acmcopyright

# Unsupervised Method to Localize Masses in Mammograms

Bilal Ahmed Lodhi

Korea University  Seoul  Republic of Korea<EMAIL_ADDRESS>
###### Abstract

Breast cancer is one of the most common and prevalent type of cancer that
mainly affects the women population. chances of effective treatment increases
with early diagnosis. Mammography is considered one of the effective and
proven techniques for early diagnosis of breast cancer. Tissues around masses
look identical in mammogram, which makes automatic detection process a very
challenging task. They are indistinguishable from the surrounding parenchyma.
In this paper, we present an efficient and automated approach to segment
masses in mammograms. The proposed method uses hierarchical clustering to
isolate the salient area, and then features are extracted to reject false
detection. We applied our method on two popular publicly available datasets
(mini-MIAS and DDSM). A total of 56 images from mini-mias database, and 76
images from DDSM were randomly selected . Results are explained in-terms of
ROC (Receiver Operating Characteristics) curves and compared with the other
techniques. Experimental results demonstrate the efficiency and advantages of
the proposed system in automatic mass identification in mammograms.

###### keywords:

Breast Mass Detection; Automatic Mammogram Segmentation; Mass Classification;

††titlenote: PhD candidate in Korea University

<ccs2012> <concept>
<concept_id>10010147.10010257.10010258.10010260.10003697</concept_id>
<concept_desc>Computing methodologies Cluster analysis</concept_desc>
<concept_significance>300</concept_significance> </concept> </ccs2012>

[300]Computing methodologies Cluster analysis

##  1 Introduction

Breast cancer is the most common cause of cancer-related deaths among women
worldwide. With more than 450, 000 deaths each year, breast cancer accounts
for about 14% of all female cancer deaths ([11]). Recent statistics says that
1 out of 10 women is affected by breast cancer in their lifetime. According to
GLOBOCAN 2012, 1.7 million Women were diagnosed with breast cancer and there
were 6.3 million women alive who had been diagnosed with breast cancer in the
previous five years ([3]). Although the breast cancer rate is increasing in
many parts of the world, however the mortality rate is much higher in less
developed countries, because of insufficient facilities available for
diagnosis and treatment. Therefore, there is an urgent need of reliable and
affordable approches for early diagnosis and treatment of breast cancer in
less developed countries.It can have significant impact on cancer treatment,
faster recovery and reducing mortality.

Mammography is considered most effective technique as it can detect
85$\sim$90% percent of all breast cancers ([3]). A mass is an uncontrolled
grown tumor and we classify them into malignant and benign by their size,
shape and other features. As described earlier that early diagnosis is a key
for effective treatment. Therefore the job radiologist becomes very important,
who can interpret mammograms for early diagnosis. Mammogram does not have so
much information imprinted on the film. Cancer diagnosis in this scenario
becomes a subjective criteria. Radiologist opinion depends on their
experience. [22] states that radiologist’s diagnosis inter-observer variation
rate is 65 $\sim$ 75% ). He can miss a significant proportion of abnormalities
and in addition a large number of mass come out to be benign after biopsy
([22]). [12] states that Computer aided diagnosis (CAD) systems is helpful for
the radiologists in diagnosis. ([26]) claims that detection accuracy improved
by combining the expert knowledge with CAD scheme.We proposed an algorithm to
address the previously described problem for breast cancer diagnosis. Proposed
scheme is novel in the following ways:

  * •

Scope of the detection algorithm is wide. It can detect different type of
cancers in malignant and benign categories. Proposed algorithm was tested on
many ill-defined masses also.

  * •

A method is proposed to identify masses, irrespective of their size and shape.

  * •

We proposed an efficient and unsupervised approach to detect masses in
mammogram images. It segments the breast region and finds the candidate
regions of interests (ROIs).

  * •

Generalization of algorithm is tested by experimenting cross validation across
two different datasets.

The organization of paper is as follows. Section I presents introduction and
significance of the work. Section II discusses previous and related work.
Section III briefly describes the proposed method for preprocessing. Section
IV analyses the results and finally, Section V concludes the article.

