Journal of Computer Aided Diagnosis of Medical Images Vol.2

Oct. 1998

Original Paper
A Method for Automatic Detection of Spicules in Mammograms
Hao JIANG*, Wilson TIU**, Shinji YAMAMOTO** and Shun-ichi IISAKU*

Abstract:

This paper presents a method for automatic detection of spicules in mammograms. The method consistsof two steps, enhancement and feature selection. First, spicule shadows are enhanced by a newly developed operation.
An opening operation is applied to remove noise and a direction map is made for feature selection. Second, a concentration
expression is given with gray levels and two features are selected for recognition of tumors with spicules. In the method,
not only is the direction of spicules considered, but also the density is utilized. The method was tested on 24 samples
which included seven tumors with spicules. The recognition rate for tumors with spicules was 100% without false
positives.
Key words:

medical image, mammogram, automatic detection, mathematical morphology

1. Introduction
Breast cancer is one of the leading cancers in the female
population. Mammographic screening is the most effective
method of early detection; however, manually reading
mammograms is labor intensive. Several approaches toanalyzing mammograms by computer have been proposedin the past [Brzakovic 90, Dhawan 86, Dengler 93, Dhawan
96, Highnam 94, Kegelmeyer 92, Kegelmeyer 93, Kobatake
96, Karssemeijer 96, Lai 89, Laine 94, Morrow 92, Ng 92,
Petrick 96, Qian 94, Spiesberger 79, Shen 94, Strickland
96, Sahiner 96, Zheng 96]. These approaches can be looselygrouped into two categories, approaches for identifying
clustered microcalcifications [Dengler 93, Dhawan 96,
Spiesberger 79, Strickland 96, Zheng 96] and approaches
for tumor detection [Brzakovic 90, Kegelmeyer 92,
Kegelmeyer 93, Kobatake 96, Karssemeijer 96, Lai 89, Ng
92, Petrick 96, Sahiner 96].
Since the existence of spicules is one of most importantvisual signs in diagnosing breast cancer, several of thesestudies discuss the detection of tumors with spicules [Ng92, Kegelmeyer 92, Kegelmeyer 93, Kobatake 96,
Karssemeijer 96]. A spine-oriented method for therecognition of tumor with spicules has been presented by*Communications Research Laboratory, MPT(Koganei, Tokyo 184-8795, Japan)**Toyohashi University of Technology(Toyohashi, Aichi 441, Japan)
received: 1 July 1998
revised: 6 October 1998
accepted: 7 October 1998

Ng [Ng 92]. Kegelmeyer suggested an approach to detecting
spicules based on analyses of local oriented edges[Kegelmeyer 92, Kegelmeyer 93]. Kobatate proposed amethod for detecting spicules based on skeleton analyses
and Hough transformation [Kobatake 96]. And finally, amethod of detecting stellate distortions based on statisticalanalyses of maps of pixel orientations has been proposedby Karssemeijer [Karssemeijer 96]. All of the studies
suggested that the direction of spicules is useful information
for detecting of tumors with spicules.
This paper describes a method for automaticallyrecognizing spicule shadows in mammograms. The method
considers not only the direction of spicules, but also thedensity for classifying tumors with spicules. In the next
section, we give an overview of the method. Section 3
discusses a method for enhancing spicule shadows, which
is modified based on gray scale reconstruction ofmathematical morphology and preserves gray levels of
spicule shadows. In Section 4, two features are provided
for classification of spicule shadows from others such as
blood vessels and mammary glands. Experimental results
as well as the results of a comparison with thresholding are
given in Section 5.

2. Method overview [Jiang 97a, Jiang 97b]
Most breast carcinomas appear as stellate lesions,consisting of a central tumor mass surrounded by spicules.
Spicules have a star-shaped structure that emanates fromthe central mass with sharp, dense, and fine lines. A model- 23 -

Journal of Computer Aided Diagnosis of Medical Images Vol.2 Oct. 1998
of a spicule is illustrated in Fig. 1 and is considered as a gray scale reconstruction, i.e., gray scale partial
ridge. The central tumor mass is an isolated shadow and reconstruction and TPR. Then we apply an opening operation
appears as a bright blob on mammograms. Candidate to remove noise and make a direction map of spicules forshadows of central tumor masses can be detected by using the next step of classification.
the method proposed by Yamamoto [Yamamoto 96]. In this
3.1 Gray scale reconstruction
method, input images for recognition of spicules are
Vincent provided two definitions of gray scale
separated from original images based on the detectedreconstruction [Vincent 93]. Let F and S be two gray scale
locations of candidate central tumor masses. A flowchart ofimages defined on the same domain and S(p) < F(p) for
the recognition process for spicules is shown in Fig. 2.
each pixel p. The gray scale reconstruction R can be given
by
A
A
A
F
R ( S ) = { In+1 = ( In B ) ∧ F , until In+1 = In }(1)
where In B is the dilation of In by structuring element B,
I0 = S, and ∧ stands for pointwise minimum.
(a)

