International Journal of Engineering Trends and Technology (IJETT) – Volume 12 Number 2 - Jun 2014
A Review: Image Segmentation and Medical
Diagnosis
1
Nitesh Sharma, 2 Sheenam, 3Amit chhabra
Department of Computer Science & Engineering
Swami Devi Dayal Institute of Engineering & Technology-Barwala
Panchkula, India
Abstract: Image processing plays a key role in computer aided diagnosis and medical practice, especially in detection of various tumors.
Image Segmentation procedures partition an images into its constituent parts or objects. In general, autonomous segmentation is one of the
most difficult tasks in digital image processing. Several general-purpose algorithms and techniques have been developed for image
segmentation. Firstly, this paper describes the segmentation and use of Otsu’s Thresholding technique to detect the boundaries of the object
whose boundaries are not defined. Then it describes the use of neural netwoks in medical diagnosis.
Keywords: Otsu’s Thresholding , segmentation, Computer Aided Diagnosis, Feed forward neural network.
III.SEGMENTATION TECHNIQUES
I. INTRODUCTION
An image is an array, or a matrix, of square pixels (picture
elements) arranged in columns and rows [6].
Segmentation is the process of partitioning a digital
image into multiple segments (sets of pixels, also known as
super pixels). The goal of segmentation is to simplify and/or
change the representation of an image into something that is
more meaningful and easier to analyze The neural can be used
to extract patterns and detect that are too complex to be
noticed by either humans or other computer
techniques. Neural networks are typically perform in layers.
Layers are made up of a number of interconnected with
'nodes' which contain an 'activation function'. Patterns are
presented to the network via the 'input layer’ that
communicates to one or more 'hidden layers' where the actual
processing is done via a system of weighted 'connections'. The
hidden layers then connected to an 'output layer'. Section II
describes the various segmentation techniques, Section IV
gives the introduction to Otsu’s Thresholding technique and
rest of the paper gives its working and application of neural
network in medical diagnosis.
II. IMAGE SEGMENTATION
Image segmentation is the process of dividing an images into
multiple parts. This is typically used to identify objects or
other relevant information in digital images. It is the process
by which an image is subdivided into its different parts.
Level of segmentation is depends on the level of requirement
of objects. Segmentation has two objectives. The first
objective is to decompose the images into parts for further
analysis.
The second objective of segmentation is to perform a
change of representation. The pixels of the image must be
organized into higher-level units that are either more
meaningful or more efficient for further analysis (or both).
ISSN: 2231-5381
Most of the image segmentation algorithms are based on one
of the two basic properties of intensity values: discontinuity
and similarity.
• Detecting Discontinuities
It means to partition an image based on abrupt changes in
intensity [10], this includes image segmentation algorithms
like edge detection.
• Detecting Similarities
It means to partition an image into regions that are similar
according to a set of predefined criterion [10], this includes
image segmentation algorithms like thresholding, region
growing, region splitting and merging.
A. Segmentation Based on Edge Detection
This method attempts to resolve image segmentation by
detecting the edges or pixels between different regions that
have rapid transition in intensity are extracted [1] and linked
to form closed object boundaries. The result is a binary image
[2]. Based on theory there are two main edge based
segmentation methods- gray histogram and gradient based
method [3].
1) Gray Histogram Technique: The result of edge detection
technique depends mainly on selection of threshold T, and it
is really difficult to search for maximum and minimum gray
level intensity because gray histogram is uneven for the
impact of noise, thus we approximately substitute the curves
of object and background with two conic Gaussian curves
[3], whose intersection is the valley of histogram. Threshold
T is the gray value of intersection point of that valley[10].
2) Gradient Based Method: Gradient is the first derivative for
image f(x, y), when there is abrupt change in intensity near
edge and there is little image noise, gradient based method
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International Journal of Engineering Trends and Technology (IJETT) – Volume 12 Number 2 - Jun 2014
works well [4]. This method involves convolving gradient
operators with the image. High value of the gradient
magnitude is possible place of rapid transition between two
different regions. These are edge pixels, they have to be
linked to form closed boundaries of the regions [10].
