International Journal of Advance Research, IJOAR .org ... · PDF fileflowchart are also used to show analyze how the image segmentation are ... of seeds as input along with the

International Journal of Advance Research, IJOAR .org

Volume 1, Issue 4, April 2013, ISSN 2320-9119

MEDICAL IMAGE SEGMENTATION (MIS) USING CELLULAR NEURAL NETWORK (CNN)

Oloruntoyin Sefiu Taiwo & Adeyanju Ibrahim Adepoju Department of Computer Science & EngineeringFaculty of Engineering & TechnologyLadoke Akintola University of Technology, Ogbomoso, NigeriaE-mail: [email protected]

ABSTRACT

Cellular neural network is an indispensable in the segmentation of an image. Over the last decade, numerous methods for the segmentation of images has been developed within different theoretical frameworks. Our main aim in this paper is to make use of the method of cellular neural network in image segmentation. We would classify the images and show each step of how the images is been segmented. Furthermore, methods like the Compression based method, histogram based method, region growing method and spilt and merge method have been used for image segmentation in this paper, histogram based method is the most efficient method of all. Also, in this work, cellular neural network algorithm and flowchart are also used to show analyze how the image segmentation are performed.

KEYWORDS: Medical Image, Segmentation, Cellular Neural Network, Artificial Neural Network.

INTRODUCTION

The analysis of image and processing of image tends to focus on 3-d images, how to transform one image to another. Like for instance the pixel-wise operation such as operations such as the contrast enhancement, local operations or the geometrical transformations like the image rotation.

Segmentation of tissues and structures from medical images is the first step in image analysis

9International Journal of Advance Research, IJOAR.org ISSN 2320-9119

IJOAR© 2013 http://www.ijoar.org

applications developed for medical diagnosis. Development of treatment plans and evaluation of disease progression are other applications. These applications stem from the fact that diseases affect specific tissues or structures, lead to loss, atrophy (volume loss), and abnormalities. Consequently, an accurate, reliable, and automatic segmentation of these tissues and structures can improve diagnosis and treatment of diseases. Manual segmentation, although prone to rater drift and bias, is usually accurate but is impractical for large datasets because it is tedious and time consuming. Automatic segmentation methods can be useful for clinical applications if they have: 1) Ability to segment like an expert; 2) Excellent performance for diverse datasets; and 3) Reasonable processing speed. Artificial Neural Networks (ANNs) have been developed for a wide range of applications such as function approximation, feature extraction, optimization, and classification. In particular, they have been developed for image enhancement, segmentation, registration, feature extraction, and object recognition. Among these, image segmentation is more important as it is a critical step for high-level processing such as object recognition. Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), Hopfield, Cellular, and Pulse-Coupled neural networks have been used for image segmentation. These networks can be categorized into feed-forward (associative) and feedback (auto-associative) networks. MLP, Self-Organized Map (SOM), and RBF neural networks belong to the feed-forward networks while Hopfield, Cellular, and Pulse-Coupled neural networks belong to the feedback networks.

Segmentation is refers to as the process of partitioning a digital image into many regions also, he goal of segmentation is to simplify and change he representation of an image into something that is meaningful and definitely easier to analyze and process. So classically, segmentation of image is thereby defined as the partitioning of an image into non-overlapping and constituent regions which are homogeneous with respect to some features such as intensity and texture. Therefore, the segmentation of image plays a vital role in the medical image through the facilitating of the delineation of regions interest. It is leant that the clinicians gained knowledge from a good segmentation as they provide important information for surgical planning, 3-D visualization and early disease detection, MR is generally more sensitive in detecting brain abnormalities during the early stages of disease, and is excellent in early detection of cases of cerebral infarction, brain tumors, or infections. MR is particularly useful in detecting white matter disease, such as multiple sclerosis, progressive multifocal leukoencephalopathy, leukodystrophy, and post-infectious encephalitis. The following artifacts are present in MR imaging:

