dense breasts. A possible sign of breast cancer is the appearance of clustered microcalcifications whose individual particles are usually under 0.5 mm in diameter with irregular and hetero-geneous shape. Individual microcalcifications are difficult to be detected because they are variable in shape as well as in size and may be embedded in areas of dense parencymal tissues. Therefore, careful diagnosis should be performed for the clustered microcalcifications that may herald an early-stage cancer. To improve the visibility of mammographic lesions on a computer monitor, image enhancement methods for digitized mammograms have been attempted by several

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JAUES Al Azhar University Journal, ISSN 1110-6409, Vol. 5, No. 15, Cairo, Egypt, April 2010.

researchers. Emphasizing image details and suppressing noises usually perform image enhancement [1] [2] [3]. Throughout this framework we develop multi-stage auto detection system used to identify the classification in digital mammogram. The first stage is pre-processing of digital mammogram images where the image is improved by which the anomalism could be detected easily. The second stage is the segmentation where the region of interest (ROI), the artifacts, breast layers and the breast direction are identified. The third phase is the mass detection where the wavelets, edge detection and template matching have been applied to identify the cancerous cells exactly. The remainder of this paper is organized as follows; Section 2 shows the breast structure including the digital mammogram. Section 3 presents preprocessing stage including the median filtering, normalization, histogram equalization. Section 4 focuses on the segmentation with a special emphasis on the removal of artifact, detecting the region of interest and extracting of the layers. Section 5 illustrates the mass detection based on wavelets in digital mammogram beside representing the template matching results on the 3D model. Finally, the conclusion is presented in section 6. 2. Digital Mammogram: Mammogram is an X-ray imaging technique to examine the breast. As any imaging technique in order to examine the breast, an X-ray beam is passed through the tissue to record the variations in amounts of radiation that are absorbed. Where the differences exist in tissues in the breast absorb different amounts of radiation, it is possible to distinguish features and details about the tissues being examined. In screening mammography each breast is compressed into a relatively flat surface. Then an X-ray source on one side of the breast emits radiation through the breast. On the other side of the breast the radiation is recorded [4]. Denser tissues in the breast display brighter intensities in the mammogram images. Muscles, fibro-glandular tissue, malignant with benign masses, and vascular tissue appear brighter while areas containing fat or skin appear darker. The mammogram image taken to the breast can be segmented into four regions they are; the background, the tissue “subcutaneous fat layer”, muscles and breast composition which contain the component of mammogram and that region where the cancer could be located “region of interest (ROI)”. That segmentation is accomplished according to the radiation recorded of the breast where the background hasn’t been affected by the beam. The tissue has a little absorbing of the X-ray where it appears brighter than background and the muscles are the brightest in the mammogram, see figure1.

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JAUES Al Azhar University Journal, ISSN 1110-6409, Vol. 5, No. 15, Cairo, Egypt, April 2010.

Figure 1: Structure of the breast in the mammogram DDSM case “A_1178_1.LEFT_CC”

The malignant and benign masses exist in this area with different shape, density and size where all these details have different absorption of radiation recorded by x-ray. The processing of digital mammogram has several steps to finally detect the malignant in mammogram. 3. Preprocessing of digital Mammogram Image enhancement refers to attenuation, or sharpening, of image features such as edges, boundaries, or contrast to make the processed image more useful for analysis. Image enhancement includes gray-level and contrast manipulation, noise reduction, background removal, edge crisping and sharpening, filtering, interpolation and magnification, pseudocoloring, and so on. [12] 3.1 Median Filtering Median filtering is used to remove the high frequency components in the mammogram image, where it is used to remove the noise without disturbing the edges. For each pixel a window of neighborhood pixels are extracted, and the median value is calculated for that window. The intensity value of the center pixel value is replaced with the median value. This procedure is done for all the pixels in the image to smoothen the mammogram image [2]. The window size that we use it in median filtering for enhancing the image by removing the noise from the image give us another benefit that it strength the relation between pixels in the same window and that causing more strength layer that we will use this relation in these layer in segmentation of mammogram as we will see. 3.2 The Universal Denoising Filter The proposed filter is a cascaded spatial filter based on median fitter and Coiflet Wavelets. Its edge-preserving nature makes it useful in cases where edge blurring is undesirable. This filter is the best one for removing all types of noise as shown from the results. The most important task of this article is to determine the best spatial filter applied to remove a certain type of noise. Objectively we used two important similarity

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JAUES Al Azhar University Journal, ISSN 1110-6409, Vol. 5, No. 15, Cairo, Egypt, April 2010.

measures to match the restored image after denoising with the original one. The first one is the 2D cross correlation while the second is the peak signal to noise ratio (PSNR) [13], [14].