##  2 Related Work

In order to develop computer aided breast cancer detection tools, researchers
have used several approaches. [10] proposes a Particle Swarm Optimized Wavelet
Neural Network (PSOWNN) based classification approach for detection of masses
in digital mammograms. Their method is based on extracting Laws Texture Energy
Measures from the mammograms and classifies the suspicious regions by PSOWNN.
Their method does not have any noise removal algorithm and also they do not
propose any intelligent method ROI detection. In ([25], [24], authors used
Latent Dirichlet Allocation (LDA) to mine the feature set of mammogram images.
They presented the modified Morphological Component Analysis method to
identify the mass region and then extracted morphological features. Finally,
LDA is used to classify the masses. Simple Morphological approaches are
sensitive to noise. They also did not presented any preprocessing for
collection of ROIs.

In [21], authors proposed the modified Fuzzy c-means clustering to cluster the
masses, extracted morphological, textual and spatial features and classified
the features using SVM (Support Vector Machine). Their method lacks the noise
removal and intelligent ROI segmentation. [16] presented a set of tools to aid
segmentation and detection of mammograms that contained mass. After the top-
hat morphological operator, de-noising is applied. Image gray-level was
enhanced by wavelet transform and wiener filter. Finally, segmentation method
was employed using multiple thresholding, wavelet transform and genetic
algorithm. They used manual process to reduce the false positives generated by
genetic algorithm. Authors also did not do the automatic classification of the
ROIs. [1] proposed a method for mass detection based on saliency map. After
the creation of saliency map, a threshold is used to obtain the ROI. A number
of features were extracted and classified by SVM. Automated detection of
malignant masses in screening mammography has been discussed in [19]. It
developed a technique that used presence of concentric layers which surrounds
a focal area in the breast region, that has suspicious morphological
characteristics and low relative incidence. Segmentation process in both of
the earlier described algorithms are focused on the bright or salient parts of
the image, which is always mis-leaded by the blood vessels resulting in the
whole breast parenchyma as a ROI. [13] work is based on applying one-
dimensional recursive median filter to different number of angles to each
pixel. It becomes difficult to detect when structure of the mass and a normal
glandular looks similar. It can only be detected if there were asymmetry
between the left and right breasts.

[14] proposed method is based on the analysis of ISO-intensity contour groups
to segment skeptical masses. False positives are then removed using features
based on flow orientation in adaptive ribbons of pixels across the margins of
masses. The procedure is tested on 56 images from the Mini-MIAS database and
got a sensitivity at the rate of 81% at 2.2 false positives per image.
Furthermore, based on gray-level co-occurrence matrices (GCM) and using
features on a logistic regression method, the classification of masses were
performed as benign or malignant using five texture features. An accuracy of
0.79 is achieved as a result of this classification, with 19 benign and 13
malignant lesions. Authors used the hard thresholds to get the contours of
objects in the image. Contour are very sensitive to noise resulting in
increase of false positives and bad segmentation. Algorithm will fail to
detect the mass if the boundary is ill-defined or even the mammogram is much
denser.

[5] proposed a method for diagnosis of breast lesions (diagnosis). Masses
using the wavelet transform to obtain a multi-resolution representation of the
original image at each resolution, a set of features is extracted which serves
as input to a binary tree classifier. Algorithm achieved 91.9% true positive
detection accuracy. ROIs were manually cropped in the proposed system. Their
proposed system is based on wavelet and curvelet coefficients, which is very
high in numbers. Selecting best coefficients is an optimization problem and
also it is very sensitive to noise. [27] proposed a method combines several
artificial intelligence techniques with the discrete wavelet transform (DWT).
ROI’s are determined through dimensional analysis using a multi-resolution
Markov random field algorithm, the segmentation is performed that leads to the
application of tree type classification strategy. The algorithm was tested in
the Mini-MIAS database and has a sensitivity of 97.3% with 3.9 false positives
per image. Their proposed method works well with well-defined masses, but ill-
defined masses are difficult to be classified by this method.

##  3 Methodology

Female breast parenchyma is a multiplex biological structure and is composed
of glandular, fatty, and lymphatic tissues (lymphovascular structures).
Mammography imprints the texture information of breast tissue in image. Though
the composing components may be complicated, the mass regions are
characterized of high intensity and high texture. Figure 1 shows the process
of a typical analysis system.