Partial Volume

RF Noise

Intensity inhomogeneity

Gradient

Motion

Wrap Around

Gibbs Ringing

Susceptibility

There are different in the method of performing a good segmentation and this depends on the specific application, Medical imaging is performed in various modalities, such as MRI, CT, ultrasound, positron emission tomography (PET), etc. In the present review, we are focusing primarily on the segmentation of MR and CT images only image modality and other noted factors. Like for instance the method of segmentation of kidney is definitely different from that of the Liver, each image modality has its own idiosyncrasies with which to contend. It is therefore concluded based on the research so far there is



currently no single segmentation methods that yield an acceptable result for every medical image. However the methods that are hereby specialization to particular applications can often achieved better performance by taking into consideration via knowledge. There may arise difficulties in the selection of appropriate approach to a segmentation problem.

Techniques that are used in image segmentation are as follows:

i. Artificial Neural Network ii. Classifiers iii. Histogram-based methodiv. Region Growing Approachesv. Deformation Modelsvi. Markov Random Field Modelvii. Threshold ApproachesEach of these techniques has its own ways operation in the image segmentation and each also has its own merit and demerit.

II. RELATED WORK

MEDICAL IMAGE SEGMENTATION TECHNIQUES

Due to the fact that there is the increase in the number and size of medical image, there is necessity in the use of computer in facilitating their processing and analysis. Most especially, computer algorithm for the delineation of anatomical structures and other regions of interest are known to be the key component in assisting and automating specific radiological tasks. The image segmentation algorithm really play a vital role in the biomedical imaging applications like the location of pathology, study of anatomical structure, treatment planning, quantitative of tissue volumes etc.

With the development of complex medical imaging modalities which is known to be capable of producing a large quantity three dimensional images, segmentation is known to be a challenging field of image analysis. As it has been said earlier, the process of image segmentation is very important for most doctors and patients in the regard of the provision of important information for some medical acts of practices like in surgical planning, 3-D visualization and early time disease detection, image segmentation so this by facilitating delineation of regions of interest.

METHODS OF IMAGE SEGMENTATION

There are various methods by which image segmentation can be carried out, these methods has its individual way of operations, level of usefulness, implementations and limitations. These methods will be discussed in the part.

COMPRESSION-BASED METHODS

Compression based methods postulate that the optimal segmentation is the one that minimizes, over all possible segmentations, the coding length of the data. The connection between these two concepts is that segmentation tries to find patterns in an image and any regularity in the image can be used to compress it. The method describes each segment by its texture and boundary shape. Each of these components is modeled by a probability distribution function and its coding length is computed as follows:

The boundary encoding leverages the fact that regions in natural images tend to have a smooth



contour. This prior is used by Huffman to encode the difference chain code of the contours in an image. Thus, the smoother a boundary is, the shorter coding length it attains.

Texture is encoded by glossy compression in a way similar to minimum description length (MDL) principle, but here the length of the data given the model is approximated by the number of samples times the entropy of the model. The texture in each region is modeled by a multivariate normal distribution whose entropy has closed form expression. An interesting property of this model is that the estimated entropy bounds the true entropy of the data from above. This is because among all distributions with a given mean and covariance, normal distribution has the largest entropy. Thus, the true coding length cannot be more than what the algorithm tries to minimize.

For any given segmentation of an image, this scheme yields the number of bits required to encode that image based on the given segmentation. Thus, among all possible segmentations of an image, the goal is to find the segmentation which produces the shortest coding length. This can be achieved by a simple agglomerative clustering method. The distortion in the lossy compression determines the coarseness of the segmentation and its optimal value may differ for each image. This parameter can be estimated heuristically from the contrast of textures in an image. For example, when the textures in an image are similar, such as in camouflage images, stronger sensitivity and thus lower quantization is required.

HISTOGRAM-BASED METHOD

Histogram- based methods are very efficient when compared to other image segmentation methods because they typically require only one pass through the pixels. In this technique, a histogram is computed from all of the pixels in the image, and the peaks and valleys in the histogram are used to locate the clusters in the image. Color or intensity can be used as the measure.

