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Journal of Clinical Research in Radiology  •  Vol 1  •  Issue 2  •  2018 3
INTRODUCTION
C
omputer-aided diagnosis (CAD) is an interdisciplinary
technology combining elements of digital image
processing with medical image processing. CAD
techniques in X-ray, mammogram, magnetic resonance
imaging, and ultrasound diagnostics yield a great deal of
information, which the radiologist has to analyze and evaluate
comprehensively in a short time. The interpretation of medical
images, however, is still almost exclusively the work of
humans, but in the next decades, this is expected to change.[1-3]
Breast cancer affecting the women is known to cause high
mortality unless detected in time. Detection requires a simple
procedure of mammography followed by biopsy of the tumors
or lesions presents in the breast tissue. CAD systems require
medical image processing, a multidisciplinary science that
involves the development of computational algorithms on
medical images. In CAD-based breast cancer detection, the
digital mammogram image pair is initially preprocessed. The
right oriented mammogram is flipped for removal of any
unwanted artifacts and noise. Next image registration and
homogeneity enhancement are performed. Pectoral muscle
holds the breasts to the body and needs to be suppressed as it
mimics the intensities exhibited by tumors. The pectoral region
ismasked out leavingbehindthebreastregionofinterest(ROI).
Edge detection and contour detection are performed to separate
the ROI from the background. The anatomical segmentation
of the breast image is performed to isolate all closed objects
within the breast ROI. Asymmetry analysis is performed both
morphologically and anatomically for separation of both breast
regions. Density estimation of both breasts is performed to
suppress low-intensity regions. The presence resulting high-
intensity region is measured to establish risk factor.
Digital microscopy is used in field of blood specimen analysis
worldwide, but its use in surgical biopsy slide analysis
is rudimentary. Using mammogram and other screening
techniques can only detect the abnormalities; however,
conclusion cannot be drawn from that observation. Hence,
the CAD may be a detection tool but not a diagnosis tool
without incorporating the histopathological part.
In any CAD-based diagnosis of breast cancer, the schematic
diagram of modules is shown in Figure 1.
The breast region contains the outline edges of various breast
constituents bounded by the breast boundary that defines the
breast region from the image background. It is important to
extract the breast region by eliminating the pectoral muscle
region.
LITERATURE REVIEW
The presence of mammary gland is one of the key
characteristic features of female mammals. This paired organ
REVIEW ARTICLE
Edge-based Suppression of Pectoral Muscle in
Digital Mammogram
Samir Kumar Bandyopadhyay
Advisor to Chancellor, JIS University, Kolkata, West Bengal, India
ABSTRACT
The pectoral muscle, a non-breast region in mammograms, acts like an additional complexity in automated analysis using
computer-aided diagnosis systems. The suppression of pectoral muscle is necessary because the muscle has a similar opacity
as tumor. This paper proposed an algorithm for pectoral muscle isolation.
Key words: CAD, ROI, Asymmetry analysis, Pectoral muscle
Address for correspondence:
Prof. Samir Kumar Bandyopadhyay, Advisor to Chancellor, JIS University, India. E-mail: 1954samir@gmail.com
https://doi.org/10.33309/2639-913X.010202 www.asclepiusopen.com
© 2018 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.
Bandyopadhyay: Suppression of Pectoral Muscle
4 Journal of Clinical Research in Radiology  •  Vol 1  •  Issue 2  •  2018
which looks like a hemisphere is located at the level of the
third-sixth ribs.[2]
The breast is one of the most dynamic
organs in human. The breast can be divided into three regions
skin, nipple, subareolar tissues; subcutaneous region contains
fat and lymphatic’s; parenchyma region, a triangular shape
between the subcutaneous and retromammary regions, with
the apex toward the nipple; and finally, retromammary region
consists of retromammary fat, the pectoral muscle, ribs,
intercostal muscles, and the pleural reflection.[4]
The breast is
prone to various benign and malignant conditions. The most
common benign conditions are puerperal mastitis, fibrocystic
breast changes, and mastalgia. The latter part is very serious
in nature that is breast cancer. Breast cancer, a malignant
tumor developed from breast cells, is considered to be one of
the major causes for the increase in mortality among women,
especially in developed and developing countries.[5-8]
Some researchers proposed improved the seeded region
growing algorithm by making it independent of the pixel order
of processing and making it more parallel. This algorithm
retains the advantages of seeded region growing such as
fast execution, robust segmentation, and no parameters
to tune. The algorithm is also pixel order independent. All
pixels are processed in parallel if more than one pixel in the
neighborhood has same minimum similarity measure value.
If a pixel cannot be labeled, because it is equally likely to
belong to two or more adjacent regions, then it is labeled
as “tied” and takes no part in the region growing process.
