形态学颅骨剥离

using Morphological Operators

1

D.Selvaraj1 R.Dhanasekaran2

2

Research Scholar, Department of Electronics & Communication Engg., Sathyabama University

Chennai, India 1

mails2selvaraj@yahoo.com Principal,

Syed Ammal Engineering College,

Ramanathapuram, India 2

rdhanasekar@yahoo.com

and dilation, and their compositions i.e., opening and closing. The operators of morphological processing are particularly useful for the analysis of binary images so that MRI images need to be previously binarized. The background and brain mask of the image are obtained by applying a combination approach of thresholding with morphology.

The next section presents some basics on morphological operations. Section 3 describes our methodology. Finally we show some results in section 4 and draw some conclusions and future work perspectives in section 5.

Abstract— We present a new technique for segmenting brain nuclei from MRI brain images. Our method performs the segmentation using a combination approach of thresholding with morphological operators. The MRI brain image contains skull and noisy background. The latter have to be removed for further analysis. Elimination of any obstacles and noise from the image is the primary function of the morphological operators. We use simple morphological operators like dilation, erosion, opening and closing to the binarized MRI brain image. The results of skull stripped MR image with the use of disk shaped structuring elements are presented in the paper. The proposed method has been applied to a large number of MR images showing promising results for various image qualities, encouraging for future.

II. MATHEMATICAL MORPHOLOGY CONCEPTS

Mathematical morphology is a non-linear image analysis

technique that extracts image objects information by Keywords— Image segmentation, Image processing, skull

describing its geometrical structure in a formal way [7]. stripping, Morphological operator, brain segmentation

Mathematical morphology has been largely used in several

I. INTRODUCTION practical image processing and analysis problems. Here we

Magnetic resonance imaging (MRI) of the human brain is briefly review some mathematical morphology operators and the most common type of medical imaging used in the the corresponding operations used in this work.

Mathematical operators take two data as an input: an image medical diagnosis among a variety of imaging modadilities

such as computer tomography (CT), positron emission to be processed and a structuring element, which is a matrix tomography, ultrasound, mammography and radiography. So, used for defining a neighbourhood shape and size [1]. By MR images are widely used not only for detecting tissue choosing the shape and size of the element, we can influence deformities such as cancer and injuries but also for studying the morphological operations sensitivity to specific shapes brain pathology [8]. Also, many neurological diseases and appearing in the processed image. The elementary shapes of conditions alter the normal volume and regional distribution symmetrical structuring elements used in the following of brain parenchyma (Gray and white matter), cerebrospinal processing are shown in Fig. 1.

The erosion of binary image I by structuring element S is fluid. Such abnormalities are commonly related to the

conditions of hydrocephalus, cystic formation, brain atrophy defined by the formula [1]: and tumour growth. I ⊗ S = {x,y : Sxy ⊆ I} (1)

The basis for reliable measurement of the brain The dilation of binary image I by structuring element S is parenchyma, CSF volume and shape is segmentation. Image defined by the formula [1]: segmentation is to divide the image into disjoint homogenous (2) I ⊕ S = {x,y : Sxy ∩ I≠∅}

nregions, where all the pixels in the same class must have some Let f: D⊂ R → R is an image function and g: G⊂ Rn →

common characteristics but the major problems that affect the R is a structuring function. The two fundamental operations of quality of MRI segmentation are noise, inhomogeneous pixel gray-scale morphology, erosion and dilation, are defined as: intensity distribution and blunt boundaries in the medical MR images caused by MR data acquisition process [2, 3, and 4]. Definition 1 (Dilation) The dilation of the function f(x) by These problems do make manual quantitative analysis of brain the structuring function g(x), (f⊕ g)(x), is given by: imaging data a tedious and time consuming procedure, prone (f ⊕ g)(x) = max {f(z) + (gx)(z) : z ∈ D[gx]} (3) to observer variability [2]. Due to the characteristics of brain Definition 2 (Erosion) The erosion of the function f(x) by MRI, development of automated segmentation algorithms the structuring function g(x), (f Θ g) (x), is given by: require pre-processing which includes denoising, stripping of

(f Θ g)(x) = min {f (z) − (gx) (z): z ∈ D[gx]} (4)

skull.

Where gx indicates the translation by x (gx) (z) = g(z − x),

This paper presents a method for skull segmentation using a

and D[gx] denotes the domain of the translated structuring

sequence of mathematical morphological operations: erosion

function.

978-1-4244-5392-4/10/$26.00 ©2010 IEEE

1. Binarization of every image. The operations of closing and opening are the combinations

2. Opening operation and closing operation on every image of erosion and dilation, both using the same structuring

element. Morphological opening is erosion followed by in the sequence using the structuring element.

3. Applying the binary mask to the received MRI input dilation and morphological closing is dilation followed by

erosion. The Fig.3 shows that in a binarized image there are image. some remaining pixels that represent the noise. To remove the

left-over pixels the opening operation was used.

