3D-Brain MRI Segmentation Based on Improved ...

Journal of KIIT. Vol. 14, No. 5, pp. 75-88, May 31, 2016. pISSN 1598-8619, eISSN 2093-7571 75 http://dx.doi.org/10.14801/jkiit.2016.14.5.75

3D-Brain MRI Segmentation Based on Improved Level Set by AI Rules and Medical Knowledge Combining 3 Classes-EM and Bayesian Method Nguyen Kim

Ho

Minh

Dao*, Atsuo

Duy*, Tran

Anh

Yoshitaka**, Jin

Tuan*, Nguyen

Young

Hai Duong*, Tran

Kim***, Seung

Anh

Ho Choi****, and

Tuan*, Nguyen Pham

The Bao*

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. 2011-0012586) and “Japan-Asia Youth Exchange Program in Science” which is supported by JST. JST stands for Japan Science and Technology Agency.

Abstract MRI and CT images are the most popular formats assisting a doctor in diagnosis and treatment, but highly accurate segmentation is a challenging problem due to intensity inhomogeneity and environmental noises. In this paper, we introduce an appropriate and effective automatic approach to facilitate this problem in two stages. In the first stage, skull region is removed from the brain by morphological active contour and level set process. Moreover, in level set process, some AI rules are defined on slice positions of brain to increase the accuracy. In the second stage, a modified EM method is performed on the resultant skull-stripping image to identify some candidate main regions of CSF (cerebro-spinal fluid), GM (gray matter), and WM (white matter). The candidate regions are then re-estimated into the proper CSF, GM, and WM through a Bayesian Estimation Process. The experimental results show that the proposed approach obtains a robust segmentation for IBSR, OASIS and Korean Hospital database. With the proposed AI-rules, the level set method gets good skull-stripping images regardless of MRI slice position in bran. Also, Bayesian postprocessing can improve the segmentation performance by 10~15% in CSF, GM and WM ratios compared the basic EM algorithm.

요 약 MRI와 CT 영상은 의료 진단과 치료에서 가장 일반적인 수단이지만, 의료영상을 정확하게 분할하는 것은 신호강도의 불균등성과 잡음으로 인하여 매우 어려운 문제이다. 이 문제를 해결하기 위하여 본 논문에서는 두 개의 단계를 갖는 효과적인 자동분할 알고리듬을 제안한다. 첫 단계에서는 형태학적 능동 컨투어 및 레벨셋 과정을 통하여 뇌영상으로부터 두개골 영역을 제거한다. 레벨셋 과정에서 더욱 정밀도를 높이기 위하여 뇌영 상의 슬라이스 위치에 따른 AI 규칙이 정의된다. 두 번째 단계에서는 변형된 EM알고리듬이 두개골 제거 의료 영상의 주요영역들을 식별하기 위하여 수행되는데, 주요영역은 CSF(뇌척수액), GM(회색질) 그리고 WM(백색질) 이다. 이 후보영역들은 베이지안 추정과정을 통하여 더 정확한 CSF, GM 및 WM으로 재추정된다. 의료영상 분 할실험 결과 제안한 방법이 IBSR, OASIS 그리고 한국 DB에 대하여 강인한 분할 결과를 얻었다. AI 규칙을 갖 는 레벨셋 방법은 뇌에서 MRI 슬라이스의 위치와 무관하게 두개골이 제거된 영상을 얻었다. 또한 베이지안 후 처리는 기본 EM 알고리듬에 대하여 CSF, GM 그리고 WM 탐지비율에서 10~15%의 분할성능 향상을 가져왔다. Keywords brain image segmentation, morphological active contour, level set with AI rules, EM, bayesian estimation process * Department of Mathematics and Computer Science, Uni. of Science, HCMC, Vietnam ** School of Information Science, Japan Advanced Institute of Science and Technology, Japan *** Department of Electronics and Computer Engineering, Chonnam National University, Korea *** Departmet of Computer Science, Dongshin University, Korea