Figure 2: The proposed filter

A. 2D cross correlation The 2D cross correlation is applied to match the original image with restored image. The maximum correlation defines the fittest one. 2D cross correlation is a standard method of estimating the degree to which two images are similar. Consider two images xi and yi where i=1,2... ,n are its pixels. The 2D cross correlation  is represented by equation (1). i n

   [ x i  m x  *  y i  m y ] i 1

i n

x i 1

i

 mx 

2

i n

 y i 1

i

 my 

(1)

2

Where mx and my are the means of the corresponding image. Table (1) shows the similarity measure between the original image and the restored one after applying different digital filters in presence of different types of noise based on the 2D cross correlation. B. Peak Signal to Noise Ratio Peak Signal to Noise Ratio PSNR is used to measure the difference between two images. Mathematically it is defined as;   PSNR  10 log10   1  MN 

   M N 2  I 1 i , j   I 2 i , j    i 1 j 1  B2

(2)

Where B is the largest possible value of the signal (typically 255 or 1), where I1(i,j) and I2(i,j )denote the pixel values of the restored image and the original image, respectively. 2 1 M N I 1  i , j   I 2  i , j   is the mean square difference between two images. The   MN i 1 j 1 PSNR is given in decibel units (dB), which measure the ratio of the peak signal and the difference between two images. An increase of 20 dB corresponds to a ten-fold decrease in the RMS difference between two images. There are many versions of signal-to-noise ratios, but the PSNR is very common in image processing, probably because it gives better-sounding numbers than other measures. The results of the PSNR are presented in table (2). Table 1. 2D cross correlation similarity measure Median Adaptive Gaussian Proposed Salt/pepper 0.6983 0.9809 0.7804 0.9984 Gaussian 0.9446 0.9701 0.9701 0.9876 Poisson 0.9900 0.9901 0.9913 0.9961 Speckle 0.7737 0.8341 0.8547 0.9871

Salt/pepper Gaussian Poisson Speckle

Table 2. PSNR similarity measure Median Adaptive Gaussian 16.08 dB 28.81 dB 18.08 dB 21.87 dB 23.43 dB 23.60 dB 30.19 dB 31.92 dB 31.97 dB 25.32 dB 26.67 dB 26.73 dB

Proposed 49.48 dB 32.80 dB 43.16 dB 37.67 dB

3.3 Normalization Mammograms are digitalized to gray level images. However, the pixel values do not cover the whole histogram [12]. That cause several difficulties for the diagnosis like the global appearance, brightness, contrast of the breasts may differ, usually due to variations in the recording procedure. This problem can be avoided using normalization method. In

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JAUES Al Azhar University Journal, ISSN 1110-6409, Vol. 5, No. 15, Cairo, Egypt, April 2010.

order to reduce the variation, and achieve computational consistency, the images are normalized, by mapping all mammograms into a fixed intensities range xmin and xmax. Concerning each pixel value i(m,n), the new value x(m,n) is defined mathematically as follows: x  xmin x(m, n)  [i (m, n)  imin ]  max  xmin (3) imax  imin imin is the minimum pixel value in the original image; imax is the maximum pixel value in the original image; xmin is the minimum pixel value in the normalized image; xmax is the maximum pixel value in the normalized image. [12]

3.4 Histogram The histogram of a digital mammogram image with gray levels in the range [0, L-1] is a discrete function h(rk )  nk , where rk is the kth gray level and nk is the number of pixels in the image having gray level rk. It is common practice to normalize a histogram by dividing each of its values by the total number of pixels in the image, denoted by n. Thus, a normalized histogram is given by p(rk)=nk/n, for k=0, 1,….. ,L-1, where p(rk) gives an estimate of the probability of occurrence of gray level rk and the sum of all components of a normalized histogram is equal to 1. The gray levels in an image may be viewed as random variables in the interval [0, 1]. The probability of occurrence of gray level rk in an image is approximated by n pr (rk )  k k=0,1,2,.....,L-1 (4) n

Where the plot of pr(rk) versus rk is called a histogram. [8] [9]. The histogram of a digital mammogram image is presented in figure 3

Figure 3: The original mammogram case No “A_1520_1.LEFT_MLO” and its corresponding histogram. 4. Mammogram Segmentation and Removal of Image Artifacts: The first stage we will segment the image to two parts background and all mammogram with artifact where they will be in the same intensity but different size and after that we can deal with the artifact as a cell has number of pixels lower than the mammogram so it

