This document summarizes a study on segmenting cysts in breast ultrasound images using texture features and an active contour method. The authors apply the Chan-Vese level set method to segment cyst regions based on texture features calculated from the images using different kernel sizes. Segmentation performance is evaluated using measures like area error rate, DICE coefficient, sensitivity and Hausdorff distance. The results show that mean texture features and preprocessing the images with Qui's mask produce more accurate segmentations with lower error rates compared to other texture features and kernels.

1. International Journal of Research in Advent Technology, Vol.2, No.6, June 2014
E-ISSN: 2321-9637
200
Cyst Segmentation in Texture Feature Space in
Ultrasound Breast Images
Maitri R Bhat, Nanda S
Dept of Instrumentation Technology
SJCE, Mysore
Email:rbmaitri123@gmail.com, nanda_prabhu@yahoo.co.in
Abstract–Object segmentation is a crucial step in medical image analysis. Active contour method of
segmentation gives more accurate results but depends largely on initial contour selection. In this paper we are
concerned with breast cyst segmentation based on texture features and also using Chan-vese method. Image
features are obtained by convolving first order feature kernels of mean, standard deviation and entropy with
image. Performance evaluation of segmentation is done using measures like Area Error Rate, DICE coefficient,
sensitivity and Hausdroff distance.
Index Terms–Chan-vese level set; image features; ultrasound images; area error rate
1. INTRODUCTION
Cysts are fluid-filled sacs that grow inside the
breasts. These sacs form when normal milk glands in
the breast get bigger. They're often described as round
or oval lumps with distinct edges. Breast cysts can be
painful and may be worrisome but are generally
benign[Guyer,(1992)]. They are most common in pre-menopausal
women in their 30s or 40s.Cystic breast
disease has been recognised as most frequent female
lesion and is generally benign [Huff,(2009)]
Many works have been carried out and still
continued to segment breast mass and to distinguish
malignant mass from benign. Segmentation of benign
breast cyst is first step in this direction.
Active contour models are considered useful in
segmentation and applied on ultrasound images to
segment cyst part. In this paper, level set method of
active contour method (or snakes) is applied to
segment breast cyst in ultrasound images.
Also, developing the idea suggested by Moraru
et.al [Moraru, 2013] the active contour model is
applied over texture features obtained by convolving
first order features with the image.
The results were analysed by using Area Error
Rate (AER), DICE coefficient, sensitivity and
Hausdroff distance.
2. MATERIAL AND METHODOLOGY
A set of 15 cystic Breast Ultrasound (BUS)
images were obtained from hospital. They are
acquired by Philips HD 15 using 7.5MHz transducer.
The implementation method is summarized in
block diagram shown in Fig 1.
The BUS image is passed through wiener filter
to remove noise added mainly due to respiration of
patient. Then, filtered image is contrast enhanced
using adaptive histogram equalization. Then those
images are converted to texture features as described
in following section.
Images are convolved using four kernels of
size 3 x 3, 5 x 5, 7 x 7, and 9x 9 to get texture
features. Three texture features Mean, standard
deviation, entropy are used. Another mask used is
Qui’s mask [Qui,(2005)] which is atypical mask and
is given by,
M =
√
√
√
√
√
√
√
√
1
√
√ √
√
√
√
√
√

7. Eq.(1)
where should satisfy the condition 4 × ( + ( /2 +
/√2 + /2√2)) + 8 × ( /√5) = 1 and its value is
0.0724.

8. International Journal of Research in Advent Technology, Vol.2, No.6, June 2014
E-ISSN: 2321-9637
201
Fig1. Block diagram of segmentation and evaluation scheme
Active contour method is applied on each
Image feature obtained for fixed number of iterations
[Whitakar,(1998)]. Chan-vese level set method is used
for active contour. Chan- vese level set method
performs better than gradient method [Wang (2010)]
because it utilizes global image statistics inside and
outside the curve and works well even with weak
boundaries and in the presence of noise. Region based
measures used for validating segmentation are Area
Error Rate (AER), dice coefficient and Sensitivity.
Boundary based measure of Hausdorff distance is also
calculated.
Area Error Rate estimates difference between
occupied areas and is used to quantitatively assess the
segmentation accuracy.
Area Error Rate is given by
=
× 100 % Eq(2)
Where represents the number of pixels
within the area URand is the amount of pixels
within the area IR, while isthe number of pixels
in the manually extracted area MSR. LVsnakedenotes
the automatically segmented area and LVmanual is
the fragment extracted manually. UR = LVsnake∪
Lvmanualand
IR = Lvsnake∩Lvmanual.
Dice coefficient is used to compare similarity
of two samples and is calculated as
!# = ∗
%
Eq(3)
Sensitivity is calculated by using following
formula
'()*+,+-+,. =
Eq(4)
Hausdroff distance is a measure for the
dissimilarity of two shapes and is used here for
comparing the boundary detected. It is given by,
/! = 0 1230+)4‖#2 − #4‖7
8#2 ∈ ', #4 ∈ ;' Eq(5)
Where ASR is Automatically Segmented Area
and MSR is Manually Segmented Area and #2 is
boundary of ASR and #4 is boundary of MSR.
3. RESULTS
The breast images contain breast muscle
tissues (as bright areas) and suspicious areas (as dark
regions).
The image is filtered and contrast enhanced as
shown in Fig 2.Fig 2(c) shows initial contour selected
by the user. Level set segmentation is applied on pre-processed
image and features mean, standard
deviation and entropy are obtained using kernels of
sizes 3 X 3,5 X 5,7 X 7 and 9 X 9 shown respectively
in Fig 3,Fig4,Fig 5 and Fig 6.