Received: Mar. 15, 2016 Revised: May 18, 2016 Accepted: May 21, 2016 ž Corresponding Author: Seung Ho Choi Dept. of Computer Science, Dongshin University, Naju, Korea, Tel.: +82-61-330-3194, Email: [email protected]

76 3D-Brain MRI Segmentation Based on Improved Level Set by AI Rules and Medical Knowledge Combining

Ⅰ

. Introduction

approaches are thresholding and region-based segmentation [20][21]. However, the thresholding method has many

Recent brain disease diagnosis requires a lot of experiences and knowledge on brain CT (Computed Tomography) or MRI (Magnetic Resonance Imaging)

disadvantages for various databases, especially, database infected by intensity inhomogeneity and environmental noises. With the demands of dealing with limitations or

images. Even in the case of using softwares, a doctor’s diagnosis is quite subjective and labor-intensive, and also adjusting some parameters or thresholds for an

restrictions of database, many supervised, unsupervised and hybrid intelligent methods have been proposed. K-nearest, SVM, PCA are representative to supervised

optimal segmentation of interested regions [1] is not easy. The underlying objective of brain MRI segmentation is to partition an input image into three

methods [22]-[24]. And, Fuzzy k-means, expectation maximization (EM), hierarchical clustering are representative to unsupervised methods [25]-[27]. Besides, some

main regions: CSF (cerebro-spinal fluid), GM (gray matter), and WM (white matter) [2]. Nowadays, highly accurate segmentation is actually a

hybrid approaches are also cited in some papers to deal with the problem effectively [28][29]. In postprocessing, some methods tried to add some constraints

challenging problem due to intensity inhomogeneity and environmental noises of medical images. Intensity inhomogeneity is observed to be the variations of

or additional information to further improve the segmentation results, for example neighborhood regularization, imposing shape, and localization

intensities in low-frequency domain, and it is derived from the abnormal conditions of magnetic fields while scanning [3]. In a medical database environmental

constraint [30][31]. In compared to some conventional approaches, we introduce a new approach to improve the segmentation

noises occur mainly from instrumental limitations, which impair the segmentation performance [4]. Besides above two main problems, the poor resolution and

significantly in the following aspects. Firstly, most of the approaches only work well on high-quality images. But in low quality images, such as in IBSR or OASIS

weak contrast are also additional factors to prevent algorithms from performing their correct functionalities. The main framework of brain MRI segmentation is

databases, the results become uncontrollable. Secondly, conventional approaches focus only on some slices, where the brain images are fine and high information.

described in the review paper of [5]. Usually, the process is comprised of four steps: preprocessing, feature extraction, segmentation, and post- processing.

Thirdly, the segmentations are only relatively acceptable but not sure to be exact information as doctor needs. In this case, we need to compare the segmentation

In preprocessing, most of the actions are related to registration [6], de-noising [7], skull -stripping [8], and intensity normalization [9] for a well preparation and

results with provided ground truth images. Lastly, the approach in a medical images requires a high accuracy and not much time-consuming.

enhancement. Especially, the skull-stripping task plays a crucial role to analyze brain anomalies or liver tumors in the subsequent steps [10]. In the next step, due to

Our proposed approach is a two-stage approach. In the first stage, the combination of morphological operations with active contour method aids to obtain

the occupation of large amount of time and memory, many feature extraction methods is proposed by researchers. There are three trends: shape based features

skull boundaries robustly and effectively. However, in the need of removing skull boundary as well as bones inside the head, we introduce one more method using

[11]-[13], intensity based features [14][15], and texture based features [16]-[19]. Segmentation step is the main step of brain-related problems. The very simple

level set directed by some AI rules of slice positions. Secondly, three main regions CSF, GM and WM are extracted based on the anatomical structure of a brain