Figure 1: General phases of Detection Algorithm

We propose an efficient and unsupervised approach to identify the suspicious
regions in mammogram images. Proposed algorithm isolates the spatially
interconnected structures in the image, which are concentrated around salient
intensities. As a result, it is possible to extract high-level information to
analyze further, to characterize the physical properties of mass regions and
to prepare a short-list of skeptical ROIs. Figure 2 shows our proposed
algorithm. Further explanation of the algorithm is explained in the following
subsections.

###  3.1 Image Standardization

Data from different sources should be converted to one format. Proposed
algorithm was tested on two datasets: Digital Database for Screening
Mammography (DDSM) ([8]:[9]) and Mammographic Image Analysis Society Database
(mini-MIAS) ([20]). MIAS dataset is in Portable Gray Map (PGM) format while
DDSM dataset contains images in LJPEG format. We converted the DDSM dataset TO
16-bit Portable Network Graphics (PNG) format by a wrapper program developed
by us 111Utilities at http://microserf.org.uk/academic/Software.html were used
to write a wrapper program..

Figure 2: Overview of the Proposed Algorithm

###  3.2 ROI Detection Phase

One of the main tasks is to get mass-candidate regions. Following subsections
describe the way to get those regions.

####  3.2.1 Smoothing

It is assumed that malignant masses typically cause distortion to the
surrounding tissues. So, segmentation process can over-segment the image and
it can’t get those masses in a single entity. To overcome this problem, prior
smoothing of the image is necessary. In the present work, Gaussian pyramid is
used to uniformly highlight the salient regions. Subsampling to many levels
results in over smoothing the image which converts the image regions as blobs.
However, some researchers ([17]) have performed mass detection on reduced
resolutions of 800m.

Figure 3: Original Image from MIAS dataset

Regions of mass are hyper-densed. We need to get the full mass area to extract
meaningful features from the ROI. Abrupt changes in the intensity of the
objects present in the image effect the segmentation process. Peaks in the
image objects are smoothed by the above described preprocessing.

####  3.2.2 Hierarchical Clustering with GLCM (Gray level Co-occurrence
Matrix) data

We applied hierarchical clustering with GLCM data to segment the salient
regions of image. Before segmentation of the image, its contrast was enhanced
by CLAHE (Contrast Limited Adaptive Histogram Equalization). Further, we
calculate the gray-level co-occurrence matrix from image. GLCM is created with
distance one and 4 directions [0 1; -1 1; -1 0; -1 -1] (0$\degree$,
45$\degree$, 90$\degree$, 135$\degree$). Other angles were not computed due to
redundancy of the data. GLCM data from all directions are summed up and
normalized. Figure 4 depicts the explanation of co-occurrence matrix.

Figure 4: Process of co-occurrence matrix

Intensities in mass exhibit the glowing effect (intensities are propagated
from the center of the masses). Hierarchical clustering can cluster image
according to propagated intensities while having a family structure of
concentric objects. At each hierarchical level a measure of dissimilarity is
defined to differentiate clusters and object are merged together as one, if
their dissimilarity is less than or equal to the acceptable dissimilarity
measure.

Many researchers have proposed methods for multilevel thresholding by
discriminant analysis ([15], [18], and [2]). They thresholded the image by the
cluster analysis irrespective of the physical location of the cluster. This
idea works better if the image is multi-modal and we divide it into two
clusters (background and foreground). However, it does not give fine results
on low-level x-rays images which are mostly unimodal. In this case, multi-
thresholding does not give compact objects for ROI. We incorporated the
discriminant analysis ([2]) with GLCM data to get compact objects. The
proposed method clusters the image intensities in a hierarchy, according to
their co-occurrence and similarity measure. Number of thresholds are found by
cutting the dendrogram at desired level.Initially, each gray-level is
designated to a different cluster i.e. g gray-levels in image will generate q
number of clusters and each cluster has its own threshold $T_{i}$ . Family
hierarchy of clustering process can be viewed as a dendrogram. The estimated
thresholds for the image to segment can be obtained by cutting the branch in
dendrogram. Clustering algorithm is defined in algorithm 1.