This algorithm produces consistent segmentation because it
is not dependent on the order of pixel processing. Parallel
processing ensures that the pixels with the same priority are
processed in the same manner simultaneously.[9-10]
Other
researchers proposed a hierarchical stepwise optimization
algorithm for region merging, which is based on stepwise
optimization and produces a hierarchical partition of the
image. The sequences of partitions reflect the hierarchical
structure of the image.[11]
PROPOSED METHOD
In the pectoral muscle, suppression process consists of two
distinct separate processes - initially define rectangle to
isolate pectoral muscle from ROI and followed by suppression
of pectoral muscle.
The two common projections of mammogram are
medicolateral oblique (MLO) and craniocaudal (CC) view.
The advantage of the MLO projection is that almost the
whole breast is visible, often including lymph nodes. The
main disadvantage is that part of the pectoral muscle is
shown in upper left for the left oriented breast image, whereas
upper right corner for the right oriented breast mammogram,
respectively. In contrast, the CC view is taken from above,
resulting in an image that sometimes does not show the area
close to the chest wall. In this research work, MLO view is
considered for its complete visibility though it introduces a
new challenge to detect and isolate pectoral muscle. Edge-
based method is proposed for detection, extraction, and
isolation of pectoral muscle from the breast region. A smooth
pectoral boundary is obtained by taking pixels at a fixed
discrete interval and joining them by drawing a simple curve
between the pixel positions to obtain a smooth and enhanced
pectoral boundary. It is observed that the pectoral muscle lies
at the top left corner of the breast region (right pair flipped
horizontally during pre-processing) and its boundary line
traverses from the top margin downward toward the horizontal
reference line, that is, chest wall of the mammogram image
forming an inverted triangle. Since it is a muscular structure,
so, there are several layers that are present. These layers form
several impressions in edge map.
Therefore, more than one inverted triangles are there in edge
map. The objective is to identify the largest triangle which is
actual border of pectoral muscle and dividing pectoral muscle
from the breast region. This is shown in Figure 2.
It is now needed to identify the outermost edge line that
constitutes the edge of the pectoral muscle. It is observed that
the largest inverted triangle in the edge map starting from
the top margin and ending on the vertical baseline, that is,
horizontal reference line of the breast region on the left side,
is the pectoral region. It is important to identify the rightmost
pixel of the breast region on the right side and draw a vertical
line from top margin to bottom margin, parallel to the left
vertical baseline. Another line is drawn parallel to the top
and bottom margin. The said line is originated from the 2/3
position of horizontal reference line to the right vertical line.
Figure 3 shows the result.
The pectoral muscle barring a few mammograms lies within
the rectangle formed by these straight lines. The search is
limited to the obtained rectangle, thus reducing the processing
time instead of considering the entire mammogram image.
Anatomical
Histopathlogical
Biopsy Slide Analysis
Identification
and localization of
CAD
Tool
Density Asymmetry
Volume
Figure 1: The diagram of computer-aided diagnosis tools for
the detection of breast cancer
Bandyopadhyay: Suppression of Pectoral Muscle
Journal of Clinical Research in Radiology  •  Vol 1  •  Issue 2  •  2018 5
The scanning will be right to left, starting from the right
vertical line at the first row. If a pixel with black intensity is
obtained, that is, indicating an edge path. Traverse the pixel
path by considering all the surrounding pixels in a clockwise
priority and select the pixel with the highest priority for further
progress. The pixels that surrounded the edge pixel but are
of lower priority are stored in a backtrack stack to priorities
value and to be used only if the traversal process reaches a
dead end. If a dead end is reached, pop from the backtrack
stack for a lesser priority pixel and continue with the traversal
process. The pixels traversed is stored in a plotting list to be
used later for drawing the pectoral boundary. It continues
up to the left baseline or the bottom of the rectangle. If the
bottom of the rectangle is reached, the path is discarded, the
plotting list is erased, and the algorithm resumes by searching
the next black pixel at the first row from right to left. If the
horizontal reference line is reached that specifies the path
stored in the plotting list is pectoral boundary and that is
drawn in a new image and further processing is terminated. It
is shown in Figure 4.
The pectoral boundary obtained is further processed by
pectoral boundary smoothing method. The pectoral boundary
image obtained so far is not smooth. A smooth pectoral
boundary is obtained by taking pixels at a fixed discrete
interval and joining them by drawing a simple curve between
the pixel positions to obtain a smooth and enhanced pectoral
boundary. Figure 5 shows the result.