Journal of KIIT. Vol. 14, No. 5, pp. 75-88, May 31, 2016. pISSN 1598-8619, eISSN 2093-7571 77 and modified EM process. And then, these candidate regions are refined into well-established regions though

boundary. The process of morphology cooperating with AC is shown in the Fig. 3 and Algorithm 1.

a strategy of Bayesian estimation process. This paper is organized in five sections. Section II presents our first problem of skull-stripping from the brain image following two approaches: morphological active contour and AI rules-based level set. Section III concentrates on the segmentation problem with two steps: modified EM segmentation and region-refined Bayesian process. Section IV reports our experiments on a variety of 3 Brain MRI databases (IBSR, OASIS, and Korean Hospital database). Conclusions will be remarked in the last section

Fig. 1. Brain shape of brain MRI image

II. Skull-Stripping Algorithm

2.1 Morphological Active Contour Algorithm On observing the image as shown in Fig. 1, the

(a)

boundary of the brain has a complicated shape. The complicated shape means that the brain has concave shape or convex shape in different positions. Moreover, we notice that there is always a small layer of cerebro-spinal fluid (CSF) between skull and brain. If we find the approximate boundary based on the accurate boundary and morphology techniques, the algorithm will run fast and has a higher accuracy.. The main reason of using morphology operations is

(b) Fig. 2. (a) Accurate result in FLAIR images (in Korean hospital database) and (b) Inaccurate result in T1-weighted images (in OASIS database)

that it is very simple and easy to implement for detecting the boundary of a brain in fluid-attenuated inversion recovery (FLAIR or T2-weighted) images. Unfortunately, the results are unstable to the other database of T1-weighted images. In the Fig. 2, the morphology results are accurate in FLAIR images but

(a)

(b)

(c)

inaccurate in T1-weighted images. The difference between T1-weighted and FLAIR(T2- weighted) image is described in the paper [33]. In order to deal with all situations from different databases, we suggest an additional step using active contour (AC) to reduce the impact of selecting parameter for morphology and to get a desired brain

(f) (d) (e) Fig. 3. Step (a) to (e) results from algorithm 1

78 3D-Brain MRI Segmentation Based on Improved Level Set by AI Rules and Medical Knowledge Combining

Algorithm 1: Morphology active contour in Fig 3 (a) Input: A Brain MRI Image (b) Apply Otsu method to get a binary image (c) Apply Active Contour model to get a better contour (d) Apply Canny Edge Detection (e) Apply morphology to keep the largest connected component and create a mask (f) Output: the desired result.

on the MRI slices need to be constructed for improving the accuracy of segmentation. And the level set method replaces active contour to get more exact area of brain boundaries. Fig. 4 shows a complex image with brain shape of being abnormal and a lot of bones inside. In this case, the morphological Active Contour gives an empty result.

2.2 AI-rules based Level Set Algorithm Although morphological active contour performs well on the most of brain MRI Images, there are some incorrect boundary segmentation cases when the brain region is not unique. Another drawback is that the more complex the brain image is, especially having bones in the brain regions, the more wrong brain regions are determined. Therefore, the AI rules

Fig. 4. Complicated brain image with abnormal brain shape and bones inside brain

Fig. 5. Modified level set method pipeline

Journal of KIIT. Vol. 14, No. 5, pp. 75-88, May 31, 2016. pISSN 1598-8619, eISSN 2093-7571 79 The whole process of modified level set method for skull-stripping is depicted in Fig. 5. In this figure, after preprocessing, we can remove the background of the image and decide the initial regions for level set evolution. The initial regions are chosen by shifting a window size 3×2 through the image. The acceptable window is selected for that all of the inside pixels are higher than the gray level average of the image. The AI rules are set up in the following constraints. Some sample skull-stripping results are shown in the Fig. 6.