(b)

(c)

The gray scale reconstruction R of F from S is obtainedby iterating gray scale geodesic dilations of S under F until
stability is reached. The regional domes of image F can beextracted by subtracting the reconstructed image from theoriginal image; i.e., the components with high frequency
can be effectively detected based on the gray scalereconstruction. This operation is called top-hat byreconstruction.

Fig. 1 The model of a spicule shadow,(a) a bird’s eye view with gray levels,(b) gray levels intersecting spicule at A,(c) gray levels along with spicule at A.

Original image including
tumor candidate region
Top-hat by partial reconstruction
Opening operation
Direction map
Opening image
Feature selection
Classification
ResultFig. 2 Flowchart for recognition of spicules

The spicule recognition method we propose consists of
two steps, enhancement of spicule shadows and feature
selection. First, spicule shadows are enhanced by using a
newly developed operation. An opening operation is applied
to remove noise and a direction map is made for feature
selection. Then, a concentration expression of spiculesaround a central mass candidate region is given with graylevels and two features are selected based on the expression
for classification.

On the other hand, spicules are the components with highfrequency, but the lines with directions. So that it is difficult
to extract spicules by gray scale reconstruction because twodimensional flat structuring elements are used in thedefinitions. For example, Fig.3 illustrates gray scalereconstruction applied to a spicule. At point A, the peak does
not appear in the cross section along the spicule in Fig.1(c),although it does appear in the cross section intersecting the
spicule in Fig.1(b). The top of the spicule (broken line ofFig.3(c)) can be extracted, but the shoulder with the higher
gradient can not be extracted and is reconstructed by iterating
gray scale geodesic dilations.
A

F

A

A

S

Reconstructed image

3. Enhancement of spicules(a)A new operation, called top-hat by partial reconstruction(TPR), is proposed for enhancing the image of the spicules.
This operation is based on gray scale reconstruction ofmathematical morphology [Vincent 93]. In this section, we
first review gray scale reconstruction and provide a modified- 24 -(b)(c)

Fig. 3 Gray scale reconstruction of spicule shadow,(a) bird’s eye views of F and S with gray levels,(b) gray levels intersecting spicule shadow at A,(c) gray levels along with spicule shadow at A.

Journal of Computer Aided Diagnosis of Medical Images Vol.2
Original image including
tumor candidate region

Seed image

Oct. 1998

Original image including
tumor candidate region

Structuring elements
1Gray scale reconstruction
using each structuring element
2
Reconstructed images
3
1 2
3 4

Subtracting h fromoriginal image gray levels
Seed Image
Partial reconstruction

Minimum intensity
projection

4
Point of interest
Output of top-hat bypartial reconstruction

Output of partial
reconstruction

Fig. 4 Top-hat by partial reconstruction

3.2 Top-hat by partial reconstruction
Since spicules are long and slender shadows and havedirections, the operation for enhancing spicule shadows isbased on gray scale reconstruction that preserves the
direction, length and density of spicules. The operation isshown in Fig. 4. The modified gray scale reconstruction,i.e., gray scale partial reconstruction PR, is given by(2)
PR = Min { Rk ( S, Bk) } ( k = 1, 2, 3, 4 )where Bk is structuring element with direction as shown in
Fig. 5 and Min is minimum operation. The top-hat by partial
reconstruction TPR is given by
TPR = F - PR.
(3)
1

2

3

4

Fig. 5 The structuring elements of four directions for gray scalepartial reconstruction

A

h
F

A
h

S

A

Reconstructed image(b)

3.3 Seed image selection
The seed image S uses the image subtracted from theoriginal image with constant h because it can be
automatically obtained and the details of the spicules arepreserved. So that the seed image is included in the original
image and the gray levels of the spicules enhanced by TPR
operation are restricted to be below h. In our experiments,
h is selected based on the gray levels measured from spiculesin the original images.
3.4 Opening operation and direction map
After the TPR operation, an opening operation is applied
to suppress noise and a direction map is made for classifying
tumors with spicules. A flowchart showing the opening
operation and direction map generation is shown in Fig. 7.

h(a)spicules so that they are below the gray level h. For example,
at point A in Fig. 6, the output of the PR operation using thestructuring element, which is parallel to the spicule, is higher
than that of the other three structuring elements. The original
image is reconstructed (just like the gray scale reconstructionof Fig. 3(c)). However, the output using the structuring
element, which is perpendicular to the spread direction of
the spicule, is lower. Therefore, the spicule shadows areenhanced and the difference h is preserved by minimum
and TPR operations (Fig. 6).