Algorithm: Pectoral muscle boundary detection
PM-BOUNDARY-DETECTION (Edge Map, HorzRefLine,
RightVerlLine, Height, Width)
PlottingList⟵ New Array of coordinates
Loop y ⟵RightVerlLine +1 to HorzRefLine - 1
Do PlottingList⟵ NULL
If Edge Map [1, y] = 0
Then PlottingList [1] ⟵ (1, y)
PlottingList⟵ TRAVEL (Edge Map, HorzRefLine,
RightVerlLine, Height, PlottingList)
If PlottingList ≠ NULL
Then PECTORAL-MUSCLE-BOUNDARY- SMOOTHING
(PlottingList)
Return 1
y ⟵ y – 1
Return 0
TRAVEL (Edge Map, HorzRefLine, RightVerlLine, Height,
PlottingList)
Back track Stack⟵ New Stack to store coordinates
Local Stack⟵ New Stack to store coordinates
Figure 3: Pectoral muscle lies within gray shaded derived
rectangular area
Figure 2: (a) The edge map of mammogram, (b) The layers of
pectoral muscle, and (c) inverted triangles marked by different
gray shades
a b c
Figure 4: (a-c) Original mammogram, obtained edge map,
and isolated pectoral boundary
a b c
Bandyopadhyay: Suppression of Pectoral Muscle
 Journal of Clinical Research in Radiology  •  Vol 1  •  Issue 2  •  2018
Privy⟵Holdingtheimmediatepreviouslytraversedcoordinate
Curt ⟵ Holding current coordinate
Privy⟵ NULL
Count ⟵ 1
Back track Stack. Push (PlottingList [1].x, PlottingList [1].y)
While x ≠ HorzRefLine AND y ≠ Height*(2/3) AND Back
track Stack ≠ Empty
Do x ⟵PlottingList [Count].x
y ⟵PlottingList [Count].y
Count ⟵ Count + 1
Flag ⟵ 0
If Edge Map [x,y+1] = 0 AND (Prev.x ≠ x OR Prev.y ≠ y+1)
Then PlottingList [Count].x⟵ x
PlottingList [Count].y⟵ y+1
Flag ⟵ 1
If Edge Map [x+1,y+1] = 0 AND (Prev.x ≠ x+1 OR Prev.y
≠ y+1)
Then If Flag = 0
Then PlottingList [Count].x⟵ x+1
PlottingList [Count].y⟵ y+1
Flag ⟵ 1
Else Local Stack.Push (x+1,y+1)
If Edge Map [x+1,y] = 0 AND (Prev.x ≠ x +1 OR Prev.y ≠ y)
Then If Flag = 0
Then PlottingList [Count].x⟵ x+1
PlottingList [Count].y⟵ y
Flag ⟵ 1
Else Local Stack. Push(x+1,y)
If Edge Map [x + 1,y - 1] = 0 AND (Prev.x ≠ x +1 OR Prev.y
≠ y -1)
Then If Flag = 0
Then PlottingList [Count].x⟵ x+1
PlottingList [Count].y⟵ y-1
Flag ⟵ 1
Else Local Stack. Push(x + 1,y - 1)
If Edge Map [x,y-1] = 0 AND (Prev.x ≠ x OR Prev.y ≠ y-1)
Then If Flag = 0
Then PlottingList [Count].x⟵ x
PlottingList [Count].y⟵ y-1
Flag ⟵ 1
Else Local Stack. Push(x,y - 1)
If Edge Map [x-1,y-1] = 0AND (Prev.x ≠ x-1 OR Prev.y ≠ y-1)
Then If Flag = 0
Then PlottingList [Count].x⟵ x-1
PlottingList [Count].y⟵ y-1
Flag ⟵1
Else Local Stack. Push(x - 1,y - 1)
Figure 5: (a and b) Detected pectoral boundary and smooth
boundary
b
a
Bandyopadhyay: Suppression of Pectoral Muscle
Journal of Clinical Research in Radiology  •  Vol 1  •  Issue 2  •  2018 7
If Edge Map [x-1,y] = 0 AND (Prev.x ≠ x-1 OR Prev.y ≠ y)
Then If Flag = 0
Then PlottingList [Count].x⟵ x-1
PlottingList [Count].y⟵ y
Flag ⟵ 1
Else Local Stack. Push(x - 1,y)
If Edge Map [x-1,y+1] = 0AND (Prev.x ≠ x OR Prev.y ≠ y+1)
Then If Flag = 0
Then PlottingList [Count].x⟵ x-1
PlottingList [Count].y⟵ y+1
Flag ⟵ 1
Else Local Stack. Push(x-1,y +1)
If Flag = 1
Then While Local Stack ≠ Empty
Do Back track Stack. Push(Local Stack. Pop)
Else Found ⟵ 0
While Found = 0 AND Back track Stack ≠ Empty
Do Curt ⟵Back track Stack. Pop
i⟵ Count – 1
While i 0 AND Found = 0
Do If PlottingList [i] = Curt
Then Found ⟵ 1
PlottingList[Count]=Curt
Prev =Curt
If x = HorzRefLine
Then Return PlottingList
Else If y = Height*(2/3) AND Back track Stack = Empty
Then Return NULL
PECTORAL-MUSCLE-BOUNDARY- SMOOTHING
(PlottingList)
New Image⟵ New Image containing smooth pectoral
Muscle Boundary
Δ ⟵ The Fixed Discrete Interval
i⟵ Holding a Coordinate
Loop i⟵PlottingList [1] to PlottingList.Count
Do NewImage ⟵ CURVE-LINE (NewImage, i, i+Δ)
ii ⟵ + Δ
Return NewImage
CURVE-LINE (NewImage, i, i + Δ)
//Function to draw the curve between two coordinates
Return NewImage
CURVE-LINE (NewImage, i, i + Δ)
//Function to draw the curve between two coordinates
Return NewImage
RESULTS ANALYSIS
The algorithm has been applied over all the 322 mammograms
in MIAS database and other available databases that
cover every type of breast shape, size, and type including
deformities, asymmetries, and abnormalities.
