Biomedical Image Retrieval using LBWP

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 04 Issue: 09 | Sep -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 839
BIOMEDICAL IMAGE RETRIEVAL USING LBWP
Joyce Sarah Babu1, Soumya Mathew2, Rini Simon3
1Dept. of Computer Science Engineering, M.Tech student, Viswajyothi College of Engineering and Technology,
Kerala, India
2,3 Dept. of Computer Science Engineering, Assistant professor, Viswajyothi College of Engineering and Technology,
Kerala, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - A new design of feature descriptor is proposed
in this paper, which could be used in retrieval, analysis or
recognition purposes. This paper mainly focus on retrieval
of biomedical images, i.e. CT and MRI images. The new
feature descriptor is LBWP (Local Bit-Plane Wavelet
Pattern). In this a wavelet function is applied onto
different bit planes of an image, thus capturing fine and
coarse details present in an image. The wavelet function
would help in encoding the inter and intra pixel
relationship in an image. Thus guaranteeing effective
retrieval rate and efficient performance. The training and
testing data where taken randomly from TCIA database.
The experimental results shows a promising efficiency.
Key Words: LBWP, Bit-plane, Wavelet, Pixel, TCIA
I. INTRODUCTION
Content based image retrieval has always been a
vast research topic for many years. In the field of medical
analysis, there is no chance for any kind of failure or
error, as it may cost lives of patients. The factor of
accuracy stands so important in life and death decisions.
Content based image retrieval (CBIR) may play a major
role in disease diagnosis, tutorial and research purposes.
But due to increase of population, the number of disease
subject also increases. As a result there is always a need
of efficient and accurate image retrieval measure. Hence,
it would help in searching, indexing and retrieving
methods.
Moreover, when the similarity measure comes
to medical images, the values may be varying so less, for
they all seem similar. The values may show a slight
variation, due to presence of tumor, or due to a small
lesion or cut. But it is extremely important to find the
specific problem for whatever the reason is. Because as
the time delays, the chance of patient’s survival decrease.
This paper mainly focus on CT and MRI images.
The basic instinct of CBIR is based on color,
texture, shape, structure etc. The detailed description of
literature survey is mentioned in [13-18]. CBIR usually,
is based on pattern recognition, thus captures image’s
detailed information. Using the retrieval system, doctors
could recognise the disorder or could retrieve the most
similar report from the medical histories.
The principle behind any skeleton system for
retrieval is the measurement of similarity amoung the
image. The similarity between the index image and
images from the databases are measured. This similarity
measurement is done with the help of feature vectors.
The feature vector generates a value for a particular
image. This value is checked over the entire database to
find the similarity. The performance and effectiveness of
the system is heavily depended on this feature vectors.
In this paper, we present a method that uses
wavelet function in each layer of images. This layering is
based on depth of the bit planes. Each bit planes captures
different information based on the depth of images. The
new proposed system, is expected to have the efficiency
along with information by capturing all fine and coarse
details. Thus the encoded information, and improvised
wavelet function is confined together to provide hike in
accuracy, compared to other existing system.
II. RELATED WORK
Many researches and studies are conducted on feature
vectors and retrieval systems. In image processing, the
information of images are encoded in many possible
manners. This information encoding can be done using
feature vectors. Feature vectors are the basic unit of any
systems used for classification, analysis, recognition and
retrieval. Without these feature vectors, the system built,
would be a body without soul.
An extensive literature survey on content based image
retrieval is done in [13-18]. The revolution in usage of
feature vector started with introduction of LBP (local
binary pattern) by Ojala et al [8] due to reduced
complexity. Depending on this concept a lot of variants of
LBP evolved. Other generalised concept that gained a lot
of attention include LTP [9], LTrP [19], Local Diagonal
Extrema Pattern [18], LMeP [10] etc.
An example of generalization of the LBP, is local
ternary pattern (LTP) [9] for face recognition under
changing lighting conditions. Local mesh patterns

