Survey on Medical image processing areas

Survey on Medical image processing areas

NIKITA K.CHAUHAN,

PROF.SWATI J. PATEL

1

M.E. Student , Department of information Technology, L.D.Engg college , Ahmedabad , Gujarat.

2

Professor of IT & computer, Department of IT & computer L.D.Engg college , Ahmedabad , Gujarat.

nikita.chawda17@gamil.com, swati.ldce@gmail.com

ABSTRACT : Medical image computing is the one of the branch of medical image processing. MIC focuses on the computational analysis of the image. The methods of MIC can be classified into several categories like : image segmentation, image registration, image-based biomechnical and many more. A survey on medical image processing has prove that 25% of medical image processing are image registration , 46% are image segmentation and 29% are regmetation i.e. image segmeantation based registration and vice versa. In medical image processing image registration and image segmentation are major areas of research topic.

Keywords— image segmentation , image registration , correspondence , medical image computing, medical image processing.

I: INTRODUCTION

Medical image processing is very growing, advance and qualified techniques for detecting diseases or problems of a patient to solve without too much opening the body. In a growing number of clinical studies, images are acquired from multiple imaging modalities. Medical Image segmentation and registration have been important research topics over the last two to three decades and several state of the art surveys exist for segmentation as well as for registration techniques. A patient gets more benefits like precise and rapid disease management, fewer side effects, improved diagnosis and more cost- effective treatment thru the medical imaging. Image segmentation is very much important as well as difficult task to solve the problem of medical image processing.

In image segmentation the process is done as it partition the image into several parts and detect the interested part of the image. In image registration techniques two different images are compared and find the interested part from the source image.

II Medical Image Segmentation

In this section we define the general algorithms for segmenting the image. from [3] we find that segmentation can be classified in to two different part as : soft segmentation and hard segmentation.

Segmentations that allow regions or classes to overlap are called soft segmentations. A hard segmentation forces a decision of whether a pixel is inside or outside the object. But for medical image processing we can’t use soft segmentation because it suffers from the partial volume effect which results in multiple tissues contribute to a single pixel or voxel resulting in a blurring of intensity across boundaries In many literature there are different types and kind of algorithm and category is given but we only

focuses on main five categories of segmentation of medical image we is shown in below.

1.)Thresholding 2.)Region Growing

3.) Morphological watershades 4.)Classification

5.)Clustering

1.) Thresholding

A binary partition of image intensities is created by this approach thru segmenting the scalar image .In this procedure an intensity value called T, threshold is attempted which ia used to separate the desire classes.The pixel with intensities greater than T is grouped in one class and less than or equal to T is grouped in another class and on this basis the segmentation procedure will done. In this procedure the main task is to select the threshold value T.

From [2] threshold categorized into two different categories: global thresholding and local thresholding. In global thresholding all the pixel’s intensity in the image is constant and in local thresholding the threshold value T depends on the local properties of some region.

The probably most popular method for finding a single threshold separating two classes is Otsu’s method [4] which has been extended to multiple classes [5]. It finds the global maximum of the between-class variance:

K

σ²between modified =∑ωjµj2 j=1

where k is the number of object classes, µj is the mean intensity of the class and ωj is the probability of each class given by the histogram. Furthermore machine learning techniques can be used to obtain thresholds.Determination of more than one threshold value is a process called multithresholding.

Thresholding is an effective and straightforward method to obtain a segmentation For simple images

in which different structures have contrasting intensities or other quantifiable features.

In medical imaging operations thresholding is often used as initial step for sequences. Many medical images are having low contrast and lots of noise in such cases histogram for intensities become complex and thresholding approach may fail to segment the image. Moreover thresholding have the problem of over segmentation due to undesirably large number of region.

1.) Region Growing

In this technique the part of interest is segmented using intensities of interested part by a seed point.

Region growing is a technique for extracting a region of the image that is connected based on some predefined criteria. This criteria can be based on intensity information and/or edges in the image. In simple meaning this technique required a seed point for extracting the pixel intensities of the interested part. In this technique procedure will start with a set of seed point and the algorithm is growing by appending to each seed its neighboring pixels have similar properties as the seed. Like thresholding, region growing is not often used alone but within a set of image processing operations, particularly for the delineation of small, simple structures such as tumors and lesions [6].

Region growing algorithms are efficient and easy to implement. For simple regions that have homogeneous intensities, region growing can provide an accurate segmentation result.