Results obtained after execution of the proposed algorithm on
MIAS database mammogram image are shown in Figures 6-8.
CONCLUSION
The proposed region-based and edge-based method for
pectoral muscle detection and suppression has been discussed.
The results obtained over MIAS database and other available
datasets have shown excellent output. The algorithms are
used to accurately identify pectoral muscle and suppress the
pectoral muscle successfully without losing any information
from the rest of the mammogram.
REFERENCES
1.	 Kurt B, Nabiyev VV, Turhan K. A novel automatic suspicious
mass regions identification using havrda and amp; charvat
Bandyopadhyay: Suppression of Pectoral Muscle
8 Journal of Clinical Research in Radiology  •  Vol 1  •  Issue 2  •  2018
entropy and Otsu’s N thresholding. Comput Methods Programs
Biomed 2014;114:349-60.
2.	 Roshan DY, Harada KA. Connected Component Labelling
Algorithm for Grayscale Images and Application of the
Algorithm on Mammograms. In: Annual ACM Symposium
on Applied Computing, 11-15 March 2007. Seoul, Republic of
Korea. New York, NY, USA: ACM; 2007. pp. 146-52.
3.	 Vaidehi K, Subashini TS. Automatic identification and
elimination of pectoral muscle in digital mammograms. Int J
Comput Appl 2013;75:15-8.
4.	 Yam M, Brady M, Highnam R, Behrenbruch C, English R,
et al. Three-dimensional reconstruction of microcalcification
clusters from two mammographic views. IEEE T Med Imaging
2001;20:479-89.
5.	 Weidong X, Lihua L, Wei LA. Novel Pectoral Muscle
Segmentation Algorithm Based on Polyline Fitting and
Elastic Thread Approaching. In: International Conference
on Bioinformatics and Biomedical Engineering, 6-8 July
2007; Wuhan, China. New York, USA: IEEE; 2007.
pp. 837-840.
6.	 Domingues I, Cardoso JS, Amaral I, Moreira I, Passarinho P,
Comba JS, et al. Pectoral muscle detection in mammograms
based on the shortest path with endpoints learnt by SVMs. Eng
Med Biol 2010;3158-61.
7.	 Camilus KS, Govindan VK, Sathidevi PS. Computer-aided
identification of the pectoral muscle in digitized mammograms.
J Digit Imaging 2010;23:562-80.
8.	 Abdellatif H, Taha TE, Zahran OF, Al-Nauimy W,
El-Samie FE. Automatic Pectoral Muscle Boundary Detection
in Mammograms Using eigenvectors Segmentation. In:
International Radio Science Conference, 10-12 April 2012;
Cairo, Egypt. New York, USA: IEEE; 2012. pp. 633-640.
9.	 Liu L, Wang J, Wang T. Breast and Pectoral Muscle Contours
Detection Based on Goodness of Fit Measure. In: International
Conference on Bioinformatics and Biomedical Engineering,
10-12 May 2011; Wuhan, China. New York, USA: IEEE;
2011. pp. 1-4.
10.	 Moayedi F, Azimifar Z, Boostani R, Katebi S. Contourlet-
based mammography mass classification using the SVM
family. Comput Biol Med 2010;40:373-83.
11.	 Sultana A, Ciuc M, Strungaru R. Detection of Pectoral Muscle
in Mammograms using Mean-Shift Segmentation Approach.
In: IEEE International Conference on Communications,
23-27 May 2010; Cape Town, South Africa. New York, USA:
IEEE; 2010. pp. 165-168.
How to cite this article: Bandyopadhyay SK. Edge-based
Suppression of Pectoral Muscle in Digital Mammogram.