(c)Fig. 6 Gray scale partial reconstruction,(a) bird’s eye views of F and S with gray levels,(b) gray levels intersecting spicule at A,(c) gray levels along spicule at A.

Fig. 6 illustrates the TPR operation. Let us first prepare
four different structuring elements (Fig. 5) and generate aseed image S by subtracting a constant h from original image
F. We then carry out the gray scale reconstruction for eachstructuring element and operate pointwise minimum of thefour reconstructed images to obtain PR. Finally, we subtract
the image of PR from the original image F to enhance the

The structuring element of opening is eight bars withdifferent directions and the length of bars is experimentally
determined to remove noise and preserve the enhanced
spicules. The opening operation is carried out by using eachstructuring element and an opening image is obtained bypointwise maximum of intensities from the eight images.
Eight direction codes are given according to eight
structuring elements with different directions as shown in- 25 -

Journal of Computer Aided Diagnosis of Medical Images Vol.2

Oct. 1998

Output of top-hat bypartial reconstruction
Code Structuring element
0゜
0
22.5゜
1
45゜
2
67.5゜
3
90゜
4
112.5゜5135゜
6
157.5゜
7

Opening operation
Maximum intensity projection

Opening image

Direction map

Fig.7 Opening operation and direction map

Fig. 7. If a maximum output of the opening operation using
a structuring element is obtained from outputs of the opening
operation using eight structuring elements at a pixel, thedirection code of the structuring element is considered as adirection unit vector of a spicule at the pixel and recordedin the direction map (Fig. 7).

4. Feature selection and classification
In this section, we give the concentration expression of
spicules based on the enhanced image and direction map
and select two features for classification.
4.1 Concentration expression of spicules
Let Si express the degree of concentration of spicules fora selected region i, which can be given by
S i = m,n
Σ S i (m,n)

S i (m,n) =

(4)

(x,y)
Σ cos α・bo
r

x,y

Σ
x,y

bo (x,y)r(5)where bo(x,y) is the gray level of pixel (x,y), α is the angle
between the line from pixel (m,n) to (x,y) and the direction

4.2 Mask selection
Two masks are selected for classification of spicules. One
is for the concentration region of spicules and the other isfor the existence region. The concentration region is thedomain of pixel (m,n) and the area of a circle with radius r1
from central point O of the tumor candidate region, which
is previously determined. The value of r1 is experimentally
determined. The discussion with respect to r1 is also in thefollowing section. The existence region is the domain ofpoint (x,y) and the bounded area of circles from radius r1 to
r2. The radius r2 is also experimentally determined, but it islarge enough to include the central mass and spicules.
4.3 Classification
To suppress the reflections of noise such as from bloodvessels and fibrous tissues, the existence region is notdirectly used to calculate the degree of concentration.
Instead, the existence region is partitioned into eight small
regions Ri (i=1,…,8) having the same size (Fig. 8(b)) andthe degree of concentration of each region Si (i=1,…,8) is
calculated individually by equations (4) and (5). Then two
features are selected to classify spicules and others such as
blood vessels and mammary glands.

unit vector at pixel (x,y), and r is the distance between pixel(m,n) and (x,y).

Bo(x,y)
α
R3 R2
r1 O
R4
R1
S(m,n)α'α''R5R8Bo(x',y')R6 R7Bo(x",y") r2(b)(a)
Concentration region of spicules
Existence region of spicules

Term Si is measures the concentration behavior of theradiating line pattern around a central region. The term cosα
is a measure of the convergence from pixel (x,y) to pixel(m,n). If it is close to 1.0, it means that the direction vector
points to the pixel (m,n) (Fig. 8(a)). The direction map isapplied to calculate cosα. The term bo(x,y) is the gray level(a) Two masks of concentration andexistence regions of spicules(b) 8 small regions partitioned fromexistence region of spiculesof spicule at pixel (x,y) and is the output of the opening
operation. The term 1/r is the amount the influence of the
pixel is suppressed as the distance increases. The
denominator of equation 5 normalizes the degree bounded
between 0 and 1.0.

