26-09-2014, 03:24 PM
An Improved Support Vector Machine Kernel for Medical Image Retrieval System Project Report
An Improved Support.pdf (Size: 145.34 KB / Downloads: 98)
Abstract
Digital medical images take up most of the storage
space in the medical database. Digital images are in the form of
X-Rays, MRI, CT. These medical images are extensively used in
diagnosis and planning treatment schedule. Retrieving required
medical images from the database in an efficient manner for
diagnosis, research and educational purposes is essential. Image
retrieval systems are used to retrieve similar images from
database by inputting a query image. Image retrieval systems
extract features in the image to a feature vector and use
similarity measures for retrieval of images from the database. So
the efficiency of the image retrieval system depends upon the
feature selection and its classification. In this paper, it is
proposed to implement a novel feature selection mechanism using
Discrete Sine Transforms (DST) with Information Gain for
feature reduction. Classification results obtained from existing
Support Vector Machine (SVM) is compared with the proposed
Support Vector Machine model. Results obtained show that the
proposed SVM classifier outperforms conventional SVM
classifier and multi layer perceptron neural network.
INTRODUCTION
Digital images provide visual information required for
diagnosis and progress in medical treatment. Image retrieval of
digital medical images from archives is a challenge that is
widely researched. Textual annotations of images were the
basis on which images were retrieved during the early 80s [1,
2]. Semantic queries were used for retrieving images. Textual
annotations of the medical images were manually done using
keywords. Retrieval was based on these keywords. Manually
annotating using keywords involves a huge amount of manual
labor and with the increasing volume of digital images stored,
it is not feasible. Thus, the need to retrieve images based on the
content of the image rather than using metadata such as
keywords, and tags for efficient use of medical database. A
medical retrieval system which can automatically classify
images based on the features of the image and retrieve images
based on query image is required. Earlier works in literature
include use of visual features with text annotation for image
retrieval [3, 4].
RESEARCH METHOD
This section briefly introduces to Discrete Sine Transform
(DST), Information Gain (IG) and the proposed Support Vector
Machine (SVM).
A. Discrete Sine Transform (DST )
The feature vector from each image was extracted using the
discrete sine transform. Pixels which are one length away
from each other are selected. The algorithm pseudo is given
below:
1. Compute Image size MxN
2. for each alternate value 'i' in array M and array size
less than M or M+1
3. for each alternate value 'j' in array N and array size
less than N or N+1
4. compute DST(array[xi, yj])
5. Store computed value in one dimensional array
6. Repeat from step 1 till all images are computed
Multilayer Perceptron (MLP)
Multilayer perceptron (MLP) is the most favored
supervised learning network model. The neural network
consists of an input layer, one or more hidden layer and an
output layer. Connections between the layers are typically
formed by connecting each of the nodes from a given layer to
all neurons in the next layer. During the training phase each
connection’s scalar weight is adjusted. The outputs are got
from the output nodes. The feature vector x is input at the input
layer and the output represents a discriminator between its class
and all of the other classes. In training, the training examples
are fed and the predicted outputs are computed. The output is
compared with the target output and error measured is
propagated back through the network and the weights are
adjusted
PROPOSED SVM
Support vector machine (SVM) is a linear machine which
constructs a hyperplane as a decision surface. It is based on
the method of structural risk minimization; the error rate is
bound by the sum of the training-error rate and a term that
depends on the Vapnik-Chervonenkis (VC) dimensions. The
SVM provides good generalization performance on pattern
classification. The principle of SVM algorithm is based on the
inner-product kernel between a “support vector” xi and the
vector x drawn from the input vector.
In this paper it is proposed to modify the existing polykernel
of Support Vector Machine (SVM) [15]. The proposed
Gaussian poly kernel of SVM, GPK-SVM is derived as
follows. The function Kxy (, ) is a kernel function if it
satisfies (6)
CONCLUSION
In this paper it was proposed to extract features using
Discrete Sine Transform (DST) and select the top 50 attributes
based on class attribute using information gain. The extracted
features were trained and classified with SVM using poly
kernel. A novel SVM was proposed and the classificationaccuracy of the proposed method improves by a factor of 5.18.
The reduced features in the proposed method, decreases the
overall processing time for a given query input image.