J Clin Res Radiol 2018;1(2):3-8.
Figure 8: (a and b) Detected pectoral boundary and ground
truth superimposed on original mammogram
a b
Figure 7: (a and b) Detected pectoral boundary and smooth
boundary
a b
Figure 6: (a and b) Original mammogram and corresponding
edge map
a b

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BENIGN BREAST DISEASE
 

Edge-based Suppression of Pectoral Muscle in Digital Mammogram

  • 1. Journal of Clinical Research in Radiology  •  Vol 1  •  Issue 2  •  2018 3 INTRODUCTION C omputer-aided diagnosis (CAD) is an interdisciplinary technology combining elements of digital image processing with medical image processing. CAD techniques in X-ray, mammogram, magnetic resonance imaging, and ultrasound diagnostics yield a great deal of information, which the radiologist has to analyze and evaluate comprehensively in a short time. The interpretation of medical images, however, is still almost exclusively the work of humans, but in the next decades, this is expected to change.[1-3] Breast cancer affecting the women is known to cause high mortality unless detected in time. Detection requires a simple procedure of mammography followed by biopsy of the tumors or lesions presents in the breast tissue. CAD systems require medical image processing, a multidisciplinary science that involves the development of computational algorithms on medical images. In CAD-based breast cancer detection, the digital mammogram image pair is initially preprocessed. The right oriented mammogram is flipped for removal of any unwanted artifacts and noise. Next image registration and homogeneity enhancement are performed. Pectoral muscle holds the breasts to the body and needs to be suppressed as it mimics the intensities exhibited by tumors. The pectoral region ismasked out leavingbehindthebreastregionofinterest(ROI). Edge detection and contour detection are performed to separate the ROI from the background. The anatomical segmentation of the breast image is performed to isolate all closed objects within the breast ROI. Asymmetry analysis is performed both morphologically and anatomically for separation of both breast regions. Density estimation of both breasts is performed to suppress low-intensity regions. The presence resulting high- intensity region is measured to establish risk factor. Digital microscopy is used in field of blood specimen analysis worldwide, but its use in surgical biopsy slide analysis is rudimentary. Using mammogram and other screening techniques can only detect the abnormalities; however, conclusion cannot be drawn from that observation. Hence, the CAD may be a detection tool but not a diagnosis tool without incorporating the histopathological part. In any CAD-based diagnosis of breast cancer, the schematic diagram of modules is shown in Figure 1. The breast region contains the outline edges of various breast constituents bounded by the breast boundary that defines the breast region from the image background. It is important to extract the breast region by eliminating the pectoral muscle region. LITERATURE REVIEW The presence of mammary gland is one of the key characteristic features of female mammals. This paired organ REVIEW ARTICLE Edge-based Suppression of Pectoral Muscle in Digital Mammogram Samir Kumar Bandyopadhyay Advisor to Chancellor, JIS University, Kolkata, West Bengal, India ABSTRACT The pectoral muscle, a non-breast region in mammograms, acts like an additional complexity in automated analysis using computer-aided diagnosis systems. The suppression of pectoral muscle is necessary because the muscle has a similar opacity as tumor. This paper proposed an algorithm for pectoral muscle isolation. Key words: CAD, ROI, Asymmetry analysis, Pectoral muscle Address for correspondence: Prof. Samir Kumar Bandyopadhyay, Advisor to Chancellor, JIS University, India. E-mail: 1954samir@gmail.com https://doi.org/10.33309/2639-913X.010202 www.asclepiusopen.com © 2018 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.