Fig. 8 The masks and regions for feature selection

Let us denote the mean and the minimum of Si by Smeanand Smin. The two features are given as- 26 -

Journal of Computer Aided Diagnosis of Medical Images Vol.2 Oct. 1998
Smean = (S1+S2+…+S8)/8(6)Class Average Distance
0.032
Smin = Min (S1,S2,…,S8)
(7)
0.030
where Min is minimum operation. It is clear that the values
0.028
of Smean and Smin are close and large for spicules and the values
0.026
are small for others such as blood vessels and mammary
0.024
0
20
40
60
r1 mask radiusglands.

80

Fig. 9 Relationship between the mask radius of concentration
region and average distance in class.

5. Experiments
5.1 Experimental data set
The mammograms used in the experiments are computed
radiography (CR) images from a data base published by theJapan Society of Computer Aided Diagnosis of Medical
Images. The spatial resolution is 0.1 mm and the size ofeach CR image is 2550×2100 pixels with 10-bit accuracy.
We used 24 samples to evaluate the performance of the
proposed method. The original images of the samples are
partitioned 512×512 sized areas based on the positions ofcandidate regions identified as containing central tumor
masses [Yamamoto 96]; i.e., seven samples that include
tumors with spicules, five samples that include tumors
without spicules, eight samples of mammary glands, andfour samples of blood vessels.
5.2 Experimental conditions
The position of a candidate region of a central tumor massis first detected by using Quoit filtering [Yamamoto 96].
Quoit filtering is effective for detecting isolated shadowlike central tumor masses.

5.3 Results
Fig. 10 shows a series of images for classification basedon the proposed method. Fig. 10(a) is the input image andis enhanced by TPR processing as shown in Fig. 10(b). The
gray levels of the image are bounded from 0 to 10. The
values of spinal axes of spicules and local maxima are close
to 10. The result of opening operation is shown in Fig. 10(c)and the noises such local maxima are mostly suppressed.
Fig. 10(d) shows the direction code at each pixel in the
direction map for the right lower frame in Fig. 10(c). The
code corresponds to the direction code of Fig. 8. The code
at a pixel is the mean, if the number of the code with thesame maximum in opening operations of eight directions isappeared in multiple times from 2 to 4. The sign - meansthat there is not a spicule pixel and the - is given if the numberof the code with the same maximum is appeared more than
5 times among eight directions of opening operation. Fig.
10(e) and (f) are the two masks, one for the concentration
region and one for the existence region.

The h of generating seed image is 10 for enhancement of
spicules and the value is the mean of gray levels of 10 places
selected from the original images with spicules. The lengthof the bar filter used in the opening operation is 20 pixels.
The radii r1 and r2 of the mask radius are respectively 38
and 200 pixels. The central point O is the center of gravityof the tumor candidate region. The radius r2 is determinedbased on the consideration that the maximum diameter ofconcentric circles formed by spicules is less than 40 mm.
The value of r1 is experimentally determined based on thefollowing discussion.
The features of Smean and Smin are calculated from samples
with spicules by changing the value r 1. The average of
distance in the class of tumors with spicules is calculated
based on the two features. Fig. 9 shows the result and there
is a minimum at r1=38; i.e., for the optimal classification oftumors with spicules, the radius r1 should be 38 pixels.
- 27 -

(a)

(b)

(c)

(d)

(e)

(f)

Fig. 10 Series of images for recognition of spicules,(a) original image,(b) enhancement,(c) opening operation,(d) direction map,(e) concentration region of spicules,(f) partitioned existence region.

Journal of Computer Aided Diagnosis of Medical Images Vol.2

Discrimination axis
(Normal to principle axis)
Smean + 1.36 Smin = 1.42

0.7

Oct. 1998

Tumor with Spicules
Tumor without spicules
Mammary glands
Blood vessels

Smin

0.6

0.5

Principle axis
1.36 Smean - Smin = 0.33
0.4
0.58

0.60

0.62

0.64

Smean

0.66

0.68

0.70

Fig. 11 Classification graph

Table 1 Classification results

Input images

Classification Tumors with spicules

The others

Tumors with spicules

7/7

0/7

The others

0/17

17/17

The experimental results for the 24 samples are shown in
Fig. 11 (the axes are Smean and Smin). Tumors with spicules

is very difficult and the gray levels are important forclassifying tumors with spicules.

and others such as blood vessels and mammary glands areclassified according to the following condition. The sample
is classified as a tumor with spicules if the features Smean andSmin are respectively larger than 0.652 and 0.521. Table 1
shows the classification results for this condition.