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JAUES Al Azhar University Journal, ISSN 1110-6409, Vol. 5, No. 15, Cairo, Egypt, April 2010.

can be detected and removed by morphological operation. The way of segmentation in this step is the differences between pixels and how the intensity in the image varying. The background is in between 0-r1 and begin to increase until it reach to r2 and in between this two intensity the variance may increase by delta d or more than it or decrease by negative delta or lower than it or there is no variance or the variance is lower than delta or bigger than negative delta as we consider the delta is a certain change. So we can organize them into three case as the pixel in the mammogram is i(m,n), and the variance between pixels in the same raw is d and where we enhance the mammogram with median filter by specific window size it will be enough relation in the rows as the result will be fine as we will see [10] [7]. So the begin of segmentation will be by scanning the mammogram according to the next cases. 0  i(m, n)  r1 1  i (m, n)  r 2  x(m, n)  i (m, n  1)  d  i(m, n)  i (m, n  1)  d (5) 1  i(m, n)  i (m, n  1)  d  0  i( m, n)  i (m, n  1)  d i (m,1)  0 In our segmentation process we will scan the image and compare every pixel to its neighbor and the decision will be according to the previous equation. Considering that i(m,1)=0 as reference for all other pixels i(m,n). Figure 4 shows the result of segmentation of the mammogram with artifact removal while figure 5 presents the edge detection.

(a)

(b)

(c)

(d)

Figure 4: The original mammogram case NO “A_1178_1.LEFT_CC” and the corresponding segmentation and a) The original mammogram b) The mask for mammogram and artifact c) The mask for mammogram after removing the artifact d) the mammogram without artifact

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JAUES Al Azhar University Journal, ISSN 1110-6409, Vol. 5, No. 15, Cairo, Egypt, April 2010.

Figure 5: Edge detection of the mammogram after removing artifact The produced image has now the main component of the mammogram and ready to another processing to extract the region of interest “ROI” where by the same method we can begin in detection of the muscle and extract the ROI. 4.1. Detection of region of interest (ROI) We could evaluate our segmentation process that depending on the pixel relation by counting the summation of the pixels that agree the relation and give its percent with the whole mammogram. After applying the artifact removing and reprocessing the quality of mammogram segmentation reached 98.2 where the pixel of these percentage are strongly correlated to each other. By counting the pixels that has relation with other pixels around here where every pixel doesn’t increase or decrease from the four pixel around here as soon as by delta value and the found percentage is per=98.2 %. m ,n

per 

 r (m, n)

100% mn   d  i (m, n)  i (m  1, n)  d Where   1   d  i (m, n)  i (m  1, n)  d   r (m, n)    d  i (m, n)  i (m, n  1)  d   d  i (m, n)i (m, n  1)  d  0  otherwise m ,n

Figure 6 shows the mammogram without artifact and the corresponding histogram.

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(6)

JAUES Al Azhar University Journal, ISSN 1110-6409, Vol. 5, No. 15, Cairo, Egypt, April 2010.

Figure 6: The mammogram without artifact and the corresponding histogram We can separate between mammogram parts by construct window size and begin to counting the pixels that has relation with each other and categorize them into four windows. Figure 7 shows the segmentation of the mammogram and the possible layer in tissue according to the window chosen.

(a)

(b)

(c)

Figure 7: Edge detection and the composition layer inside the breast

4.2. Layers Extraction and Nipple Localization The segmentation of tissue gives us the second benefit that it locates the nipple inside the mammogram where in some cases the nipple doesn’t appear in the edge detection of the image, where it is important to locate the nipple inside the image, See figure 8.

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JAUES Al Azhar University Journal, ISSN 1110-6409, Vol. 5, No. 15, Cairo, Egypt, April 2010.

a) The original mammogram

b) Mammogram after enhancing

C) Mammogram edge detection

d) Mammogram edge detection and the first layer inside the mammogram show the nipple

e) Mammogram edge detection and the second layer inside the mammogram show the nipple

Figure 8: The composition layer inside the mammogram enhance the nipple from this prospective we can locate the ROI and begin in detection the calcification to locate the cancer from the suspicious regions.