III. Brain Segmentation Algorithm

3.1 Anatomical Structure Analysis of Brain MRI MR Imaging is a popular medical imaging technique used

in radiology

to visualize detailed internal

structures. It provides good contrast between different soft tissues of the body, which makes it especially useful in imaging the brain, muscles, the heart and cancers when compared with other medical imaging techniques, such as computed tomography (CT) or X-rays. In the most of anatomical brain structure analyses, there are three main parts for a doctor to make a diagnosis and treatment for a patient. Gray matter (GM) is the outer layer in the cerebral cortex composed mostly of neuron cell bodies. White matter (WM) is the fiber tracts deep to the gray matter.

Fig. 6. Some skull-stripping results AI Rules

- Denote (xi-1, yi-1) as a center point as the previous slice. It is the prior information to predict the central th location of the brain in the i slice. In the case, working slice is the first one, (x0, y0) is initialized manually: x0 =

w 5h , y0 = 2 8

- Given some selected regions, we keep the other regions whose pixels and selected regions’ pixels are symmetric about the x-axis that runs through (x0, y0). Similar to the y-axis, this step ensures unconnected area(s) of the brain will not be left out. - Update x0 and y0 by the following equations (1).

(1)

80 3D-Brain MRI Segmentation Based on Improved Level Set by AI Rules and Medical Knowledge Combining

CSF regions: - EM 3-classes segmentation for CSF candidate regions, WM and GM region. - EM 2-classes segmentation of CSF candidate regions for real CSF regions and low intensity GM regions W M regions: - Remove the CSF regions from the image. - EM 3-classes segmentation for WM candidate regions and GM region - EM 2-classes segmentation of WM candidate regions for real WM regions and high GM regions G M regions: - Remove CSF from the brain image - Remove WM from the brain image

into k classes by any of clustering or classifying methods, the results can be more segmented regions in the case of CSF and WM and less segmentation regions in case of GM. For that reason, we suggest the following procedure and formula to segment CSF, WM, and GM as separate regions. Remind that 3-regions segmentation in medical image always requires two properties: non-overlap and

∩ ∅

completeness. That means ( ∪ Si=Ι) and (Si Sj= ), where i and j

∈ [1, K], and K

is the number of

subclasses from the image I. In EM segmentation, an image is segmented into k classes. If we denote A as a mapping type which is composed of one or more classes in k classes, the mapping function is (2). πA,k = π(A,k) =

ℝ

2

where I(k) = {1, 2,

→ A

∈2

I(k)

(2)

….k} is the index set after EM

k-classes segmentation, and 2

I(k)

is all the subsets of

I(k). Additionally, we define some concepts for

Fig. 7. Anatomical structure analysis of brain image: original image – CSF (first rows) white matter – gray matter (second rows) Cerebral-spinal fluid (CSF) is continuously produced and absorbed and that flows in the ventricles within the brain and around the surface of the brain and spinal

familiarization of our algorithm: - The range of a function f is rng(f) = f(U) - The set of constrained size k:

∈ ⊳

V⊳k= {s 2V||s| k} for

⊳∈{>,≥,=}

- A Region of πA,k is the reverse function of range of

cord. In the gray scale range, WM is always the

unique index in k classes:

highest value and CSF is the lowest value among three

ρ(πA,k):=πA,k (rng(πA,k)=1)

regions. GM is in the middle range between WM and

-1

- An Union of two regions πA,k and πB,k is a region

CSF. However, the intensity transition from CSF to

which contains all elements in type A or B.

GM or from GM to WM is rather sensitive and

ρ∪(πA,k,πB,k):={r r’|r ρ(πA,k) r’ ρ(πB,k)}

difficultly determined. Fig. 7 is the anatomical structure

∪ ∈

∨∈

- A difference of one region of type A from another

analysis of brain image for three regions.

region of type B is a region which contains elements

3.2 Candidate Region Segmentation by Modified EM Algorithm

belong to type A and not type B.