9. International Journal of Research in Advent Technology, Vol.2, No.6, June 2014
E-ISSN: 2321-9637
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(a) (b) (c)
Fig 2 . Original Image (a) , filtered and contrast enhanced image (b) ,Selection of initial contour(c).
(a1) (a2) (a3) (a4)
Fig 3. Segmentation using Image features, Original Image (a1), Image features using kernel size 3 X 3- mean
(a2), standard deviation (a3), entropy (a4).
(b1) (b2) (b3) (b4)
Fig 4.Segmentation using Image features, Original Image (b1), Image features using kernel size 5 X 5- mean
(b2), standard deviation (b3), entropy (b4).
Applying segmentation on image processed with
Que’s mask gives result as in Fig 7.
The red contours represent automatically detected
boundaries
4. DISCUSSIONS
Evaluation measures Area Error Rate (AER),
DICE coefficient, sensitivity and Hausdroff distance
(HD) are tabulated in table 1 for segmented original
image and for image filtered using Qui’s mask. Also,
these are calculated for texture features mean,
standard deviation and entropy using four kernels

10. International Journal of Research in Advent Technology, Vol.2, No.6, June 2014
E-ISSN: 2321-9637
203
(3X3, 5X5, 7X7, and 9X9) in table 2 and table 3.
According to Moraru et al. [Moraru(2013)] using
entropy filter gives reduced AER. In our experiments
AER is prominently reduced using Mean kernel and
Qui’s mask compared to Entropy and Standard
deviation.
DICE coefficient gives measure of similarity
between manually segmented region and region
segmented using texture features. DICE and
sensitivity values are in [0,1] range. Segmentation
performed on pre-processed BUS image, image
filtered using Qui’s mask and using mean texture
feature have comparatively higher DICE and
sensitivity values than segmentation results obtained
using texture features standard deviation and entropy.
Hausdroff distance (HD) is used as evaluation
of boundary obtained automated method. Manual
segmentation is done by radiologist. HD is
prominently reduced using Mean kernel and Qui’s
mask compared to Entropy and Standard deviation.
(c1) (c2) (c3) (c4)
Fig 5. Segmentation using Image features, Original Image (c1), Image features using kernel size 7 X 7- mean
(c2), standard deviation (c3), entropy (c4)
(d1) (d2) (d3) (d4)
Fig 6. Segmentation using Image features, Original Image (d1), Image features using kernel size 9 X 9- mean
(d2), standard deviation (d3), entropy (d4)
(e1) (e2)
Fig 7. Segmentation using Image filtered with Que’s mask , Image filtered with Que’s mask (e1), boundary
detected (e2).

11. International Journal of Research in Advent Technology, Vol.2, No.6, June 2014
E-ISSN: 2321-9637
AER DICE
3X3 5X5 7X7 9X9 3X3 5X5 7X7 9X9
Imean 10.97157964 12.42564442 15.796431 19.03502974 0.9423211 0.9342197 0.9147342 0.895120175
IstdDev 33.44348976 39.59021811 46.13351 45.80304032 0.8116158 0.7755714 0.7271306 0.713754647
Ientropy 99.47124917 98.94249835 97.752809 58.29477859 0.0105194 0.0209287 0.043956 0.588619403
Imean 0.8962327 0.882352941 0.8473232 0.8122935 2.8284271 2.828427125 2.645751311 3
IstdDev 0.720423 0.684071381 0.6146728 0.5710509 3.3166248 3.464101615 3.31662479 3.162278
Ientropy 0.0052875 0.010575017 0.0224719 0.4170522 3.7416574 3.464101615 3 3.464102
204
Table 1. Segmentation validation for Qui’s kernel.
AER DICE Sensitivity
Hausdroff
Distance
Original
Image 0.462657 0.997689 0.9986781 1.732050808
Qui's kernel 11.235955 0.9408901 0.8942498 2.645751311
Table 2. Features AER and DICE derived with different kernels
Table 3.Features sensitivity and HD derived with different kernels
Sensitvity HD
3X3 5X5 7X7 9X9 3X3 5X5 7X7 9X9
5. CONCLUSION
This paper presents an approach to quantitatively
assess the performance of segmentation of cyst in
breast ultrasound images using texture features and
Chan-vese level set method.
The experimental results show that error rates are
higher in texture features standard deviation and
entropy. The mean texture feature and Qui’s mask
gives less error rate and hence more accurate results.
ACKNOWLEDGMENTS
We would like to express our gratitude to Dr
Rajesh (JSS Hospital, Mysore), for his support in data
collection and valuable suggestions during the
development of this work. His willingness to give his
time so generously has been very much appreciated.
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