(LMeP) [10], peak valley edge patterns (PVEP) [11], and
local mesh PVEP [12] are other state-of-art descriptors
proposed for the biomedical image retrieval. Dubey et al.
introduced LTrP (Local Tetra Patterns)[19] and Local
Diagonal Extrema Pattern [18] for the retrieval of CT
images.
Depending on the existence of local structure, in
MR images of brain, a retrieval method was proposed by
Unay et al [5]. In creation of LWP (Local Wavelet
Pattern) by Dubey et al. [1] a wavelet function is used to
encode relationship between centre pixel and
neighboring pixel. Similarly in LBDP (Local Bit-Plane
Decoded Pattern) various bit planes are evaluated to
include more image information [2].
After the detailed study, an idea of system along
with more information in an encoded manner with all
intra- and inter-pixel relationship was idealised. Thus a
wavelet function is applied locally on a central pixel and
neighboring pixels, on different bit planes, to store more
information. The paper illustrates the system design, and
includes the performance evaluation and concluding
remarks as follows.
III. PROPOSED SYSTEM FRAMEWORK
In the area of biomedical images and their
retrieval, it is found that encoding the relationship of
pixel's intensity is very important. The retrieval can be
effectively done, if more information about a particular
pixel or it's neighbors are included. In most of the
methods the intensity value of pixel is taken directly
without much transformations.
After analysing a few works, these pixel
intensity values were undergone some mathematical
transformation. The proposed system is a design of new
feature descriptor, LBWP (Local Bit-Plane Wavelet
Pattern) for the retrieval of biomedical images. It aims at
defining a powerful feature vector for efficient retrieval
of biomedical images and also to encode the maximum
relationship information of central and neighboring
pixels. The proposed system also focus on, transformed
intensity values of each pixel in separate bit planes. The
architecture of system is as shown in Fig 1. Along with
this, an SVM classifier is introduced to distinguish if the
query image is CT or MRI from the database.
The principle idea is to include more
information about the relationship between the intensity
of central pixel and neighboring pixel. For this, each pixel
of image is decomposed to several bit planes depending
upon it's depth. The LBWP (Local Bit-plane Wavelet
Pattern) encodes the relationship among neighbors in
each bit-plane separately, using local bit-plane
transformation which generates the local bit-plane
transformed values and then encodes the relationship of
centre pixel with each transformed values. It then
applies the wavelet function to them thus generating a
new feature descriptor.
Local Neighborhood extraction: In order to compute
feature vector, it is assumed that a centre pixel have
equally spaced neighboring pixels. The nearest ones
amoung them are taken into consideration. They are
assumed to be in a circular manner,
Hence the coordinates of these pixels are converted from
polar system to cartesian ones[1].
Fig 1. Proposed system framework of LBWP for
biomedical image retrieval.
Fig 2 shows the skeleton of a basic unit of local
neighborhood, along with the variables used to refer
them. Let M be an image of dimension m1 x m2. Then P
be the pixel at (i,j) coordinate. N will be the total number
of neighbors surrounding the central pixel and t is used
to denote a particular pixel. R be the radius distance
between central and neighbouring pixel. A particular
pixel in (i,j) coordinate surrounded by N neighbours, at
distance R from central pixel is represented as . The
intensity of this Pixel is represented as
Fig 2: Skeleton of basic unit of local neighborhood