∈

∧∉

ρ−(πA,k,πB,k):={r|r ρ(πA,k) r ρ(πB,k)} Based on these above concepts and the procedure

As we mentioned in the anatomical structure analysis section, if we directly segment a brain image

of 3 regions segmentation, the CSF, GM and WM are able to be represented in the formula (3) to (5).

Journal of KIIT. Vol. 14, No. 5, pp. 75-88, May 31, 2016. pISSN 1598-8619, eISSN 2093-7571 81

3.3 Candidate Region Refinement by Bayesian Estimation

CSF regions (formula 3): -1 -1 =1 =1 ρCSF:=πB,2 (rng(πB,2(πA,3 (rng(πA,3) )) )) where 2 I(3) πA,3=π(A,3)=ℝ → → A ∈ 2 πB,2=π(B,2)=ℝ2 → B ∈ 2I(2)and I(2)={1,2}, I(3)={1,2,3}, A={2,3}, B={1}

Although the modified EM procedure can get accurate segmentation results from a brain image into three regions (CSF, WM and GM), the intensity

WM regions (formula 4): -1 -1 =1 =1 ρWM:=πB,2 (rng(πB,2(πA,3 (rng(πA,3) )) )) where 2 I(3) πA,3=π(A,3)= ℝ \ ρCSFà A ∈ 2 2 I(2) πB,2=π(B,2)= ℝ \ ρCSFà B ∈ 2 and I(2) ={1,2}, I(3)={1,2,3}, A={2,3}, B={2}

transition from CSF to GM and GM to WM has no spatial information. That means there is still wrong case, in which a pixel is labeled as GM but the surrounding pixels is WM or CSF. For that reason, we decide a T percent of intensity transition from CSF to

GM regions (formula 5): 2 ρGM:=ρ−(ℝ \ρBG,ρ∪( ρCSF,ρWM)) where the background region is defined -1 =1 ρBG=πA,2 (rng(πA,2) ) and 2 {1,2} πA,2=π(A,2)=ℝ → A={1}∈2

GM and GM to WM, which is needed to verify whether its labels are correct or not by Bayesian Estimation. We call these pixels in these situations as unsure pixels. According to the Bayes decision theory, x is

In the Fig. 8-9, some sample images are applied by the

Modified

EM

segmentation

and

give

an

approximate results as the ground truth images. In OASIS database, the round truth image of each region is provided for the comparison.

assigned to the class ωi if P(ωi|x) > P(ωj|x)

∀i≠j or

taking into account of p(x) which is positive and the same for all classes, we have another condition: p(x|ωi)p(ωi) > p(x|ωj)p(ωj)

∀i ≠ j

Fig. 10. 3×3 window to verify some unsure pixels Fig. 8. Comparison of segmentation results with ground truth in slice 40

Fig. 9. Comparison of segmentation results with ground truth in slice 60

Fig. 11. Overall results of proposed approaches. 3-regions segmentation image – largest rectangle for Bayesian estimation process – unsure pixels – resultant Bayesian estimation – ground truth image (first row); CSF–WM-GM region segmentation in color (second row)

82 3D-Brain MRI Segmentation Based on Improved Level Set by AI Rules and Medical Knowledge Combining

A window size of 3×3 is scanned around the center

choice for this problem. However, there are some

area of brain to verify some unsure pixels. According

cases; the fuzzy level set does not work well. The Fig.

to experimental observations, the wrong segmentation

13(c) is the evidence of undesired brain segmentation.

areas mostly belong to the center area of a brain (in

Some regions of brain are miss- segmented by fuzzy

Fig. 10). For that reason, a simple largest rectangle is

level set.

found inside the brain and then Bayesian Estimation

Our proposed method of the combination of Fuzzy

process will continue to verify these unsure pixels. Fig.