5. Wavelets based Mammogram Mass Detection Wavelet analysis is an extremely powerful data representation method that allows the separation of images into frequency bands without affecting the spatial locality [13]. Wavelet transform analyze different frequencies of a signal using different scales. High frequencies are analyzed using low scales and low frequencies are analyzed in high scales. We experimented with the aforementioned types of wavelets aiming at incrementing the percentage of energy that corresponds to the horizontal, vertical and diagonal details of the third level of wavelet transform Asymmetric Daubechies 8 is the most appropriate to be chosen, when considering mammogram enhancement, since the respective energy is far higher, compared to the other wavelets [15]. Asymmetric Daubechies’ wavelets were proven to be the most promising wavelets. This choice is preferable since the Least Asymmetric Daubechies’ wavelets have finite length and are nearly symmetric, see figure 8. Due to these features, they can achieve high correlation with the clustered microcalcifications, and, therefore, they can effectively enhance microcalcifications. [16]

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JAUES Al Azhar University Journal, ISSN 1110-6409, Vol. 5, No. 15, Cairo, Egypt, April 2010.

Figure 9.a: Wavelets decomposition

Figure 9.b: Wavelet decomposition applied to the digital mammogram

5.1 Sobel based Edge Detection To distinguish the calcifications from the breast tissues we apply a threshold to the detail coefficients for each level from 1 to 3. This can be accomplished by hard-thresholding, which means setting to zero the elements whose absolute values are lower than the threshold, or by edge detection like Sobel gradient operator. This method is based on compute the gradient of each pixel in the image, in order to detect the direction in which the change in the pixels intensity is bigger. This technique allows defining if the pixel belongs to an image's border or an image's homogeneous region based on a defined threshold. [17] The Sobel operator has distinct advantages, although it is slightly more complex than other gradient methods. It is less sensitive to isolated high intensity point variations since the local averaging over sets of three pixels tends to reduce this. In effect it is a edge detector, rather than a point detector. Secondly, it gives an estimate of edge direction as well as edge magnitude at a point which is more informative and is of considerable use in later processing. The Sobel operator is an edge detection operator, so the summary of all the elements in the template is 0. By choosing a suitable threshold, the results of using the normal Sobel operator are binarized to two-valued images. Figure 9 shows the result of processing the enhanced image by wavelet and Sobel edge detection. After rescanning the image in specified region of interest the result finally will be the calcification inside the ROI area.

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JAUES Al Azhar University Journal, ISSN 1110-6409, Vol. 5, No. 15, Cairo, Egypt, April 2010.

Figure 10.a: Sobel edge detection

Figure 10.b: Sobel edge detection with suspicion region

5.2 Template matching After region of interest (ROI) was identified using our segmentation process. Then, a mass template was used to categorize the ROI as true masses or non-masses based on their morphologies. Each pixel of a ROI was scanned with a mass template to determine whether there was a shape (part of a ROI) similar to the mass in the template. The similarity was controlled using two thresholds. If a shape was detected, then the coordinates of the shape were recorded as part of a true mass. While masses are thicker and more circular, other structures tend to be thinner and longer. Therefore, to distinguish the masses from normal structures based on their morphologies, a circle mass template with different diameter in range 10 to 30 pixels was used [11]. Figure 10 shows the result of auto detection by template matching and the final detection on the original mammogram.

Figure 11.a: Auto detection of calcification Figure 11.b: Marking the suspicion region 5.3 3D mammogram representation We will present the correspondence of mammogram X-ray images where mammogram is the common modality used for breast cancer screening and a 2D image is generated of

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JAUES Al Azhar University Journal, ISSN 1110-6409, Vol. 5, No. 15, Cairo, Egypt, April 2010.

the compressed breast. It provides accurate morphological information which allows visualisation of abnormalities which are too small to detect in physical examinations. The images provide both morphological tissue distribution and functional dynamic studies information and are used to provide a specific diagnosis. The 3D image can be obtained from 2D image and it represents the static and dynamic mammogram distribution. We present the mammogram statistics after and before image enhancement to show the suspicious region inside the breast and the similarity between the part of cancer existence and muscles or nipples. We use our template to detect linear structures in both images and the main aim is to extract characteristic points of linear structures determined by template matching. The correlation is closely related to convolution where in correlation, the value of an output pixel is also computed as a weighted sum of neighboring pixels. The difference is that the matrix of weights, in this case called the correlation kernel, and our kernel is the template. Their orientation and features are then represented in 3D matrix structure image. Figure 12 shows the correlation without enhancement while figure13 shows the correlation after the enhancement and finally figure 14 shows the correlation after removing the muscles and the nipple from the ROI.

Figure 12: (a) The 3D image represent the mammogram structure

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(b) Original mammogram without label and back ground

JAUES Al Azhar University Journal, ISSN 1110-6409, Vol. 5, No. 15, Cairo, Egypt, April 2010.

Figure 13: (a) The 3D image represent the mammogram structure after enhances and shows three suspicious areas

(b) The enhance mammogram

Figure 14: (a) The 3D image represent the cancer detection inside the mammogram.