Result: Return n thresholds

Given: A set of gray-levels {$x_{1},x_{2},....,x_{q}$};

A distance function dist(c1,c2);

m number of threshold levels;

for  _i=1 to q_ do

$c_{i}$ = {$x_{i}$};

$t_{i}$ = {$x_{i}$};

end for

C = {$c_{1}$,…..,$c_{q}$};

T = {$t_{1}$,…..,$t_{q}$};

for  _k=1 to q-m_ do

\- make adjacent cluster pairs;

\- ( $c_{min1}$,$c_{min2}$) = minimum dist($c_{i}$,$c_{j}$) for all
$c_{i}$,$c_{j}$ in C;

\- remove $c_{min1}$ and $c_{min2}$ from C;

\- remove $t_{min1}$ and $c_{min2}$ from T;

\- add {$c_{min1}$,$c_{min2}$} to C;

\- add {$t_{min1}$,$c_{min2}$} to T;

end for

Algorithm 1 Clustering Algorithm

##### Distance Metric

The distance measure between two clusters in the proposed algorithm is defined
as ratio between the measure of observed dispersement and the expected
dispersement. it is calculated as:

$dist_{(q_{i},q_{j})}\ =\
{{(1-CP{{}_{q_{i}q_{j}}})(P_{q_{i}}-P_{q_{j}})^{2}{\left[{\overline{X_{q_{i}}}-\overline{X_{q_{j}}}}\right]}^{2}}\over{\sigma_{q_{i}q_{j}}^{2}}}$
(1)

where $q$ is the total number of clusters, $P_{q}$ is probability density
function of image histogram and it can be calculated as equation 2. $CP_{i,j}$
represents the normalized co-occurrence frequency of the cluster pair being
merged. It is defined in equation 3. $\overline{X}$ is the mean value of the
cluster and defined in equation 5. $\sigma^{2}$ is the variance of both
clusters which are being merged. It is defined in equation 7.

$P_{q}\ =\ \sum\limits_{l=T_{q-1}+1}^{T_{q}}{h(l)}$ (2)

where $l$ represents the gray-level in image (value: [0 255])such that
$\sum\limits_{i=1}^{q}{P_{i}}=1$.

$CP_{i,j}=\sum\limits_{t=T_{q_{j-1}+1}}^{T_{q_{j}}}{{{\sum\limits_{s=Tq_{i-1}+1}^{T_{qi}}{CM_{s,t}}}\over{T_{qi}-T_{q_{i-1}}}}}$
(3)

where $CM_{s,t}$ is the co-occurrence probability of gray-level s and t.

Mean is also called as the expectation of the cluster and can be represented
as:

$\mu=E(q_{i})=\sum\limits_{i=1}^{q}{l_{i}P(l_{i})}$ (4)

so we calculated the mean as:

$\overline{X}_{q}\
={{1}\over{P_{q}}}\sum\limits_{l=T_{q_{1}}+1}^{T_{q}}{lh(l)}$ (5)

Variance of the distribution is defined as:

$\sigma^{2}=\sum\limits_{i=1}^{q}{{\left({l_{i}-\mu}\right)}^{2}P(l_{i})}$ (6)

This formulates the variance into the following equation.

$\sigma_{q_{i}q_{j}}^{2}\ =\
{\sum\limits_{l=T_{q-1}+1}^{T_{q2}}{\left[{l-\overline{CX}_{q_{i}q_{j}}}\right]}}^{2}h(l)$
(7)

where $\overline{CX}$ is defined as average mean of the cluster pair. It is
calculated as the weighted average between the cluster means of the pair being
merged:

$\overline{CX}_{qiqj}\ =\ {{P_{qi}\overline{X}_{qi}\ +\
P_{qj}\overline{X}_{qj}}\over{P_{qi}+P_{qj}}}$ (8)

We imposed a restriction that only the adjacent clusters are allowed to merge.
The similarity measurement is adapted by [15]. Pair having the minimum
distance value is the best candidate to merge.

The saliency of a region is measured by the nesting depth of hierarchical
clustering which identifies nested objects. One statistical parameter
LevelParameter is introduced that represents the levels in hierarchical
clustering. LevelParameter value of 5 is used in the study. Figure 5(a) shows
the number of objects found in mammogram by segmentation process.