A refinement of this technique is to recursively apply the histogram-seeking method to clusters in the image in order to divide them into smaller clusters. This is repeated with smaller and smaller clusters until no more clusters are formed.

One disadvantage of the histogram-seeking method is that it may be difficult to identify significant peaks and valleys in the image. In this technique of image classification distance metric and integrated region matching are familiar.

Histogram-based approaches can also be quickly adapted to occur over multiple frames, while maintaining their single pass efficiency. The histogram can be done in multiple fashions when multiple frames are considered. The same approach that is taken with one frame can be applied to multiple, and after the results are merged, peaks and valleys that were previously difficult to identify are more likely to be distinguishable. The histogram can also be applied on a per pixel basis where the information results are used to determine the most frequent color for the pixel location. This approach segments based on active objects and a static environment, resulting in a different type of segmentation useful in Video tracking.

REGION-GROWING METHODS

The first region-growing method was the seeded region growing method. This method takes a set of seeds as input along with the image. The seeds mark each of the objects to be segmented. The regions are iteratively grown by comparing all unallocated neighboring pixels to the regions. The difference between a pixel's intensity value and the region's mean, , is used as a measure of similarity. The pixel with the smallest difference measured this way is allocated to the respective region. This process continues until all pixels are allocated to a region.



Seeded region growing requires seeds as additional input. The segmentation results are dependent on the choice of seeds. Noise in the image can cause the seeds to be poorly placed. Unseeded region

growing is a modified algorithm that doesn't require explicit seeds. It starts off with a single region –the pixel chosen here does not significantly influence final segmentation. At each iteration it considers the neighboring pixels in the same way as seeded region growing. It differs from seeded region growing in that

if the minimum is less than a predefined threshold then it is added to the respective region . If not,

then the pixel is considered significantly different from all current regions and a new region is created with this pixel.

One variant of this technique, proposed by Haralick and Shapiro (1985), is based on pixel intensities. The mean and scatter of the region and the intensity of the candidate pixel is used to compute a test statistic. If the test statistic is sufficiently small, the pixel is added to the region, and the region’s mean and scatter are recomputed. Otherwise, the pixel is rejected, and is used to form a new region.

A special region-growing method is called -connected segmentation (see also lambda-connectedness). It is based on pixel intensities and neighborhood-linking paths. A degree of connectivity (connectedness) will be calculated based on a path that is formed by pixels. For a certain value of , two pixels are called - connected if there is a path linking those two pixels and the connectedness of this path is at least . -connectedness is an equivalence relation.

SPLIT-AND-MERGE METHODS

Split-and-merge segmentation is based on a quad tree partition of an image. It is sometimes called quad tree segmentation. This method starts at the root of the tree that represents the whole image. If it is found non-uniform (not homogeneous), then it is split into four son-squares (the splitting process), and so on so forth. Conversely, if four son-squares are homogeneous, they can be merged as several connected components (the merging process). The node in the tree is a segmented node. This process continues recursively until no further splits or merges are possible. When a special data structure is involved in the implementation of the algorithm of the method, its time complexity can reach , an optimal algorithm of the method.

LEVEL SET METHODS

The level set method was initially proposed to track moving interfaces by Osher and Sethian in 1988 and has spread across various imaging domains in the late nineties. It can be used to efficiently address the problem of curve/surface/etc. propagation in an implicit manner. The central idea is to represent the evolving contour using a signed function, where its zero level corresponds to the actual contour. Then, according to the motion equation of the contour, one can easily derive a similar flow for the implicit surface that when applied to the zero-level will reflect the propagation of the contour. The level set method encodes numerous advantages: it is implicit, parameter free, provides a direct way to estimate the geometric properties of the evolving structure, can change the topology and is intrinsic. Furthermore, they can be used to define an optimization framework as proposed by Zhao, Merriman and Osher in 1996. Therefore, one can conclude that it is a very convenient framework to address numerous applications of computer vision and medical image analysis. Furthermore, research into various level set data structureshas led to very efficient implementations of this method.