(b) The detected cancer inside the mammogram.

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JAUES Al Azhar University Journal, ISSN 1110-6409, Vol. 5, No. 15, Cairo, Egypt, April 2010.

6. Conclusion Throughout this research work we applied multi-stage CAD technique to detect the Calcification in digital mammogram. The new contribution in this article achieved from the precise detection of calcified regions of the digital mammogram. This packet of algorithms was efficient enough to avoid similarity in tissues, to exclude the nipple region, identify the region of interest and finally match the cancer template successfully. The universal denoising filter proved more efficiency compared with the traditional filters. The 3d representation of the classification auto-detection enable doctors to find the suspension regions simply. The proposed technique can help the doctors where the bio-vision fails to identify these distinguished features. 7. References [1] Segmentation of the Breast Region in Mammograms using a Rule-Based Fuzzy Reasoning Algorithm, M. Wirth, D. Nikitenko, J. Lyon, Dept. Computing & Information Science, University of Guelph Guelph, Ontario, Canada. [2] CAD System for Preprocessing and Enhancement Of Digital Mammograms K.Thangavel, M.Karnan, Department of computer science, Periyar University, SalemTamil nadu, India Department of computer science, Gandhigram Rural Institute-Deemed University, Gandhigram-624302, Tamil Nadu, India. [3] Sheila Timp, Analysis of Temporal Mammogram Pairs to Detect and Characterise Mass Lesions, 2006. [4] John Terry Sample, COMPUTER ASSISTED SCREENING OF DIGITAL MAMMOGRAM IMAGES, University of Southern Mississippi, August 2003. [5] Digital Database for Screening Mammography, University of South Florida, http://marathon.csee.usf.edu/Mammography/Database.html [6] The Digital Database for Screening Mammography, K.Bowyer, D.Kopans, W.P.Kegelmeyer, Jr.R.Moore, M.Sallam, K.Chang, K.Woods, University of South Florida, Department of Radiology, Massachusetts General Hospital, Department of Radiology, Massachusetts General Hospital. [7] Segmentation of High-Intensity Artifacts from Mammograms using Morphological Reconstruction, Michael A. Wirth, Rui Wang, and Jennifer Lyon, Department of Computing and Information Science, University of Guelph. [8] Rafel C. Gonzalez, Richard E. Woods, Digital Image Processing Second Edition, 2002 by Prentice-Hall, Inc. [9] Rafel C. Gonzalez, Richard E. Woods, Digital Image Processing using matlab, 2002 by Prentice-Hall, Inc. [10] Segmentation of the breast region in mammograms using active contours, Michael A. Wirth, Alexei Stapinski, Dept. of Computing and Information Science, University of Guelph. [11] Mammographic Mass Detection Using a Mass Template, Serhat Ozekes, Onur Osman, A.Yilmaz Çamurcu, Korean J Radiol 2005. [12] Computer Aided Diagnosis in Digital Mammograms: Detection of Microcalcifications by Meta Heuristic Algorithms, K.Thangavel, M.Karnan, Department of Mathematics, Gandhigram Rural Institute-Deemed University, Department of computer science, Gandhigram Rural Institute-Deemed University.

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[13] Mohamed Hassan, "Precise Object Tracking under Deformation", M.Sc. Thesis, Department of Electronics and Communications, Faculty of Engineering, Al Azhar University, Cairo, Egypt. 2010. [14] Wavelet Based Microcalcifications Detection in Digitized Mammograms S. Bouyahia, J. Mbainaibeye, N. Ellouze, Ecole Nationale d’Ingenieurs de Tunis, ENIT, BP37, Tunis le Belvédère 1002 Tunis, Tunisia. [15] A CAD System for the Automatic Detection of Clustered Microcalcifications in Digitized Mammogram Films, Songyang Yu and Ling Guan, IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 19, NO. 2, FEBRUARY 2000. [16] Microcalcification Detection using Multiresolution Analysis based on Wavelet Transform, Sakka E., Prentza A., Lamprinos I., Koutsouris D., Biomedical Engineering Laboratory, National Technical University of Athens Zografou Campus. [17] Detection of Breast Cancer Tumor Algorithm using Mathematical Morphology and Wavelet Analysis Mohiy Hadhoud, Mohamed Amin, Walid Dabbour Faculty of Science, Math and Computer Science Dept., Minoufia University, Shebin El-Kom, Egypt. [18] A Novel Method of Detecting Calcifications from Mammogram Images Based on Wavelet and Sobel Detector, Kai-yang Li, Zheng Dong, Laboratory of Biophysics and Biomedical Engineering, Wuhan University, China.

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