  • 2. Bandyopadhyay: Suppression of Pectoral Muscle 4 Journal of Clinical Research in Radiology  •  Vol 1  •  Issue 2  •  2018 which looks like a hemisphere is located at the level of the third-sixth ribs.[2] The breast is one of the most dynamic organs in human. The breast can be divided into three regions skin, nipple, subareolar tissues; subcutaneous region contains fat and lymphatic’s; parenchyma region, a triangular shape between the subcutaneous and retromammary regions, with the apex toward the nipple; and finally, retromammary region consists of retromammary fat, the pectoral muscle, ribs, intercostal muscles, and the pleural reflection.[4] The breast is prone to various benign and malignant conditions. The most common benign conditions are puerperal mastitis, fibrocystic breast changes, and mastalgia. The latter part is very serious in nature that is breast cancer. Breast cancer, a malignant tumor developed from breast cells, is considered to be one of the major causes for the increase in mortality among women, especially in developed and developing countries.[5-8] Some researchers proposed improved the seeded region growing algorithm by making it independent of the pixel order of processing and making it more parallel. This algorithm retains the advantages of seeded region growing such as fast execution, robust segmentation, and no parameters to tune. The algorithm is also pixel order independent. All pixels are processed in parallel if more than one pixel in the neighborhood has same minimum similarity measure value. If a pixel cannot be labeled, because it is equally likely to belong to two or more adjacent regions, then it is labeled as “tied” and takes no part in the region growing process. This algorithm produces consistent segmentation because it is not dependent on the order of pixel processing. Parallel processing ensures that the pixels with the same priority are processed in the same manner simultaneously.[9-10] Other researchers proposed a hierarchical stepwise optimization algorithm for region merging, which is based on stepwise optimization and produces a hierarchical partition of the image. The sequences of partitions reflect the hierarchical structure of the image.[11] PROPOSED METHOD In the pectoral muscle, suppression process consists of two distinct separate processes - initially define rectangle to isolate pectoral muscle from ROI and followed by suppression of pectoral muscle. The two common projections of mammogram are medicolateral oblique (MLO) and craniocaudal (CC) view. The advantage of the MLO projection is that almost the whole breast is visible, often including lymph nodes. The main disadvantage is that part of the pectoral muscle is shown in upper left for the left oriented breast image, whereas upper right corner for the right oriented breast mammogram, respectively. In contrast, the CC view is taken from above, resulting in an image that sometimes does not show the area close to the chest wall. In this research work, MLO view is considered for its complete visibility though it introduces a new challenge to detect and isolate pectoral muscle. Edge- based method is proposed for detection, extraction, and isolation of pectoral muscle from the breast region. A smooth pectoral boundary is obtained by taking pixels at a fixed discrete interval and joining them by drawing a simple curve between the pixel positions to obtain a smooth and enhanced pectoral boundary. It is observed that the pectoral muscle lies at the top left corner of the breast region (right pair flipped horizontally during pre-processing) and its boundary line traverses from the top margin downward toward the horizontal reference line, that is, chest wall of the mammogram image forming an inverted triangle. Since it is a muscular structure, so, there are several layers that are present. These layers form several impressions in edge map. Therefore, more than one inverted triangles are there in edge map. The objective is to identify the largest triangle which is actual border of pectoral muscle and dividing pectoral muscle from the breast region. This is shown in Figure 2. It is now needed to identify the outermost edge line that constitutes the edge of the pectoral muscle. It is observed that the largest inverted triangle in the edge map starting from the top margin and ending on the vertical baseline, that is, horizontal reference line of the breast region on the left side, is the pectoral region. It is important to identify the rightmost pixel of the breast region on the right side and draw a vertical line from top margin to bottom margin, parallel to the left vertical baseline. Another line is drawn parallel to the top and bottom margin. The said line is originated from the 2/3 position of horizontal reference line to the right vertical line. Figure 3 shows the result. The pectoral muscle barring a few mammograms lies within the rectangle formed by these straight lines. The search is limited to the obtained rectangle, thus reducing the processing time instead of considering the entire mammogram image. Anatomical Histopathlogical Biopsy Slide Analysis Identification and localization of CAD Tool Density Asymmetry Volume Figure 1: The diagram of computer-aided diagnosis tools for the detection of breast cancer