0.8

Discrimination axis
(Normal to principle axis)
Smean + 2.43 Smin = 2.01

0.6

Smin

The discrimination function is normal to the principle axis
obtained based on the two features and is given by
Smean + 1.36 Smin = 1.42.
(8)

0.4

0.2

Principle axis
2.43 Smean - Smin = 1.10

5.4 Discussion
In equation (5), not only is the direction of spicules
considered, but also the gray level of each pixel for feature
selection. The effectiveness of introducing gray levels of
the enhanced spicules is discussed as follows.
An appropriate threshold is selected for the binarizationof the enhanced spicules, which is 8 in our experiments.
Other conditions are the same as the experiments above.
The experimental results shown in Fig. 12 clearly show thatthe separation between the sets of tumors with spicules andother sets such as blood vessels and mammary glands is not
good, i.e., it is difficult to find a function that classifies them.
It can be found from the original images and the enhanced
images that there are small spicules with weak contrast
around the central mass. Therefore, the threshold selection

0.0
0.5

0.6

0.7

0.8

Smean
Tumor with Spicules
Tumor without spicules
Mammary glands
Blood vessels

Fig. 12 Classification graph using a binarized image

6. Conclusion
In this paper, a method for automatic detection of spicules
in mammograms has been proposed. The spicule shadowsare first enhanced by using a newly developed operation
called top-hat partial reconstruction. The operation canpreserve the length of spicules. An opening operation isapplied to remove noise and a direction map is made for- 28 -

Journal of Computer Aided Diagnosis of Medical Images Vol.2 Oct. 1998
feature enhancement by multiscale analysis, IEEE Trans. Med. Imagfeature selection. Next, a spicule concentration expression
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characterize the radiating structure of spicule shadows. Not
Desautels: Region-based contrast enhancement of mammograms, IEEE
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Journal of Computer Aided Diagnosis of Medical Images Vol.2

Oct. 1998

Authors:
Shinji YAMAMOTO received his B.S. and
Ph.D. degrees in electronics engineering fromHao JIANG received his B.S. in mechanicalengineering from Hefei University ofTechnology, China in July 1982 and his Ph.D.

Nagoya University in March 1962 and March
1974, respectively. From April 1962 to August

in information engineering from Nagoya
University in May 1992. From June 1992 toMarch 1996, he was an assistant professor at
Toyohashi University of Technology. From
April 1996, he joined the Communications Research Laboratory,
Ministry of Posts and Telecommunications, as a senior researcher.
His research interests are image processing, pattern recognition,
graphics, virtual reality, and applications in medicine.
Wilson TIU received his B.S. and M.S. degrees in knowledge
based information engineering from Toyohashi University ofTechnology in March 1993 and March 1995, respectively. Since
April 1995, he has been with the Yutaka Co. Ltd. His interests areimage processing and its application to medical image.

1973 he was a researcher, and from August
1973 to February 1980, he was a seniorresearcher at the Central Research Laboratory,
Hitachi Co. Ltd.. From February1980 to February 1987, he was
the director of the Medical Engineering Research Division at theCentral Research Laboratory, Hitachi Co. Ltd, and from February
1987 to March 1990, he was the director of the Development
Division at the Naka Factory of Hitachi Co. Ltd.. From April 1990,
he joined the Toyohashi University of Technology as a professor.
His research interests are image processing, pattern recognition,
applications in medicine, character recognition, and applications
to handwriting characters.
Shin-ichi IISAKU received his Ph.D. from
Hokkaido University in 1982. He joined
Kokusai Denshin Denwa Co., Ltd., where heworked in the R&D labs and his interests were
network architecture and communication
protocols. In 1995, he joined theCommunications Research Laboratory,
Ministry of Posts and Telecommunications. His research interests
are intelligent multimedia retrieval and image processing.

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Journal of Computer Aided Diagnosis of Medical Images Vol.2

Oct. 1998

乳房Ｘ線像におけるスピキュラ陰影自動認識の一手法
江浩＊ テュ ウィルソン＊＊ 山本眞司＊＊ 飯作俊一＊＊
郵政省通信総合研究所，＊＊豊橋技術科学大学

本論文では乳房Ｘ線像におけるスピキュラ陰影自動認識の一手法について述べる．
本手法はスピキュラ
陰影強調処理と特徴量選択という二つのステップからなっている．まずスピキュラ陰影の強調処理を行う．その強調処理では，Mathematical Morphology を用いた Tophat by Partial Reconstruction という新しく開発した手法を用いる．またその強調処理で得られた画像に対して雑音除去及びスピキュラの伸びる方向を示す方向マスクを作成する．そしてその画像における各画素の濃淡値及び方向を用いてスピキュラの集中度を計算し，二つの特徴量を選択して識別関数を作り，入力画像におけるスピキュラ陰影の有無を自動認識する．実験ではスピ
キュラ陰影を有する７症例を含む２４症例に本手法を適用した結果，全て正しく認識された．

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