The selection of seed point and definition of homogeneity criteria are two main crucial point of this approach. The main limitation of region growing clearly is its tendency to leak into neighboring objects sharing similar intensities with the structure to be segmented. Thus, for each region that needs to be extracted, a seed must be planted.

Region growing can also be sensitive to noise, causing extracted regions to have holes or even become disconnected. Conversely, partial volume effects can cause separate regions to become connected.

2.) Morphological Watershades

It is a region based technique which utilize image morphology. In this an image is visualizes in three dimensions where height of each point represent its gradient value. Initially, at least one marker in each object of the image, including the background as a separate object, are selected.

The marker of the procedure will be selected by the user or by automatically thru an another procedure which uses application specific knowledge of the part of intrest. Then, morphological operation is applied to grow these markers, just like pouring water into punctures, which are represented by the markers. When water from different punctures is

beginning to mix, a dam is built to stop the mixing.

These dams are the boundaries of objects in the image.

This is a simple and intuitive algorithm which results accurate segmentation in many application but this algorithm has same problem that is it is sensitive to noise and causes over segmentation in complex images.

3.) Classification

In this approach as its name implies image will be segmented using a classifier. It is a pattern recognition technique which seek to partition a feature space derived from image using data with known labels. In [2] a classifier f which can be determine using f(vi)=ci. where ci represent discrete class labels. an medical images are segmented in this approach by pixel classify or thru image patches.

Classifiers are known as supervised methods since they require training data that are manually segmented and then used as references for automatically segmenting new data. There are a number of ways in which training data can be applied in classifier methods. A simple classifier is the nearest-neighbor classifier, where each pixel or voxel is classified in the same class as the training datum with the closest intensity.

A commonly used parametric classifier is the maximum likelihood (ML) or Bayes classifier[7].The selection and determination of classifier is the most important part in a classification algorithm

Classification algorithm will produce a accurate result if and only if data is sufficient to distinguish the classes. However, the requirement of manual work to obtain training data is time-consuming and laborious. If the objects of classification algorithm are not pixels but image patches,

then, the boundaries of the segmented regions become uncertain. A disadvantage of classifier is that they don’t perform any spatial modeling

.

5.) Clustering

These algorithms uses same method as classification but they do not uses any kind of training data. that why these approach also known as un supervised methods. In order to compensate for the lack of training data, clustering methods iterate between segmenting the image and characterizing the properties of the each class. In a sense, clustering methods train themselves using the available data.

Commonly used clustering algorithms are k- mean, fuzzy c-mean algorithm. Clustering methods are simple to implement. Different application will measure different distance. Same anatomical part with inhomogeneous feature’s pixels are grouped into

different clusters. Many clustering algorithm rely on a good initialization to produce good result. But clustering algorithms have same problem that it is sensitive to noise and intensity inhomogenity.

III. Medical image registration

Registration aims at transforming a model or a template image to align it with a target image so that their corresponding parts are spatially aligned.

Generally registration is divided in to two main part : rigid registration and non-rigid registration. The image processing which includes rotation, translation or transformation then it is call a rigid registration. If the registration is non linear like shape change or wrapping then it is call a non-rigid registration. In most of medical images the non-linear registration is included so in this paper we only focus on non-rigid registration.

In non-rigid transformation deformation required to be taken into account which causes following three factors[8]:

(

(2) differences between individuals; and

(3) warping due to image distortion, such as in echo- planar magnetic resonance imaging.

Deformation may be caused by different factors and needs different algorithms.

Steps involved in Image Registration:[9]

Fig 1 steps for image registration

 Feature detection: Salient and distinctive objects (closed-boundary regions, edges, contours, line intersections, corners, etc) in both reference and sensed images are detected.

 Feature matching: The correspondence between the features in the reference and sensed image established.

 Transform model estimation: The type and parameters of the so-called mapping functions, aligning the sensed image with the reference image, are estimated.

 Image resampling and transformation: The sensed image is transformed by means of the mapping functions.