  • 3. Bandyopadhyay: Suppression of Pectoral Muscle Journal of Clinical Research in Radiology  •  Vol 1  •  Issue 2  •  2018 5 The scanning will be right to left, starting from the right vertical line at the first row. If a pixel with black intensity is obtained, that is, indicating an edge path. Traverse the pixel path by considering all the surrounding pixels in a clockwise priority and select the pixel with the highest priority for further progress. The pixels that surrounded the edge pixel but are of lower priority are stored in a backtrack stack to priorities value and to be used only if the traversal process reaches a dead end. If a dead end is reached, pop from the backtrack stack for a lesser priority pixel and continue with the traversal process. The pixels traversed is stored in a plotting list to be used later for drawing the pectoral boundary. It continues up to the left baseline or the bottom of the rectangle. If the bottom of the rectangle is reached, the path is discarded, the plotting list is erased, and the algorithm resumes by searching the next black pixel at the first row from right to left. If the horizontal reference line is reached that specifies the path stored in the plotting list is pectoral boundary and that is drawn in a new image and further processing is terminated. It is shown in Figure 4. The pectoral boundary obtained is further processed by pectoral boundary smoothing method. The pectoral boundary image obtained so far is not smooth. A smooth pectoral boundary is obtained by taking pixels at a fixed discrete interval and joining them by drawing a simple curve between the pixel positions to obtain a smooth and enhanced pectoral boundary. Figure 5 shows the result. Algorithm: Pectoral muscle boundary detection PM-BOUNDARY-DETECTION (Edge Map, HorzRefLine, RightVerlLine, Height, Width) PlottingList⟵ New Array of coordinates Loop y ⟵RightVerlLine +1 to HorzRefLine - 1 Do PlottingList⟵ NULL If Edge Map [1, y] = 0 Then PlottingList [1] ⟵ (1, y) PlottingList⟵ TRAVEL (Edge Map, HorzRefLine, RightVerlLine, Height, PlottingList) If PlottingList ≠ NULL Then PECTORAL-MUSCLE-BOUNDARY- SMOOTHING (PlottingList) Return 1 y ⟵ y – 1 Return 0 TRAVEL (Edge Map, HorzRefLine, RightVerlLine, Height, PlottingList) Back track Stack⟵ New Stack to store coordinates Local Stack⟵ New Stack to store coordinates Figure 3: Pectoral muscle lies within gray shaded derived rectangular area Figure 2: (a) The edge map of mammogram, (b) The layers of pectoral muscle, and (c) inverted triangles marked by different gray shades a b c Figure 4: (a-c) Original mammogram, obtained edge map, and isolated pectoral boundary a b c
  • 4. Bandyopadhyay: Suppression of Pectoral Muscle Journal of Clinical Research in Radiology  •  Vol 1  •  Issue 2  •  2018 Privy⟵Holdingtheimmediatepreviouslytraversedcoordinate Curt ⟵ Holding current coordinate Privy⟵ NULL Count ⟵ 1 Back track Stack. Push (PlottingList [1].x, PlottingList [1].y) While x ≠ HorzRefLine AND y ≠ Height*(2/3) AND Back track Stack ≠ Empty Do x ⟵PlottingList [Count].x y ⟵PlottingList [Count].y Count ⟵ Count + 1 Flag ⟵ 0 If Edge Map [x,y+1] = 0 AND (Prev.x ≠ x OR Prev.y ≠ y+1) Then PlottingList [Count].x⟵ x PlottingList [Count].y⟵ y+1 Flag ⟵ 1 If Edge Map [x+1,y+1] = 0 AND (Prev.x ≠ x+1 OR Prev.y ≠ y+1) Then If Flag = 0 Then PlottingList [Count].x⟵ x+1 PlottingList [Count].y⟵ y+1 Flag ⟵ 1 Else Local Stack.Push (x+1,y+1) If Edge Map [x+1,y] = 0 AND (Prev.x ≠ x +1 OR Prev.y ≠ y) Then If Flag = 0 Then PlottingList [Count].x⟵ x+1 PlottingList [Count].y⟵ y Flag ⟵ 1 Else Local Stack. Push(x+1,y) If Edge Map [x + 1,y - 1] = 0 AND (Prev.x ≠ x +1 OR Prev.y ≠ y -1) Then If Flag = 0 Then PlottingList [Count].x⟵ x+1 PlottingList [Count].y⟵ y-1 Flag ⟵ 1 Else Local Stack. Push(x + 1,y - 1) If Edge Map [x,y-1] = 0 AND (Prev.x ≠ x OR Prev.y ≠ y-1) Then If Flag = 0 Then PlottingList [Count].x⟵ x PlottingList [Count].y⟵ y-1 Flag ⟵ 1 Else Local Stack. Push(x,y - 1) If Edge Map [x-1,y-1] = 0AND (Prev.x ≠ x-1 OR Prev.y ≠ y-1) Then If Flag = 0 Then PlottingList [Count].x⟵ x-1 PlottingList [Count].y⟵ y-1 Flag ⟵1 Else Local Stack. Push(x - 1,y - 1) Figure 5: (a and b) Detected pectoral boundary and smooth boundary b a