Edge detection methods requires a balance between
detecting accuracy and noise immunity in practice, if the
level of detecting accuracy is too high, noise may bring in
fake edges making the outline of images unreasonable and if
the degree of noise immunity is too excessive [4], some parts
of the image outline may get undetected and the position of
objects may be mistaken. Thus, edge detection algorithms are
suitable for images that are simple and noise-free as well
often produce missing edges or extra edges on complex and
noisy images [2].
D. Segmentation Method Based on Partial Differential
Equation
Using a PDE based method & solving the PDE equation by a
numerical scheme one can segment the image. Image
segmentation based on PDEs is mainly carried out by active
contour model or snakes. This method was first introduced by
Kass et al in 1987 [4]. Kass developed this method to find
familiar objects in presence of noise and other ambiguities.
The central idea of snake is transforming a segmentation
problem into a PDE framework[10].
That is, the evolution of a given curve, surface or
image is handled by PDEs and the solution of these PDEs is
what we look forward to various methods for image
segmentation are - snake, level set and Mumford-shah
model[2].
B. Thresholding Method
IV. Threshholding Method
Thresholding technique is based on imagespace regions i.e.
on characteristics of image [3]. Thresholding operation
convert a multilevel image into a binary image i.e., it choose
a proper threshold T, to divide image pixels into several
regions and separate objects from background. Any pixel (x,
y) is considered as a part of object if its intensity is greater
than or equal to threshold value i.e., f(x, y) ≥T, else pixel
belong to background [1].
Limitation of thresholding method is that, only two
classes are generated, and it cannot be applied to
multichannel images. In addition, thresholding does not take
into account the spatial characteristics of an image due to this
it is sensitive to noise [3], as both of these artifacts corrupt
the histogram of the image, making separation more difficult.
Otsu’s method is aimed in finding the optimal value for the
global threshold. An image is a 2D grayscale intensity
function, and contains N pixels with gray levels from 1 to L.
The number of pixels with gray level i is denoted fi, giving a
probability of gray level i in an image of pi = fi / N.
It is based on the interclass variance maximization. Well
thresholded classes have well discriminated intensity values.
M × N image histogram:
L intensity levels, [0, . . . , L − 1];
ni #pixels of intensity i :
C. Region Based Segmentation Methods
Compared to edge detection method, segmentation
algorithms based on region are relatively simple and more
immune to noise [3]. Edge based methods partition an image
based on rapid changes in intensity near edges whereas
region based methods, partition an image into regions that are
similar according to a set of predefined criteria [1].
Normalized histogram:
1) Region Growing: Region Growing is a procedure that
groups pixels or sub regions into larger regions based on
predefined criteria for growth. The basic approach is to start
with a set of “seed” points and from these grow regions by
appending to each seed those neighbouring pixels that have
predefined properties similar to the seed [10].
2) Region Splitting and Merging: Rather than choosing seed
points, user can divide an image into a set of arbitrary
unconnected regions and then merge the regions [3] in an
attempt to satisfy the conditions of reasonable image
segmentation. Region splitting and merging is usually
implemented with theory based on quad tree data[10].
Figure 1: Thresholding methods such as Otsu’s method[3]
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International Journal of Engineering Trends and Technology (IJETT) – Volume 12 Number 2 - Jun 2014
VI. MEDICAL IMAGE SEGMENTATION
V. Artificial Neural Network
The neural can be used to extract patterns and detect that are
too complex to be noticed by either humans or other
computer techniques. Neural networks are typically
performed in layers. Layers are made up of a number of
interconnected with 'nodes' which contain an 'activation
function'[3]. Patterns are presented to the network via the
'input layer’ that communicates to one or more 'hidden layers'
where the actual processing is done via a system of weighted
'connections'. The hidden layers then connected to an 'output
layer'. Both feed forward and feed forward back propagation
neural networks are used for classification [6].