Non-rigid registration is a well-posed problem which can be solve thru general parametric algorithm which are: affine transformation, polynomial transformation and kernel-based transformation.

a. Affine Transformation

Affine transformation includes scaling, rotation, translation, reflection and shearing. It is represented by a matrix:

x’ a11 a12 a13 X

y’ = a21 a22 a23 y

1 0 0 1 1

where (x, y) is a point in the model or template image, and (x0, y0) is the corresponding point in the target image, and aij are the affine parameters to be determined.

b. Polynomial transformation

The polynomial transformation has the form:

xi’=∑∑ apqxip yiq 0≤p+q≤m P q

yi’=∑∑ bpqxip yiq 0≤p+q≤m P q

where m is the order of the polynomial function, apq

and bpq are the parameters of the polynomial.

c. Kernel-Based Transformation

Instead of using polynomials to represent the transformation function, kernelbased transformation uses a set of basis functions hk(x, y), k = 1, . . . ,K, to represent the transformation function

k xi’=∑akhk(xi,yi)

k=1 k yi’=∑bkhk(xi,yi)

k=1

Usually, these basis functions are radially symmetric and have finite supports. Frequently used basis functions include Gaussian, B-spline, multiquadratic spline, Fourier descriptors, wavelets, etc.

1 ICP and dual boot-strap ICP

In medical image registration the Iterative Closest Point (ICP) algorithm depends on educated guess of correspondence function [2]. In this algorithm a reference model or image m and a target image I is considered, the steps of ICP algorithm is as follows:

1. For each point pi Є M, regard its closest point pi^’ Є I as its corresponding point.

2. Using the estimated correspondence, compute the transformation T that minimize the average distance between the corresponding points. T can be affine transformation, polynomial transformation, or more complex transformations.

3. Iterate step a and b until convergence.

If the initial estimate is not good then ICP algorithm produces a poor result. An improvement of this algorithm is called dual boot strap ICP which is describe as below.

1. Extract feature points and find the initial correspondence.

2. For each initial correspondence, starting from the lowest order transformation,

iterate the following steps until convergence:

– estimate transformation parameters using robust ICP;

– select appropriate transformation and estimate parameters using

statistical model selection techniques (more details in [62]);

– expand the bootstrap region based on the uncertainty of transformation

estimated. Iteration is terminated when transformation is

accurate enough.

3. Terminate with failure if no more initial estimates are available.

2. variational approach

It is class of non-rigid parametric registration algorithm and it is represented by a function Ф(x).

A varitional approach can be divided into mainly four types [10]: elastic registration, fluid registration, diffusion registration, curvature registration.

1 Elastic registration : It is based on elastic body[12].

The

smoothing function S(ф) used by elastic registration allow for stretching or shrinking of objects, but not tearing.

2. Fluid registration : This registration focuses on spatial smoothing of velocity field dф/dt , which is oppose the elastic registration.

3. Diffusion registration : this registration is totally different from the above two. diffusion registration is based on intensities of the image.

4. curvature registration : Compared with elastic, fluid and diffusion registration, curvature registration is less dependent on the initial configuration of the reference and template images.Curvature registration aims at minimizing the curvature of the components of the displacement vectors.

IV Medical image segmentation based registration In medical image processing some imaging problems are not solved by using only segmentation or registration method, so there is also hybrid techniques which use to solve such problems. some problems occur during segmenting medical image is listed below:[11]

(a). Due to both the nature of the imaging technology and the human tissue, pixel intensity is often not homogeneous within a tissue type Intensity inhomogeneity will cause many segmentation methods based on intensity homogeneity to fail.

(b). Even if medical imaging technologies are developing rapidly, many medical images are still noisy and have low contrast. Dealing with noise and low contrast without losing accuracy is a very challenging task for medical image segmentation.

(c). Medical images have many variable properties.

For example, when a patient is being scanned during different visits, his different postures and positions will cause images of the same part to vary. A tumor or other disease will cause an anatomical part to differ significantly from a normal healthy case. The variability of anatomical parts makes the representation of prior knowledge very hard.

(d). In some medical images, especially x-ray images, the anatomical parts to be segmented overlap each other Segmentation of the overlapping anatomical parts is very difficult because of the complication of the overlapping regions.

Many medical literature reviews give ideas on these problems but the we focus on very known and easily implementing two approaches are discusses here.

General model based approach and atlas based approach.

1.) General model based approach: In this approach we are considering the general deformation model which classified into main three categories. : active contour (snake), active shape (eigenshape) and level set.