Bit-plane Decomposition: The local bit-plane
decomposition step is performed to separate each bit-
plane of the local neighboring structure of pixel, where k
is the positive integer having values between 1 and N.
the local bit plane decomposition step yields the binary
values in each bit plane and is applied over only
neighbors . Note that this step is not applied over the
centre pixel . Certain functional changes are applied
to the intensity of centre pixel, in order to obtain,
intensity values of neighbors[2].
Bit-plane Transformation: The concept of local bit-
plane transformation, which captures the local
information in each bit-plane separately, with lower and
higher bit-plane to capture the fine and coarse details
respectively. The idea is to generate a binary pattern
called as local bit plane decoded pattern by exploring the
relation between intensity value of a centre pixel with
the local bit-plane transformed values for each bit-plane.
The local bit plane transformed values for a particular bit
plane is computed by summing up of each weight values
using values in that plane.
∑ 2.1
Bit-plane Decoding: Bit plane coding generate a binary
pattern called as local bit-plane decoded pattern. This is
generated by exploring the relation between of a centre
pixel with the local bit-plane transformed values for each
bit-plane. The intensity values of pixels are used to
explore or to identify the relationship between the pixels
of an image. The decomposed and transformed values
are combined together to obtain a single value with
which new feature descriptor is calculated. Here signed
function is applied to the difference of intensity of centre
pixel and local bit plane transformed values[2].
Local Wavelet Decomposition: For wavelet
decomposition 1 D Haar wavelet function is used. It is
function of intensity of neighboring pixels and recursive
basis function[1].
Central Pixel Transformation: Here the encoding of
relationship of central pixel and neighboring ones are
done. After wavelet decomposition the range of wavelet
has changed. To perform any mathematical operation on
these functions, their ranges must be matched. Thus
range matching is the role of centre pixel transformation
[1].
Local Wavelet Pattern: Wavelet function and centre
pixel transformed values are used to encode relation
between centre and neighboring pixels. It is done by
using signed function of the difference between wavelet
function and centre pixel transformed values. Thus a
binary value is originated which will be used for creation
of local wavelet pattern map.
LBWP Feature Generation:
Local wavelet pattern map is generated for each pixel
that surrounds the centre pixel. Similarly local bit plane
decoded value for each pixel that surrounds the centre
pixel is calculated. Both of them are combined together
to form local bit-plane wavelet pattern.
IV. RESULTS AND DISCUSSIONS
The performance measures for the proposed
system are evaluated relevant to the performance
parameters ARP (Average Retrieval Precision), ARR
(Average Retrieval Rate) and F-score. The term peak
ARP (Average Retrieval Precision) is an expression for
the ratio of the number of relevant images to the number
of images retrieved. Similarly ARR (Average Retrieval
Rate) is an expression for the ratio of the number of
relevant images retrieved to the number of relevant
images in database [7]. The database considered for
performance evaluation is TCIA (The Cancer Image
Archive). The images for training and testing were
downloaded from TCIA.
Performance evaluation was conducted on a
number of CT and MRI images to verify the effectiveness
of the proposed scheme. All experiments were
implemented on a computer with an intel i5 core
processor, 4.00 GB memory, Windows operating system
and the programming environment was MATLAB 2013.
Several set of images are chosen as test images. The
parameters such as precision, recall and F-score are
checked. . In simple words, high precision means that
retrieved images consists of more relevant images, and
high recall means most of the retrieved ones are
relevant.

Fig 3: Examples of CT images from TCIA database
Each database image of database is considered
as a query image and matched with all images. The top
matching images are retrieved based on distance
measure. The results of the performance analysis are
shown in the table below. The input image where
randomly chosen by the user from test cases. And the
results from the system proved that the proposed system
has a better result compared with the existing system.
Fig 4: Examples of MRI images from TCIA database
The LWP feature vector has already proven to
be better than existing methods [1] during testing. Hence
we consider only comparison of LBWP with LWP. As we
can see, all ARP, ARR and F-Score values are higher for
LBWP, it is clear that it outperforms LWP. We can also
notice that the recall rate is so close to 100%.
Table 1: Performance evaluation of LBWP and LWP
feature vectors on selected dataset
V. CONCLUSIONS
We proposed a new local Bit-plane Wavelet Pattern
based image feature descriptor in this paper for
biomedical image retrieval. LBWP, which gave us an
incredible performance on retrieval rate. It encoded all
kinds of fine and coarse details in different bit planes
along with an application of wavelet function between
central and neighboring pixels. The wavelet function was
applied locally over a central pixel and neighboring ones.
In order to test this descriptor we downloaded some
images from The Cancer Imaging Archive (TCIA) that
formed the training and testing dataset. The
performance was measured using ARP, ARR and F-Score.
It is seen that, the LBWP outperformed the LWP, which
was the best, compared to all existing methods on basis
of retrieval rate.
VI. FUTURE WORKS
The feature descriptor can also be used in retrieval of
non-medical images. In image processing, LBWP feature
vector can also be used in all analysis, recognition and
retrieval systems
REFERENCES
[1] Shiv Ram Dubey, Satish Kumar Singh and Rajat
Kumar Singh,”Local Wavelet Pattern: A New Feature
Descriptor for Image Retrieval in Medical CT
Databases”, IEEE Transactions on Image Processing,
vol.21. January 2012.
[2] Shiv Ram Dubey, Satish Kumar Singh, “Local Bit-
Plane Decoded Pattern: A Novel Feature Descriptor
For Biomedical Image Retrieval”, IEEE Journal Of
Biomedical And Health Informatics, vol. 20, no.4 July
2016.
[3] Issam El-Naqa, Yongyi Yang, Nikolas P.
Galatsanos, Robert M. Nishikawa, and Miles N.
Wernick, “A Similarity Learning Approach to
Content-Based Image Retrieval: Application to
Digital Mammography”, IEEE Transactions Medical
Imaging, vol. 23, no. 10 October 2004.
[4] Md Mahmudur Rahman, Sameer K. Antani, and
George R. Thoma, “A Learning-Based Similarity
Fusion and Filtering Approach for Biomedical Image
Retrieval Using SVM Classification and Relevance
Feedback”, IEEE Transactions Information
Technology In Biomedicine, Vol. 15, No. 4 July 2011.
[5] Devrim Unay, Ahmet Ekin, and Radu S. Jasinschi,
“Local Structure-Based Region-of-Interest Retrieval
in Brain MR Images”, IEEE Trans actions On