1) Feed forward neural network: In a feed forward neural
networks information always moves in one direction only,
there is no any feedback. The information moves forward
from input layer through hidden layer to the output layer. The
networks are used are Hebb, Perceptron, Ada-line and
Madaline networks in feed forward. In Hebb network we
done modification of the weights of the neurons. The weights
information are used by neural network to solve problems. If
two interconnected neurons are both ‘on’ at the same time,
then the weight between those neurons are increased [10].The
Perceptron network is classifier for classifying the input into
one of two possible outputs. It is a type of linear classifier.
The classification algorithm makes its action based on linear
predictor function combining a set of weights with the feature
vector describing a given input. Both bias and threshold
technique are needed in this network. The Adaline(Adaptive
Linear Neuron) network uses bipolar activations for its input
data and target output. The weights on the connection from
the input units to the adaline network are adjustable. The
network has a bias, which perform like an adjustable weight
on a connection from a unit whose activation code is always
1. A Madaline network consists of adalines arranged in a
multi-layer net. It is a two layer neural network with a set of
adalines in parallel as its input layer and a single processing
element in its output layer[6].
2) Feed forward back propagation neural network: The
back propagation is a systematic method of training
multilayer neural networks in a supervised manner. The back
propagation method, also known as the error back
propagation algorithm, is based on the error-correction
learning rule. The back propagation network consists of at
least three layers of units: an input layer, at least one
intermediate hidden layer and one output layer. The units are
connected in feed forward fashion with inputs units
connected to the hidden layer units and the hidden layer units
are connected to the output layer units. An input pattern is
forwarded to the output through input to hidden and hidden to
output weights. The output of the network is the classification
decision[3].
ISSN: 2231-5381
Medical imaging is a group of examinations, techniques and
processes that use different types of physical effects to
visualize anatomical structures and pathological changes
inside the human body [1]. Medical Image Segmentation is
the process of automatic or semi-automatic detection of
boundaries within a 2D or 3D image. A major difficulty of
medical image segmentation is the high variability in medical
images. First and foremost, the human anatomy itself shows
major modes of variation. Furthermore many different
modalities (X-ray, CT, MRI, microscopy, PET, SPECT,
Endoscopy, OCT, and many more) are used to create medical
images. The result of the segmentation can then be used to
obtain further diagnostic insights. Possible applications
are automatic measurement of organs, cell counting, or
simulations based on the extracted boundary information.
Image segmentation remains a difficult task,
however, due to both the tremendous variability of object
shapes and the variation in image quality. In particular,
medical images are often corrupted by noise and sampling
artifacts, which can cause considerable difficulties when
applying classical segmentation techniques such as edge
detection and thresholding. As a result, these techniques
either fail completely or require some kind of post processing
step to remove invalid object boundaries in the segmentation
results. Segmentation of brain is shown in Figure 2.
Figure 2: Medical image segmentation [6]
VI. CONCLUSION
In this study, the overview of various segmentation
methodologies applied for digital image processing is
describe briefly. The study also reviews the research on
various research methodologies applied for image
segmentation and various research issues in this field of
study. This study aims to provide a simple guide to the
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International Journal of Engineering Trends and Technology (IJETT) – Volume 12 Number 2 - Jun 2014
researcher for those carried out their research study in the
image segmentation.
The basic need of this research is to find out such an efficient
method to detect the lung nodules at the earlier stage so that
chances of survival can be increased. For this purpose I have
been studied the different techniques. There is no legacy in
the previous work. There is no 100% accuracy even in
doctors results, not even the computer aided system gives 100
% accurate results but it is more accurate the doctors analysis
according to the researchers. Researchers For this purpose
fuzzy logic based algorithm are being used in my research
work. My primary aim is to present a better algorithm so that
to increase the accuracy of detection and reduce processing
time.
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