Level Set and AI rules helps to increase the accuracy

11 shows us the overall results of our proposed

and get more brain regions for the skull-stripping

algorithm.

problem. The Fig. 14 shows us some testing results of skull-stripping by our modified Level Set methods. IV. Experimental Results

4.1 Database There are some databases available on Internet for MRI Brain images. But the qualities of them are quite different from each others. The darkness or brightness of some tissues in a MRI image depends on the density of protons in some areas. Relaxation times for protons can vary and two types of measured images: T1 and T2. White matter is darker than gray matter

Fig. 12. Some MRI images from three databases : Korean Chonnam hospital database, IBSR database, and OASIS database

in T1-weighted images, and brighter than gray matter in T2-weighted images. The Fig. 12 represents some images from our three main databases: Korean Chonnam Hospital database, IBSR database, and OASIS database. The images are T2 formats and higher quality in Korean Chonnam Hospital database than the images in T1 format and lower quality. In these databases, one subject has about 170 slices from MRI scanning machine but there is only 140-150 images

Fig. 13 (a) Regular level set with 500 iterations. (b) Fuzzy level set with 400 iterations. (c) Undesired fuzzy level set result in Korean hospital database

containing brain information.

4.2 Modified Level Set and Morphology Active Contour Evaluations The conventional level set method gives wrong segmentation results, although we increase the number of iterations (400-500 evolution iterations). For example, let’s see Fig. 13(a-b) in the case of regular level and fuzzy level set. The fuzzy level set seems to be a good

Fig. 14. Result of skull-stripping by our modified level set in the combination of AI rules: Images in above are original images, and images in below are segmentation results

Journal of KIIT. Vol. 14, No. 5, pp. 75-88, May 31, 2016. pISSN 1598-8619, eISSN 2093-7571 83

Fig. 15. Result of skull-stripping by morphology active contour approach: first row is original images, and second row is brain segmentation results In comparison with AI rules-based level set, the morphology active contour is simpler and faster but unsuitable for the complex cases. Specially, if the brain area is complex and unconnected, the morphology active contour gives wrong results. Moreover, the morphology active contour approach is only able to remove the outer skull boundary, while the level set approach is able to remove the inner bone of the head. In conclusion, the AI-rules level set approach is more promising than morphology active contour approach in complete skull-stripping problems. Fig. 15 shows us some results of skull-stripping by morphology active contour approach.

4.3 Three class (WM, GM and CSF) segmentation evaluations In experimental analysis, we realize that the EM can

Fig. 16. Comparison of segmentation results with ground truth in slice 90

4.4 Classification Measurements In order to compare the classification results of our algorithm with the results of ground truth classification obtained from a doctor observation, we choose two metrics. One is Dice Percent and the other is Haussdorff Distance. Dice percent represents the overlap percentage of two segmentation images. Haussdorff represents how far two segmentation from each other. Table 1 shows us Dice percent and Haussdorff distance based on all slices of 5 patients. Dice Percent: S(X,Y)=2|X

∩ Y|/|X|+|Y|

Haussdorff distance: d H ( X , Y) = max{sup inf d(x,y) - sup inf d(x,y)} xÎ X

yÎY

xÎY

yÎ X

segment a brain image into background, WM, GM and

From Table 1, we see that, although we make the

CSF successfully. But some pixels have the gray scale

comparison of all slices (slice: 1 to over 150) from

value in the transition from CSF to GM or GM to

one patient, the similarity of our segmentation and the

WM and they are classified wrongly. The Bayesian

observation of doctors in OASIS database is about 80%

estimation process plays a correction role in re-estimate

for all classes: WM, GM and CSF. This results of

these wrong classified pixels into its proper class.

about 150 slices are quite high and promising, while

Based on the spatial information between certain WM,

OASIS database is a database of low quality and T1

CSF and GM pixels, the re-estimation process gives

format. Besides, the max of differences from our

correct results with the percentage of re-estimated

classification to ground truth image is rather small and

pixels from 10% to 30%. The Fig. 16 shows us some

acceptable less than 0.5mm. In other additional

three-class segmentation results. We can see that each

experiments, we conduct to examine two approaches of

class is mostly similar to the ground truth class

skull stripping methods using Active contour morphology

provided by a doctor.

and AI-based level set.