(a)

(b)

Figure 1. Disk shape structuring elements: (a) 2-pixel radius, (b) 5-pixel radius

III. PROPOSED METHODOLOGY FOR STRIPPING SKULL TO

SEGMENT BRAIN

This section presents the proposed methodology for segmenting brain MRI images. The fundamental task in brain MRI segmentation is the classification of volumetric data into grey matter, white matter and cerebrospinal fluid but it is not easy as there are some inherent difficulties associated with image segmentation; among them are RF coil in homogeneity, brain tissue susceptibility and other systematic artifacts. Various preprocessing steps have been proposed to deal with some or all of these difficulties. Skull stripping is the first processing step in the segmentation of brain tissueas shown in Fig 2.

Figure 2. Overview of proposed methodology

In the proposed method for skull stripping, we see the brain surface as a smooth manifold with relatively low curvature that separates brain from non-brain regions. Also, the brain cortex can be visualized as a distinct dark ring surrounding the brain tissues in the T1-weighted axial MR images.

The steps involved in the proposed methodology for skull stripping and brain extraction consists of three steps.

A. Binarization Binarization is the process that converts a grey level image into a binary image. The binarization process involves examining the grey-level value of each pixel in the enhanced image, and if the value is greater than the global threshold, then the pixel value is set to a binary value one; otherwise it is set to zero. The binarized image is shown in Fig. 3.

(a) (b)

Figure 3. (a) Input Image, (b) Binarized Image

B. Morphological Operation

The binary morphological operators are then applied on the binarized image. Elimination of any obstacles and noise from the image is the primary function of the morphological operators. The morphological operators namely, opening and closing are being employed in the proposed method.

1) Opening: An opening operation consists of erosion followed by dilation with the same structuring element. The Fig. 4 shows the image after applying the opening operator.

Figure 4. Binarized image after applying opening operator

2) Closing: A closing operation consists of a dilation followed by an erosion with the same structuring element. The Fig. 5 shows the image after applying the closing operator.

3) Erosion: Erosion operation on an image I containing labels 0 and 1, with a structuring element S, changes the value of pixel i in I from 1 to 0, if the result of convolving S with I, centered at i, is less than some predetermined value. We have

set this value to be the area of S, which is basically the number of pixels that are 1 in the structuring element itself. The structuring element (also known as the erosion kernel) determines the details of how particular erosion thins boundaries.

4) Dilation: Dual to erosion, a dilation operation on an image I containing labels 0 and 1, with a structuring element S, changes the value of pixel i in I from 0 to 1, if the result of convolving S with I , centered at i , is more than some predetermined value. We have set this value to be zero. The structuring element (also known as the dilation kernel) determines the details of how a particular dilation grows Step 8: Develop binary mask choosing the larger area Step 9: Apply binary mask on the original image to get skull stripped image (brain nuclei).

IV. EXPERIMENTAL RESULTS

The experimental results of the proposed methodology for segmenting brain MRI images are presented in this section. The proposed methodology is implemented in Matlab (7.4). The input to the proposed methodology is T1-weighted brain MRI images collected from publicly available databases. Regarding T1-weighting, every tissue in the human body has its own T1 and T2 value. This term is used to indicate an image where most of the contrast between tissues is due to boundaries in an image

Figure. 5. Brain Mask

C. Region-based binary mask extraction

Region-based extraction is done by examining the properties of each block that satisfy some criteria. We have used one of two criteria. One criterion is to look at the max-min difference and the other is to determine the mean values of the blocks. The process results with a brain mask that is then applied to the original MRI data. Consequently, we attain a brain MRI image with its brain cortex stripped as shown in Fig. 6.

Figure 6. Skull Stripped Brain Image

D. Algorithm

Step 1: Get the MRI brain image to be stripped Step 2: Find the maximum pixel value in the image Step 3: Find the limit of high and Low frequency value Step 4: Normalize the image

Step 5: Calculate the gray threshold from normalized image

Step 6: Construct binary image using gray threshold value Step 7: Apply disk shaped morphological operator to eliminate the obstacle and noise from the image

differences in the T1 value. The proposed methodology is based on Intensity Thresholding (IT), which is the easiest and fastest segmentation method, often adopted for preprocessing of medical images and preregistration problems.

The sample results of brain MRI segmentation obtained using the proposed methodology is shown in the following Fig.7 to Fig. 11.

(a) (b)

Figure 7. (a) Input Image, (b) Segmented Brain Image

(a) (b)

Figure 8. (a) Input Image, (b) Segmented Brain Image

(a) (b)

Figure 9. (a) Input Image, (b) Segmented Brain Image

(a) (b)

Figure 10. (a) Input Image, (b) Segmented Brain Image

(a) (b)

Figure 11. (a) Input Image, (b) Segmented Brain Image

V. CONCLUSION

In this paper, an automated, simple and efficient brain MRI segmentation method for classifying brain tissues has been presented. Initially, the cortex present in the brain MRI images is extracted by combining preprocessing techniques and incorporating mathematical morphology. Experimental

results have showed that the proposed method does a reasonably good job in terms of segmentation. In future from this segmented brain image we can segment grey matter, White matter and cerebrospinal fluid.

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