A.] Active contour(snake) : A snake can be deformed to match any kind of shape under the constraints of three kinds of forces: internal forces, image forces and external forces. Internal forces are constraints on the stretching and bending of the snake. Image forces are given by image features such as edges that attract the snake. External forces contain external constraints on the snake such as spring force and repulsion The aim of a snake algorithm is to iteratively deform the snake by moving the snake points to minimize the total energy so that the snake can fit the image features well. Traditional snake has two main drawbacks. Firstly, it is too sensitive to initialization, and secondly, it cannot be attracted by concave parts of image contour. The snake algorithm has proved to be very useful for many applications.It used snake algorithm for brain segmentation in MR images.Snake has also been applied to the segmentation of liver and heart in CT images, and

carpal bone in x-ray images. If the snake is well designed and the three forces are well balanced, the snake can perfectly fit the boundaries of anatomical parts. However, if the parameters are not set appropriately, snake can produce poor result.

Moreover, snake cannot handle topological changes when it evolves over time.

B.] Active Shape: Active shape model is a statistical model generated from a set of training samples.

Corresponding landmark points on the boundaries of the training samples are identified manually. The coordinates of the landmark points in each sample are represented in a vector. So, a sample becomes a vector in a high-dimensional feature space called the eigenspace. After normalizing the scale, rotation, and translation of each sample, Principal Component Analysis (PCA) is performed to identify major dimensions of the samples in the eigenspace. A sample shape can now be represented by a linear combination of the eigenvectors. By changing the parameters of the linear combination, new shapes can be synthesized for finding the optimal solution.ASM has been used in many applications. it was used to segment tibia bone in ultrasound images. ASM to heart segmentation in MR images., ASM was applied to abdomen MR image segmentation.In general, active shape model is suitable for applications in which the objects’ shape variation can be modeled by not too many parameters. Otherwise, it may be too difficult to synthesizing the desired shape. In addition, many training samples are needed to accurately compute the statistical distribution of the possible shapes in the eigenspace. Preparing these training samples can be a laborious task.

C.] Level set : The main ideas of level set methods are as follows. Let Ѓ denote a closed curve in 2D.

Then, define a 3D function Ф(x, y, t) called the level set function. Ф(x, y, t) is the distance d of the point (x, y) from Ѓat time t. Distance d is positive if (x, y) is outside ф, zero if (x, y) is on Ѓ, and negative inside.

In practice, it is not necessary to construct the whole level set function Ф at the beginning of the algorithm, which is an impossible task because Фrepresents all the possible states of the curve . Ѓ Instead, the algorithm begins with an initial curve Ф(x, y, t = 0) = 0 and a force F initialized by the user. The force F represents the speed of propagation of Ѓ in its normal direction. The major advantage of level set method is that, even if the propagating contour may change topology, break, merge and form sharp corners, the level set functionФ(x,y,t)remains a single function.

One disadvantage of level set algorithm is the lack of preservation of the shape information. Moreover, level set method may have leakage problem because of its flexibility and lack of geometric constrains.

2.) Atlas based approach:

Atlas-based approach has become a standard paradigm for exploiting prior knowledge in medical image segmentation. In atlas-based segmentation,

manual or semi-automatic segmentation is performed once on a sample image to construct a spatial map called the atlas. Given a target image, the atlas is deformed non-rigidly and registered to the target image. Various non-rigid registration methods can be used in the registration process. The registeredatlas gives the segmentation result

In general, the atlas-based approach first aligns the atlas to the target image by some global transformation. Then, local refinement of each part of the atlas is performed to accurately extract the contours of the anatomical parts of interest.

Constructing atlas based on a single sample may have some problems. [12] Firstly, the selected single sample may not be a typical one. Secondly, the atlas based on a single sample cannot contain any information of variability.So, it cannot determine whether a deformed shape is an acceptable shape.

By exploiting prior knowledge properly, atlas-based approach can solve the initialization problem of most deformable model approaches. It can also handle medical images with low contrast and inhomogeneous visual features since it knows the desired shapes of the anatomical parts. Therefore, atlas based approach has the potential of solving very complex medical image segmentation problem. The difficulty of using atlas-based approach lies on the construction of an appropriate atlas.

Most of the general deformable model-based approaches are sensitive to initialization because they do not have the prior knowledge of the anatomical structures. Atlas-based approach can solve the initialization problem by constructing an atlas from proper prior knowledge. The difficulty of using atlas- based approach lies on the construction

of the proper atlas. A simple atlas is easy to build but may have

limitations in segmenting complex medical image.

On the other hand, building a complex atlas that contains a rich amount of prior knowledge is tedious.

V: CONCLUSION

In medical image progressing we have shown the most important and popular search area for medical imaging processing. In my dissertation practical work I will work on hybrid techniques to solve the segmentation problem which is described in paper.

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