Information Technology In Biomedicine, Vol. 14, No.
4 July 2010.
[6] The Cancer Imaging Archive (TCIA)(2014-2016),
Retrieved from
http://www.cancerimagingarchive.net
[7] T. Ojala, M. Pietikäinen, and T. Mäenpää,
“Multiresolution gray-scale and rotation invariant
texture classification with local binary patterns,”
IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7,
pp. 971–987, Jul. 2002.
[8] X. Tan and B. Triggs, “Enhanced local texture
feature sets for face recognition under difficult
lighting conditions,” IEEE Trans. Image Process., vol.
19, no. 6, pp. 1635–1650, June 2010.
[9] L. Yang et al., “A boosting framework for
visuality-preserving distance metric learning and its
application to medical image retrieval,” IEEE Trans.
Pattern Anal. Mach. Intell., vol. 32, no. 1, pp. 33–44,
Jan. 2010.
[10] I. El-Naqa, Y. Yang, N. P. Galatsanos, R. M.
Nishikawa, and M. N. Wernick, “A similarity learning
approach to content-based image retrieval:
Application to digital mammography,” IEEE Trans.
Med. Imag., vol. 23, no. 10, pp. 1233–1244, Oct. 2004.
[11] B. André, T. Vercauteren, A. M. Buchner, M.
B.Wallace, and N. Ayache, “Learning semantic and
visual similarity for endomicroscopy video
retrieval,” IEEE Trans. Med. Imag., vol. 31, no. 6, pp.
1276–1288, Jun. 2012.
[12] Joyce Sarah Babu and Soumya Mathew , “Survey
on Various Biomedical CBIR Methods”, International
Journal of Advanced Research in Computer Science
and Software Engineering, vol. 7, no. 1, January
2017.
[13] A. W. M. Smeulders, M. Worring, S. Santini, A.
Gupta, and R. Jain, “Content-based image retrieval at
the end of the early years,” IEEE Trans. Pattern Anal.
Mach. Intell., vol. 22, no. 12, pp. 1349–1380, Dec.
2000.
[14] Y. Liu, D. Zhang, G. Lu, and W.-Y. Ma, “A survey
of content-based image retrieval with high-level
semantics,” Pattern Recognit., vol. 40, no. 1, pp. 262–
282, 2007.
[15] X. Xu, D. J. Lee, S. Antani, and L. R. Long, “A spine
X-ray image retrieval system using partial shape
matching,” IEEE Trans. Inf. Technol. Biomed., vol. 12,
no. 1, pp. 100–108, Jan. 2008.
[16] H. C. Akakin and M. N. Gurcan, “Content-Based
microscopic image retrieval system for multi-image
queries,” IEEE Trans. Inf. Technol. Biomed., vol. 16,
no. 4, pp. 758–769, Jul. 2012.
[17] M. M. Rahman, S. K. Antani, and G. R. Thoma, “A
learning-based similarity fusion and filtering
approach for biomedical image retrieval using SVM
classification and relevance feedback,” IEEE Trans.
Inf. Technol. Biomed., vol. 15, no. 4, pp. 640–646, Jul.
2011.
[18] S. R. Dubey, S. K. Singh, and R. K. Singh, “Local
diagonal extrema pattern: A new and efficient
feature descriptor for CT image retrieval,” IEEE
Signal Process. Lett., vol. 22, no. 9, pp. 1215–1219,
Sep. 2015.
[19] Subrahmanyam Murala, R. P. Maheshwari and R.
Balasubramanian,” Local Tetra Patterns: A New
Feature Descriptor for Content-Based Image
Retrieval”, IEEE transactions on image processing,
vol. 21, no. 5, may 2012

Biomedical Image Retrieval using LBWP

More Related Content

What's hot (20)

Similar to Biomedical Image Retrieval using LBWP (20)

More from IRJET Journal (20)

Recently uploaded (20)

Biomedical Image Retrieval using LBWP