Topic
1-Computer Aided Detection of Solid Breast
Nodules: Performance Evaluation of Support Vector Machine and K- Nearest
Neighbor Classifiers
Abstract—Breast
Cancer is one of the major health concerns of women all over the world.
Computer Aided Detection (CAD) aids radiologists for the early detection of
abnormalities in the breast masses. Abnormalities in the breast may be
cancerous or non cancerous. This work proposes an effective CAD system that
considerably reduces the misclassification rates of these abnormalities. 60
mammogram images were taken and subjected to Segmentation and Feature
Extraction techniques. K-means clustering algorithm is employed for
segmentation and Fast Fourier Transform has been employed for the extraction of
features. The unique set of feature vectors is given to the classification
module. The classification of solid masses of breast nodule is done using
Supervised Classifiers Support Vector Machine (SVM) and K- Nearest Neighbor (K-
NN). The investigation reveals that SVM outperforms K- NN in terms of
sensitivity, specificity and accuracy.
Index Terms—Mammogram, Segmentation, K- means clustering, Feature Extraction, Fast
Fourier Transform, Support Vector Machine, K- Nearest Neighbor Classifier.
2-
Textural Features Based Computer Aided
Diagnostic System for Mammogram Mass Classification
Abstract— Computer Aided Diagnosis (CAD)
could be applied as a solution to reduce the chances of human errors and helps
Medical Practioners in the correct classification of Breast Masses. This paper
emphasizes an algorithm for the early detection of breast masses. Textural
analysis is one of the efficient methods for the early detection of
abnormalities. The paper enumerates an efficient Discrete Wavelet Transform
(DWT) algorithm and a modified Grey-Level Co-Occurrence Matrix (GLCM) method
for textural feature extraction from segmented mammogram images. Each tissue
pattern after classification is characterized into Benign and Malignant masses.
A total of 148 mammogram images were taken from Mini MIAS database and solid
breast nodules were classified into benign and malignant masses using
supervised classifiers. The classifier used is Radial Basis Function Neural
Network (RBFNN). The proposed system has a high potential for cancer detection
from digitized screening mammograms.
Index
Terms—Mammogram, Pre-processing, Feature
Extraction, Grey Level Co-occurrence Matrix, Discrete Wavelet Transform, Radial
Basis Function Neural Networks.
3-
A
non-extensive entropy feature and its application to texture classification
a
b s t r a c t This
paperproposesanewprobabilisticnon-extensiveentropyfeaturefortexturecharacterization,
based
onaGaussianinformationmeasure.Thehighlightsofthenewentropyarethatitisboundedby
finite limitsandthatitisnon-additiveinnature.Thenon-additivepropertyoftheproposedentropy
makes
itusefulfortherepresentationofinformationcontentinthenon-extensivesystemscontaining
some
degreeofregularityorcorrelation.Theeffectivenessoftheproposedentropyinrepresentingthe
correlatedrandomvariablesisdemonstratedbyapplyingitforthetextureclassification
problemsince
texturesfoundinnaturearerandomandatthesametimecontainsomedegreeofcorrelationor
regularity
atsomescale.Thegraylevelco-occurrenceprobabilities(GLCP)areusedforcomputingthe
entropyfunction.Theexperimentalresultsindicatehighdegreeoftheclassification
accuracy.The performance
ofthenewentropyfunctionisfoundsuperiortootherformsofentropysuchasShannon, Renyi,TsallisandPalandPalentropiesoncomparison.Usingthefeaturebasedpolarinteractionmaps
(FBIM) theproposedentropyisshowntobethebestmeasureamongtheentropiescomparedfor
representingthecorrelatedtextures.
4-
Content-based
Image Retrieval by Information Theoretic Measure
ABSTRACT
Content-based
image retrieval focuses on intuitive and efficient methods for retrieving
images from databases
based
on the content of the images. A new entropy function that serves as a measure
of information content in an
image
termed as ‘an information theoretic measure’ is devised in this paper. Among
the various query paradigms,
query
by example (QBE) is adopted to set a query image for retrieval from a large
image database. In this paper,
colour
and texture features are extracted using the new entropy function and the
dominant colour is considered as a
visual
feature for a particular set of images. Thus colour and texture features
constitute the two-dimensional feature
vector
for indexing the images. The low dimensionality of the feature vector speeds up
the atomic query. Indices
in
a large database system help retrieve the images relevant to the query image
without looking at every image
in
the database. The entropy values of colour and texture and the dominant colour
are considered for measuring
the
similarity. The utility of the proposed image retrieval system based on the
information theoretic measures is
demonstrated
on a benchmark dataset.
Keywords:
Image retrieval, fuzzy features,
descriptors, entropy, indexing
5-
A practical design of
high-volume steganography
in digital video files
Abstract In
this research, we consider exploiting the large volume of audio/video
data streams in compressed
video clips/files for effective steganography. By observing
that most of the
distributed video files employ H.264 Advanced Video Coding
(AVC) and MPEG Advanced
Audio Coding (AAC) for video/audio compression,
we examine the coding
features in these data streams to determine appropriate data
for modification so that
the reliable high-volume information hiding can be achieved.