  • 5. Bandyopadhyay: Suppression of Pectoral Muscle Journal of Clinical Research in Radiology  •  Vol 1  •  Issue 2  •  2018 7 If Edge Map [x-1,y] = 0 AND (Prev.x ≠ x-1 OR Prev.y ≠ y) Then If Flag = 0 Then PlottingList [Count].x⟵ x-1 PlottingList [Count].y⟵ y Flag ⟵ 1 Else Local Stack. Push(x - 1,y) If Edge Map [x-1,y+1] = 0AND (Prev.x ≠ x OR Prev.y ≠ y+1) Then If Flag = 0 Then PlottingList [Count].x⟵ x-1 PlottingList [Count].y⟵ y+1 Flag ⟵ 1 Else Local Stack. Push(x-1,y +1) If Flag = 1 Then While Local Stack ≠ Empty Do Back track Stack. Push(Local Stack. Pop) Else Found ⟵ 0 While Found = 0 AND Back track Stack ≠ Empty Do Curt ⟵Back track Stack. Pop i⟵ Count – 1 While i 0 AND Found = 0 Do If PlottingList [i] = Curt Then Found ⟵ 1 PlottingList[Count]=Curt Prev =Curt If x = HorzRefLine Then Return PlottingList Else If y = Height*(2/3) AND Back track Stack = Empty Then Return NULL PECTORAL-MUSCLE-BOUNDARY- SMOOTHING (PlottingList) New Image⟵ New Image containing smooth pectoral Muscle Boundary Δ ⟵ The Fixed Discrete Interval i⟵ Holding a Coordinate Loop i⟵PlottingList [1] to PlottingList.Count Do NewImage ⟵ CURVE-LINE (NewImage, i, i+Δ) ii ⟵ + Δ Return NewImage CURVE-LINE (NewImage, i, i + Δ) //Function to draw the curve between two coordinates Return NewImage CURVE-LINE (NewImage, i, i + Δ) //Function to draw the curve between two coordinates Return NewImage RESULTS ANALYSIS The algorithm has been applied over all the 322 mammograms in MIAS database and other available databases that cover every type of breast shape, size, and type including deformities, asymmetries, and abnormalities. Results obtained after execution of the proposed algorithm on MIAS database mammogram image are shown in Figures 6-8. CONCLUSION The proposed region-based and edge-based method for pectoral muscle detection and suppression has been discussed. The results obtained over MIAS database and other available datasets have shown excellent output. The algorithms are used to accurately identify pectoral muscle and suppress the pectoral muscle successfully without losing any information from the rest of the mammogram. REFERENCES 1. Kurt B, Nabiyev VV, Turhan K. A novel automatic suspicious mass regions identification using havrda and amp; charvat
  • 6. Bandyopadhyay: Suppression of Pectoral Muscle 8 Journal of Clinical Research in Radiology  •  Vol 1  •  Issue 2  •  2018 entropy and Otsu’s N thresholding. Comput Methods Programs Biomed 2014;114:349-60. 2. Roshan DY, Harada KA. Connected Component Labelling Algorithm for Grayscale Images and Application of the Algorithm on Mammograms. In: Annual ACM Symposium on Applied Computing, 11-15 March 2007. Seoul, Republic of Korea. New York, NY, USA: ACM; 2007. pp. 146-52. 3. Vaidehi K, Subashini TS. Automatic identification and elimination of pectoral muscle in digital mammograms. Int J Comput Appl 2013;75:15-8. 4. Yam M, Brady M, Highnam R, Behrenbruch C, English R, et al. Three-dimensional reconstruction of microcalcification clusters from two mammographic views. IEEE T Med Imaging 2001;20:479-89. 5. Weidong X, Lihua L, Wei LA. Novel Pectoral Muscle Segmentation Algorithm Based on Polyline Fitting and Elastic Thread Approaching. In: International Conference on Bioinformatics and Biomedical Engineering, 6-8 July 2007; Wuhan, China. New York, USA: IEEE; 2007. pp. 837-840. 6. Domingues I, Cardoso JS, Amaral I, Moreira I, Passarinho P, Comba JS, et al. Pectoral muscle detection in mammograms based on the shortest path with endpoints learnt by SVMs. Eng Med Biol 2010;3158-61. 7. Camilus KS, Govindan VK, Sathidevi PS. Computer-aided identification of the pectoral muscle in digitized mammograms. J Digit Imaging 2010;23:562-80. 8. Abdellatif H, Taha TE, Zahran OF, Al-Nauimy W, El-Samie FE. Automatic Pectoral Muscle Boundary Detection in Mammograms Using eigenvectors Segmentation. In: International Radio Science Conference, 10-12 April 2012; Cairo, Egypt. New York, USA: IEEE; 2012. pp. 633-640. 9. Liu L, Wang J, Wang T. Breast and Pectoral Muscle Contours Detection Based on Goodness of Fit Measure. In: International Conference on Bioinformatics and Biomedical Engineering, 10-12 May 2011; Wuhan, China. New York, USA: IEEE; 2011. pp. 1-4. 10. Moayedi F, Azimifar Z, Boostani R, Katebi S. Contourlet- based mammography mass classification using the SVM family. Comput Biol Med 2010;40:373-83. 11. Sultana A, Ciuc M, Strungaru R. Detection of Pectoral Muscle in Mammograms using Mean-Shift Segmentation Approach. In: IEEE International Conference on Communications, 23-27 May 2010; Cape Town, South Africa. New York, USA: IEEE; 2010. pp. 165-168. How to cite this article: Bandyopadhyay SK. Edge-based Suppression of Pectoral Muscle in Digital Mammogram. J Clin Res Radiol 2018;1(2):3-8. Figure 8: (a and b) Detected pectoral boundary and ground truth superimposed on original mammogram a b Figure 7: (a and b) Detected pectoral boundary and smooth boundary a b Figure 6: (a and b) Original mammogram and corresponding edge map a b