####  3.2.3 Grouping and Elimination

Segmentation process described in previous section results in a large number
segmented objects. We devised an algorithm to reduce the number of objects and
extract only the relevant data for analysis. Our first step in this process is
grouping and elimination. As previously described, masses exhibits the glowing
effect, therefore, we first find the dense-core portions and then go to the
next threshold level to find objects which encircles the previously detected
object. The idea of prestige in link analysis is used and also the
hierarchical clustering nodal relation is considered. Every possible regions
are given a prestige score of 1. When they are encircled by other immediate
lower density parent they forward their prestige score to the parent. Sum of
euclidean distance between the higher density objects and lower density
objects. Lower density object should cover at least 80% of higher density
object. Algorithm 2 describes the process of merge score. This process is
repeated for all the segmented regions at every selected hierarchical level.
Hierarchical clustering gives a parent-child relationship of clusters also, we
can use this relationship to avoid unacceptable merging of objects. Objects
having at least 3 prestige score from each level are up-sampled to full
resolution image. Result of merging process is shown in Figure 5, where 5(a)
represents the detected ROIs and 5(b) shows the merged objects.

Result: Merge Score

Given;

Labels = {$L_{1},L_{2}$,…..,$L_{n}$};

for  _i = 1 to n_ do

currentLabel = {$L_{i}$};

Objects = Object by current current label;

numObjects = number of Objects by current label;

for  _j = 1 to numObjects_ do

mergeScore[i][j] = 1;;

end for

dist = distanceL2 (Objects[i], Objects[i-1] );

if  _dist <0.2_ then

mergeScore[i][j] += mergeScore [i-1][j] ;

end if

end for

Algorithm 2 Merge Score

(a) Detected ROIs (Objects)

(b) Merged ROIs (Objects)

Figure 5: Detected objects and their merging process

###  3.3 Features for False Positive (FP) Analysis

Following set of features are extracted to classify objects into true mass and
breast tissue (false positive). These features are well-established
statistical features and finalized by radiologist too after analyzing the
prominent patterns of masses on mammograms.

#### Region Contrast:

Generally, mass is imprinted on mammogram as a dense object as compared to its
surroundings, having at least a uniform density. We used this property for
classification between true mass and breast tissue. Region Contrast is
computed as a difference between mean intensities of foreground and background
in ROI. Foreground area is the selected mass or object while background
represents the background area surrounding this object. Regions which results
in negative values of region contrast are rejected for further processing.

####  Mean Gradient:

Gradient monitors the directional change in intensity. Gradient magnitude
describes that how quick the image is changing. We calculated the mean
gradient of the boundary pixels which strengthens the compactness of the
region( described later).

#### Entropy:

The concept of entropy is in information theory which states the probabilistic
behavior of the information sources. This statistical measure is a measure of
randomness that is used to characterize the texture of image.

#### Standard Deviation:

It is popular term in statistics which gives a measure of spread of data. This
represents the measure, that how much close the points are in the given region
of the image.

#### Compactness:

The value of compactness gives the ratio of contour which encloses an area. it
is defined as:

$compactness\ =\ 1-{{4*pi*A}\over{P^{2}}}$ (9)

where A is Area of object enclosed by perimeter P. Usually Benign masses have
higher value of compactness, because it defines that small perimeter is
enclosing a bigger area. We have used this feature in benign vs malignant
classification too.

###  3.4 Classification Model

SVM(Support Vector Machine) was used to classify the masses. We selected
support vector machine as it gives good results for binary classification. The
basic idea behind SVM is to separate the input data by optimal method. As our
data is not linearly separable, we used Gaussian RBF (Radial basis function)
kernel. Sigma and C are two important factors for RBF kernel. optimal values
for RBF were grid-searched between $10^{-3}$ to $10^{3}$. Harmonic Mean (HM)
is calculated to compare the C and sigma pairs. Harmonic Mean is defined as:

$HM\ =\ {{2*\ sens*spec}\over{sens+spec}}$ (10)

where sens is sensitivity and spec represents specificity of the system. We
adopted a 10-fold cross validation technique to train,test and validate the
data.