84 3D-Brain MRI Segmentation Based on Improved Level Set by AI Rules and Medical Knowledge Combining

Table 1. Dice percent and max of Haussdorff distance from comparison of our segmentation and doctor observation on OASIS database Person ID Dice Percent(%) Max Haussdorff (all slices) distance(mm) WM: 83.41 1 GM: 86.14 0.23033 CSF: 79.78 WM: 82.61 2 GM: 83.27 0.35193 CSF: 74.43 WM: 83.35 3 GM: 77.30 0.42965 CSF: 71.43 WM: 83.83 4 GM: 82.27 0.21029 CSF: 81.80 WM: 81.45 5 GM: 81.97 0.35601 CSF: 75.13 Table 2. Dice percent comparison between active contour morphology and AI-based level set results Active AI-based Dice Percent Original Contour Level (Dice 1:ACM Image Morphology (ALS)Set and Dice (ACM) Result 2:ALS) Dice1 = 86.57 % Dice2 = 85.79 % Dice1 = 94.66 % Dice2 = 92.73 % Dice1 = 92.99 % Dice2 = 91.08 % Dice1 = 70.94 % Dice2 = 84.05 % Dice1 =0% Dice2 = 70.49 %

The dice percent measurement is applied to compare our skull stripping results with the ground truth information. Because the OASIS database provides us the ground truth information, this database is used for this experiments. Taking a glance at Table 2, we recognize that all of testing images are representations of all cases of MRI Brain Image from the top to the middle of human brain. These testing image guarantees that our cases cover all possible shape of a human brain. Another notice is that the 1, 2, 3th rows has the Dice 1 (dice percent of Active Contour Morphology-ACM) greater than Dice 2 (dice percent of AI-based Level Set-ALS) but the 4 and 5th rows (with bone inside the brain) has the Dice 1 less than Dice 2. The more value of dice percent means the more accurate skull stripping results in comparison to ground truth information. From the observation, we concluded that ACM approach gives a better result than ALS approach if a slide has no much bone inside the brain. Otherwise, the ALS approach gives a better result than ACM approach if a slide has th no bone inside the brain. Especially, in the rows 5 , we see that the ACM result is empty, while ALS result is rather good. Overall, the ALS approach is well-performed than ACM approach in most of cases. The modified EM and Bayesian is then applied based on the skull-stripping results for 3 class (WM, GM and CSF) classification. In order to evaluate the effectiveness of Modified EM and the combine of modified EM +Bayesian, we measure the performance based on the ratio of grayscale range of each WM, GM, and CSF over the ground truth’s gray-scale range of WM, GM and CSF classes. From Table 3, although the modified EM can help us to classify an MRI Image into WM, GM and CSF classes, there are still much wrong classified pixels. The reason is that EM does not care about spatial information or relative information with adjacent information. For that reason, Bayesian method becomes effective and appropriate to correct all uncertain pixels (mainly pixels in the transition from CSF to GM and GM to WM).

Journal of KIIT. Vol. 14, No. 5, pp. 75-88, May 31, 2016. pISSN 1598-8619, eISSN 2093-7571 85

Table 3. Comparison of three classes (WM, GM and CSF) classification of only modified EM and combination with bayesian classifier Three classes Modified EM classification Modified EM+ Bayesian classification Original Image classification by (ratio of each range over ground truth (ratio of each range over ground truth modified EM range) range) Ratio GM = 68 % Ratio GM =78.43 % Ratio WM = 83.75 % Ratio WM = 95.79 % Ratio CSF = 85.26 % Ratio CSF = 99.35 % Ratio GM = 93.33 % Ratio GM = 97.05 % Ratio WM = 82.11 % Ratio WM = 95.13 % Ratio CSF = 71.20 % Ratio CSF = 95.15 % Ratio GM = 95.52 % Ratio GM = 99.99 % Ratio WM = 85.62 % Ratio WM = 99.98 % Ratio CSF = 65.21 % Ratio CSF = 100 % Ratio GM = 70.74 % Ratio WM = 78.33 % Ratio CSF = 78.54 %