ARTIFICIAL NEURAL NETWORKS

Artificial neural networks (ANNs) are massively parallel networks of processing elements or



nodes that simulate biological learning. Each mode in an ANN is capable of performing elementary computation. Through the adaptation of weights assigned to the connections between nodes, learning can be achieved. ANNs represent a paradigm for marching learning and can be used in a variety of ways for image segmentation. ANNs as a classifier is most widely used where the weight are determine using the training data and later used to segment new data. Though ANNs are inherently parallel, their processing is usually simulated on a standard serial computer, thus reducing its potential computational advantage. The ANNS can as well be used is an unsupervised fashion as a clustering method and as a deformable models.

RESEARCH METHODOLOGY

CELLULAR NEURAL METHOD ALGORITHM AND FLOWCHART

The algorithm comprises of the following steps:Step 1: startStep 2: select imageStep 3: read image matrixStep 4: perform laplacian filter on the image to remove noiseStep 5: smoothen image using Gaussian filterStep 6: find the global image threshold using otu’s methodStep 7: convert image to grayscale i.e. 2 dimensionalStep 8: convert image to binary (i.e. black and white) Step 9: detect the edges or regions above threshold pointStep 10: find the complement of the detected regionStep 11: stop

FIG. 1: This is the original image of the part of the brain before any segmentation take place, the part in the middle of the brain is to be segmented.



FIG 2 : Binary masking of the segmented image, this shows the first step of segmentation where the focus part of the brain which is the middle part which has been segmented becomes black while the other part appears to be white during the first segmentation. This is just the what the above diagram in Fig 2 is demonstrating.



Fig 3: This shows the complement of the segmented image. This image demonstrate what happen during after the second and last segmentation, when the image is further segmented, the part of the brain that has been segmented which is the middle part becomes white and the rest part appear to be black

.

METHOD

USED

TIME

TAKEN

NUMBER OF

ITERATION

CELLULAR

NEURAL

NETWORK

4.68 3



TABLE 1: THE ANALYSIS OF IMAGE 1

The table tells more about the analysis of the third image. i.e how image 3 is formed in terms of the method being used, the time take for segmentation to take place and the number of iteration involved.

Fig 4: This shows the original image before segmentation take place.



Fig 4; Binary masking of the segmented image, this demonstrate how the image looks like after its undergoes first segmentation where the part that has been segmented appear to be black differentiating it from other part of the brain that is not affected.



Fig. 4 The Complement of the segmented image, this shows what happen to the image after its undergoes

the final segmentation where the affected part finally becomes white while the rest part which are

unaffected appears black.

TABLE 4; THE ANALYSIS OF IMAGE 2

The table below tells more about the analysis of the third image i.e how image 3 is formed in terms of the method used for segmentation, the time take for segmentation to take place and the number of iteration



involved.

METHOD

TIME

TAKEN

NUMBER

OF

ITERATION

CELLULAR

NEURAL

NETWORK

4.69

3



Fig 4: the graphics user interface for the implementation cellular neural network method

HOW IT’S IMPLEMENTED WORKS FOR THE SEGMENTATION OF IMAGE.

i. First browse for the image for segmentation

ii. Then press detect Alzheimer region to perform segmentation

iii. After then we shall wait for few seconds for the image to undergo segmentation



iv. The segmented image will then appear

v. Then press exit to close the user interface.

CONCLUSION AND FUTURE WORK

Medical image segmentation is regard to as a case study that is interesting and as well very important. Some methods like histogram based, level set and compression based method has been seen to be so effective in the image segmentation process, they are also seen to be easy and convenient in term of time, accuracy and iterations. We recommend that there methods of image segmentation should still be checked so closely and thoroughly so that any mistakes and lapses will be corrected. The correction can be done by reorganizing the algorithm after the extensive study of the methods that can be of assistance. This will aid more accuracy will.

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International Journal of Advance Research, IJOAR .org ... · PDF fileflowchart are also used to show analyze how the image segmentation are ... of seeds as input along with the