##  4 Results and Discussion

###  4.1 Image Database

This study was carried out on images from two databases. We selected 56 images
from mini-MIAS database ([20]). It includes 13 normal, 13 malignant and 30
benign cases. The dataset include all types of masses from both classes
(benign and malignant). Table 1 shows the overview of number of cases used in
experiments from MIAS-dataset. We also selected 76 cases from DDSM database
([8]:[9]). Table 2 shows the summary of DDSM database

Table 1: MIAS (Mammographic Image Analysis Society) dataset Benign | Malignant
---|---
Dense | Fatty-Glandular | Glandular | Dense | Fatty-Glandular | Glandular
8 | 12 | 10 | 2 | 6 | 5
Table 2: DDSM (Digital Database for Screening Mammography) dataset information

Property | Description | Image Count
---|---|---
Density | 1 | 14
2 | 43
3 | 34
4 | 6
Shape | Fine Linear Branch | 2
Irregular | 16
Irregular Architecture | 8
Lobulated | 9
Oval | 8
Pleomorphic | 4
Round | 3
Margin | Circumscribed | 10
Circumscribed ill Defined | 1
ill Defined | 9
ill Defined Spiculated | 1
Microlobulated | 1
Obscured | 3
Obscured ill defined | 2
Obscured ill defined spiculated | 3
Spiculated | 15
Pathology | Benign | 14
Malignant | 38
Normal | 46

###  4.2 Detection of ROIs

Our proposed preprocessing steps detected almost all masses in the dataset.
Through careful examination of ROIs, we found that our algorithm missed two
cases in MIAS database. One from Malignant and the other from Benign case
(mdb179 and mdb191), Dense-glandular and Fatty Glandular. The contrast in
these two images was very high and distributed, making it difficult to detect
isolated regions. All other masses were successfully detected. This results in
the detection accuracy of 95.3%. The detection accuracy on DDSM dataset was
97.3%. We missed 2 cases. Detected ROIs were carefully compared with the given
ground truth data.

(a) ROC of Normal and Mass (MIAS dataset)

(b) ROC of Normal and Mass (DDSM Dataset)

(c) ROC of training on MIAS and Test on DDSM

(d) ROC of training on DDSM and Test on MIAS

(e) ROC of MIAS and DDSM combined

Figure 6: Breast tissue vs Mass classification results (ROC plots)

###  4.3 Normal and Mass Differentiation

Our algorithm detected all the malignant masses except one (mdb0186) on MIAS
dataset. However we did not get so prominent success on benign masses. 30
cases were tested but Algorithm failed to detect 6 masses. Three of these
missed masses were Fatty (mdb069, mdb080 and mdb195), two were Dense-glandular
(mdb193 and mdb290) and one was Fatty-glandular (mdb190). The total accuracy
of the system was 83.43%. Figure 7 shows the example ROI which is classified
as mass.

We further investigated the missed cases and found the following observations.
In the first missed case (mdb069), the margin and boundary with wide
transition zone, if we compare with opposite side breast, the lesion could be
detectable, and in clinical practice, we describe it as architectural
distortion. In case of mdb080, the tumor lesion is subtle ill margined, non-
mass like parenchymal asymmetric pattern. In case of mdb195, the malignant
lesion is almost isodensed to the normal breast fatty parenchyma. So the
detection is not feasible. In mdb186 we found that the mass has poor contrast
and also it lacks the dense region. Itś contrast with respect to the
surrounding was very poor. Benign cases, where algorithm was unable to
classify masses, we observed that, in three fatty and one fatty glandular case
(mdb069, mdb080, mdb190 and mdb195) the masses were not clear. They do not
have center core region and their contrast with respect to their surrounding
was poor too. We are confident that if we add some good contrast enhancement
technique, our algorithm performance will be improved by classifying above
described cases as well. The remaining two dense-glandular cases (mdb193 and
mdb290) do not follow the assumption we made in this paper (they do not have
glowing effect), so features values were not good in these cases to classify
them. To successfully detect masses in these cases, it may require additional
methods or include more features. In the present work, we did not reject any
region because of its size, this results in generating a large number of false
positives. Although our classification phase reduces the number of FPs, but we
aim to reduce the number FPs by improved algorithm in future work. We also
believe that automatic breast density assessment before applying our method
will improve the performance ([11]).