Such issues as the
perceptual quality, compressed bit-stream length, payload of
embedding, effectiveness
of extraction and efficiency of execution will be taken into
consideration. First, the
effects of using different coding features are investigated
separately and three
embedding profiles, i.e. High, Medium and Low, which
indicate
the amount of payload,
will then be presented. The High profile is used to embed the
maximum amount of hidden
information when the high payload is the only major
concern in the target
application. The Medium profile is recommended since it is
designed to achieve a good
balance among several requirements. The Low profile is
an efficient
implementation for faster information embedding. The performances of
these three profiles are
reported and the suggested Medium profile can hide more
than 10%of the compressed
video file size in common Flash Video (FLV) files.
Keywords Steganography · H.264/AVC
·MPEG AAC· Information hiding
6-
Block
Matching Algorithms
For Motion Estimation
Abstract—This paper is a
review of the block matching
algorithms used for motion estimation in
video compression. It
implements and compares 7 different
types of block matching
algorithms that range from the very
basic Exhaustive Search to
the recent fast adaptive algorithms like
Adaptive Rood Pattern
Search. The algorithms that are
evaluated in this paper are
widely accepted by the video compressing
community and have
been used in implementing various
standards, ranging from
MPEG1 / H.261 to MPEG4 / H.263. The
paper also presents a
very brief introduction to the entire
flow of video compression.
Index Terms— Block
matching, motion estimation, video
compression,
MPEG, H.261, H.263
7-
Visual Cryptography Scheme for Color
Image Using Random Number
with
Enveloping by Digital Watermarking
Abstract
Visual
Cryptography is a special type of encryption technique to
obscure
image-based secret information which can be decrypted
by
Human Visual System (HVS). This cryptographic system
encrypts
the secret image by dividing it into n number of shares
and
decryption is done by superimposing a certain number of
shares(k)
or more. Simple visual cryptography is insecure
because
of the decryption process done by human visual system.
The
secret information can be retrieved by anyone if the person
gets
at least k number of shares. Watermarking is a technique to
put
a signature of the owner within the creation.
In
this current work we have proposed Visual Cryptographic
Scheme
for color images where the divided shares are enveloped
in
other images using invisible digital watermarking. The shares
are
generated using Random Number.
Keywords: Visual Cryptography, Digital Watermarking,
Random Number.
8-
Image Compression Using Discrete Wavelet
Transform
Abstract:
This Project presents an approach towards MATLAB implemention of the Discrete
Wavelet Transform (DWT) for image compression. The design follows the JPEG2000
standard and can be used for both lossy and lossless compression. In order to
reduce complexities of the design linear algebra view of DWT has been used in
this concept.With the use of more and more digital still and moving images,
huge amount of disk space is required for storage and manipulation purpose. For
example, a standard 35-mmphotograph digitized at 12μm per pixel requires about
18 Mbytes of storage and one second of NTSC-quality color video requires 23
Mbytes of storage. JPEG is the most commonly used image compression standard in
today’s world. But researchers have found that JPEG has many limitations. In
order to overcome all those limitations and to add on new improved features,
ISO and ITU-T has come up with new image compression standard, which is
JPEG2000
9
Artificial
Bee Colony Data Miner (ABC-Miner)
Abstract—Data mining
aims to discover interesting, non-trivial,
and meaningful
information from large datasets. One of the data
mining tasks is
classification, which aims to assign the given
datasets to the
most suitable classes. Classification rules are used
in many domains
such as medical sciences, banking, and
meteorology.
However, discovering classification rules is
challenging due
to large size and noisy structure of the datasets,
and the
difficulty of discovering general and meaningful rules. In
the literature,
there are several classical and heuristic algorithms
proposed to mine
classification rules out of large datasets. In this
paper, a new and
novel heuristic classification data mining
approach based
on artificial bee colony algorithm (ABC) was
proposed
(ABC-Miner). The proposed approach was compared
with Particle
Swarm Optimization (PSO) rule classification
algorithm and
C4.5 algorithm using benchmark datasets. The
experimental
results show the efficiency of the proposed method.
Keywords:
Artificial bee colony, Classification, Rule learning, Data
mining,
ABC-Miner.
10
FACIAL
EXPRESSION RECOGNITION USING PRINCIPAL
COMPONENT ANALYSIS
ABSTRACT
Facial expressions play an
important role in human
communication. The contours of
the mouth, eyes and
eyebrows play an important role
in classification. Eigen
faces are used to classify facial
expression. It has been
assumed that, facial expression
can be classified into
some discreet classes (like
happiness, sadness, disgust,
fear, anger and surprise) whereas
absence of any
expression is the “Neutral”
expression. Intensity of a
particular expression can be
identified by the level of its
“dissimilarity” from the Neutral
expression.
Keywords-
Principal component, edge detection,
feature
extraction, segmentation
11
Tracking TetrahymenaPyriformis
Cells using Decision Trees
Abstract
Matching cells over time has long
been the most difficult
this problem by recasting it as a
classification problem.
We construct a feature set for
each cell, and compute a
feature difference vector between
a cell in the current
frame and a cell in a previous
frame. Then we determine
whether the two cells represent
the same cell over
time by training decision trees
as our binary classifiers.
With the output of decision
trees, we are able to formulate
an assignment problem for our
cell association task
and solve it using a modified
version of the Hungarian
algorithm.