Ratio GM = 97.01 % Ratio WM = 90.36 % Ratio CSF = 90.53 %

Look at Table 3, the ratio increases in most of the

but also some bones in a brain image. The EM and

cases from modified EM to the modified EM with

Bayesian approach then perform to classify a brain into

Bayesian

some

three classes: White Matter (WM), Gray Matter (GM)

promising and effective solutions for two problems in

and Cerebro-Spinal Fluid (CSF). The main contribution

MRI

in this classification stage is that we apply EM in

approach.

Brain

image:

The

results

Skull-stripping

show and

us

3-classes

classification.

multiple steps to classify some certain pixels and Bayesian to re-estimate some uncertain pixels into three V. Conclusion

classes. The uncertain pixels are pixels belonging to the transition from GM to WM or CSF to GM. Some

In this paper, we introduce a new approach for

experimental results showed that the proposed method

automatic brain MRI segmentation based on improved

has similar segmentation performance with doctor’s

level set with AI rules and medical knowledge

ground truth observation.

combining Bayesian method. This approach contributes

In the future, based on brain MRI segmentation, we

into the skull-stripping and 3-class (WM, GM and

will develop features for dementia detection and a

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program of automatic dementia diagnosis.

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88 3D-Brain MRI Segmentation Based on Improved Level Set by AI Rules and Medical Knowledge Combining

Tran Anh Tuan

Atsuo Yoshitaka 2005 : B.S. degree, 2010 : M.S.

1989 : B.S., 1991 : M.S., 1997

degree, in Faculty of

Ph.D. degrees in Hiroshima

Mathematics & Computer

University, Japan

Sciences, University of Sciences,

1997 ~ now : Professor, Japan

HCMC.

Advanced Institute of Science

2010 ~ now : Lecture of Faculty

and Technology, Japan.

of Mathematics & Computer Sciences, University of Sciences, HCMC. Research interests : software development and medical image processing

Research interests : content-based retrieval for multimedia databases, image/video indexing, affective information processing, and visual user interfaces

Nguyen Hai Duong

Jin Young Kim 2015 : B.S. degree in Department

1986 : B.S., 1988 : M.S., 1994

of Mathematics & Computer

Ph.D. degrees in Department of

Sciences, University of Sciences,

Electronic Engineering, Seoul

HCMC.

National University

2015 : Teaching Assistant, Depart-

1995 ~ now : Professor in

ment of Computer Science,

Department of Electronics and

University of Science, HCMC.

Computer Engineering, Chonnam National University

Tran Anh Tuan

Research interests : Audio-Visual signal processing and 2005 : B.S. degree in Department

embedded system

of Mathematics & Computer Sciences, HCMC University of

Seung Ho Choi

Natural Sciences.

1992. 2. : Ph.D. degrees in

2008 : M.S, degree in Department

Department of Electronic

of Computer Science, HCMC

Engineering, Myungji University

University of Science

1992 ~ now : Professor in

2014 : Ph.D. degree at Chonnam National University

Department of Computer

2014 ~ Now : Lecturer of Department of Computer

Science, Dongshin University. Research interests : Speech and

Nguyen Kim Dao

image signal processing 2006 : B.S. degree in Hue University,

Pham The Bao

2013 : M.S. degree, in Vinh

1995 : B.S., 2000 : M.S., 2009

University

Ph.D. degrees in University of

Now : Ph.D student in

Science

Department of Mathematics &

1995 ~ now : Professor in

Computer Sciences, University

Department

of Sciences, HCMC Research interests : education methodology and computer science

Computer Science,

Faculty of Mathematics & Computer Science, University of Science Research interests : Image processing & pattern recognition, intelligent computing