We validated the results by plotting the receiver operating characteristic
(ROC) curve, which illustrates the performance of binary classifier system as
its discrimination threshold is varied. Figure 6 shows the ROC curve of
classification between normal and mass data, which is obtained by varying the
threshold on the probabilities by classifier (SVM). AUC refers to the Area
Under Curve. Table 3 shows the classification results in terms of specificity
and sensitivity.In medical domain, only sensitivity is not important,
algorithm should yield good specificity results also. As previously described,
we used harmonic mean (equation 10) to get the best pair of specificity and
sensitivity

Algorithm missed 2 cases from malignant category and 6 from benign category of
DDSM dataset. The maximum sensitivity and specificity pair we achieved is
91.32% and 85.05% respectively. Average sensitivity and specificity is 76.19%
and 87.05% respectively.

We also tested our algorithm for its generality by training it on one dataset
and testing on the other. Algorithm was trained on MIAS dataset, tested on
DDSM and vice versa. Algorithm results in table 3 confirm our claim that
proposed algorithm is not limited to some limited type of masses or
abnormalities. It covers a wide spectrum of masses. Distribution of the
dataset is uneven, which degrades the performance of learning algorithm.

Table 3: Specificity and Sensitivity of Mass vs Normal classification Training | Testing |  | Senstivity
---
(%)
| Specificity
---
(%)
MIAS | MIAS | 90.47 | 82.95
DDSM | DDSM | 78.03 | 87.02
MIAS+DDSM | MIAS+DDSM | 75.51 | 84.12
MIAS | DDSM | 87.80 | 64.88
DDSM | MIAS | 75.51 | 84.12

Investigation of the missed cases confirms the reasons described earlier.
Case0004 from DDSM shows poor contrast around the mass, making it difficult to
be detected. Case0005, case0006, and case008 does not follow the assumption we
made in the paper. More features may be required to detect those masses. We
also calculated the number of false positives per image which was 4.67
FP/Image. This number is calculated only on Normal Images to give the fair
view of the system.

Figure 7: Detected Mass ROI

###  4.4 Comparison with existing algorithms

[19] stated their results of mass detection phase they achieved 84.4%
detection accuracy. Their algorithm is based on image enhancement and after
that Gaussian Markov Random Field (MRF) is used for mass segmentation. They
did not classified the ROIs into mass and non-mass regions. [10] also reported
their detection accuracy as 94.44%. They presented a particle swarm
optimization (PSO) based detection technique. Our algorithm outperformed
previously reported detection accuracies.

Work presented by [7], [23], [5], and [4] can be consider as the baseline in
recent work on this domain. [4] implemented a fully automated system. They
extracted local binary pattern LBP features and the classification is done by
SVM. They also proposed a feature selection technique. [4] reported their
performance in terms of sensitivity and 75.86% is reported for overall CAD
performance on MIAS database. [6] reported their results on already selected
305 ROIs and achieved a sensitivity of 76.53%. They extracted features from
Grey-level co-occurrence matrices (GLCM) and then classify features into mass
and non-mass regions. [5] proposed the technique of curvelet transformation,
feature selection and then classification by SVM. They manually cropped the
ROIs and then applied their algorithm. Their reported accuracy is higher 90%,
but their algorithm is not fully automated, they lack mass detection phase.
All methods were tested on separate dataset, cross validation between the
datasets was never performed.

##  5 Conclusion

This paper proposes a new mass detection in mammogram images. The proposed
method is fully automated. It finds the candidate regions by segmenting the
salient regions in mammogram and then extract features to differentiate
between breast tissue and mass.Promising results are obtained in mass
identification and normal vs mass tissue classification. Classification
results confirms that the segmentation process extracts enough information to
find masses and localize it in mammogram. Experiments were performed on mini-
MIAS and DDSM databases to show the usefulness and generalization of the
proposed algorithm. Correlating the full image set (CC and MLO) is considered
as future work that can help to identify the architectural distorted
mammograms also.

## References

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