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An Improved Region Growing based on Bilateral Symmetry Information for MRI Brain Tumors
Segmentation
Mohamed Alji, Mounir Kerroum
To cite this version:
Mohamed Alji, Mounir Kerroum. An Improved Region Growing based on Bilateral Symmetry Infor-
mation for MRI Brain Tumors Segmentation. The First International Conference of High Innovation
in Computer Science (ICHICS’16), Jun 2016, Kenitra, Morocco. �hal-01899598�
An Improved Region Growing based on Bilateral Symmetry Information for MRI Brain Tumors
Segmentation
ALJI Mohamed
Laboratoire de Recherche en Informatique et Telecommunications (LaRIT) http://larit.uit.ac.ma
Faculty of Sciences, Ibn Tofail University.
Kenitra, Morocco [email protected]
Mounir Ait Kerroum
Laboratoire de Recherche en Informatique et Telecommunications (LaRIT) http://larit.uit.ac.ma
ENCG-K, University of Ibn Tofail Kenitra, Morocco
Abstract—Both halves of the brain exhibit a high level of bilateral symmetry. A tumor presence destroys this symmetrical property of a healthy brain. Based on this anatomical knowl- edge and knowing that human intervention in any interactive segmentation method is subject to inter-user and even intra- user differences, we propose an improvement of a seeded region growing method for segmenting tumors in a magnetic resonance imaging scans by transforming it from being interactive to fully automatic. Firstly, we establish a map representing the probabilities of asymmetry occurrences. Secondly, we extract information from the asymmetry map, allowing automatic seeds selection to initialize the seeded region growing method in the region of interest. Thirdly, from the selected seeds we define a suitable homogeneity criteria representing the tumor region.
Finally, we apply the modified region growing with the chosen seeds and the predefined criteria on the volumetric magnetic resonance scans in order to extract a pathology mask and reconstruct a 3D representation of the tumor. The segmentation results and the 3D reconstruction are promising.
Index Terms—Segmentation, Region growing, MRI, Brain tumors, Symmetry analysis, Three dimensional reconstruction.
I. INTRODUCTION
A brain tumor segmentation is an image analysis method that aims to isolate the tumoral cells from the healthy brain in an established scan, which can be an in-vivo magnetic resonance imaging (MRI) scan. Detection of a tumor presence, localization, diagnosis, staging, and monitoring treatment re- sponses are crucial procedures in oncology as stated by N.
Gordillo [1]. An accurate delineation of the tumor structure in medical imaging, such as MRI, helps a neurologist to figure out the right treatment to establish for the patient.
In the particular case of brain tumors, segmentation consists of separating the tissues of the tumor from the tissue of the healthy brain. Manually, the neuroradiologists perform an empirical delineation or segmentation of the brain tumor. This task is a time consuming, and operator-dependent. Fortunately, specialized software and hardware technology allow as devel- oping computer-aided diagnosis (CAD) systems. CAD systems can assist in routine clinical practice [2], but an accurate
segmentation of the brain tumor is still a challenging problem because of the complex shape of the tumor, location and the overlap between the tumoral and the healthy tissue of the brain.
Even the manual segmentations show inter-rater variations [3].
Fig. 1. Example of a tumor (blue curve) and edema (red curve) segmentation results for T1C, T2, FLAIR and T1 MRI images respectively (the figure is adapted from the article [4]).
There is plenty of brain tumor segmentation methods. The reader can refer to the state-of-the-art survey on MRI brain tumor segmentation [1] for further reading. According to the degree of required human interaction, a brain tumor segmen- tation method can be classified into three categories: manual, semiautomatic, and fully automatic segmentation. The primary task is to establish the most accurate segmentation method and require less human intervention. The seeded region growing [5] is a semi-automatic segmentation method that needs the user to localize the initial seeds from which the algorithm will start and grow to extract meaningful regions of the patient brain.
Since it has been proven to be difficult to perform a fully automated segmentation on brain tumors based solely on the information contained in the image, integrating anatomical knowledge will intuitively increase the utility of the computer- aided diagnosis systems in helping clinicians to make the right decision [2]. In our case, the anatomical knowledge is the high level of symmetry between the left and the right halves of the healthy brain. A pathology presence could break this relative symmetry. Such fact can somehow be incorporated into the segmentation method to improve its results. The aim
of this article is to transform the region growing method into an automatic segmentation method by exploiting the relative symmetry between the two halves of a healthy brain.
The paper is organized as follows. Section II gives a description of the classical method. Then, we elaborate on the proposed method. Section III discusses the results of our method. Section IV concludes the paper.
Fig. 2. Human brain exhibits a high level of symmetry between its two halves (Credit to EUSKALANATO changes were made to the original photo).
II. METHODDESCRIPTION
Region growing is one of the simplest region-based segmen- tation methods that mean to segment an image into regions [6]. It allows extracting a group of connected pixels that have a similar criterion from a starting sub-regions or seeds [1].
As its name implies, the algorithm of the region growing starts at least with one seed that belongs to the element of interest and then ”grows”. Neighbors of the concerned seed are checked on a predefined criterion of growth. If they satisfy the homogeneity or the similarity criterion, then the pixels are added to the active output region. The algorithm iterates until no more pixels can be added to the region. The similarity criterion can be a range of pixel intensities or any feature from the image such as the average intensity of the original seeds or color. The advantage of the region growing segmentation method is its capability to segment correctly the objects of an image that have similar properties and generate connected regions.
In the region growing segmentation process, there are two factors to be taken into consideration [7]. The first one is how to choose the initial seeds in order to get a good segmentation result in dependence on the type of the segmentation problem, and by that avoid an interactive method that is subject to inter- user differences in the results. The best solution is to find the initial seeds by an automatic selection process. The second factor is how to choose a suitable similarity criterion to well discriminant the pixels that represent meaningful regions.
The method has some drawbacks. It is sensitive to noisy data and depends on the initial seeds selection. Suppose the case of the original seeds corresponding to outliers. That means
Fig. 3. The flow chart of the proposed method
the extracted feature from the selected pixels to establish the similarity criterion is not representative of the desired segment
being extracted.
It also suffers from the partial volume effect that blurs the intensity distinction between two, or more, tissues because the voxel/pixel may represent more than one kind of tissue types. In the article [8], M. Sato proposes to incorporate the gradient magnitude to the region growing algorithm to enhance the boundary definition. By applying its simple algorithm, the problem of the partial volume effect on the boundary was solved in the case of colon segmentation.
Morphologically speaking, a normal human head exhibits a high level of bilateral symmetry [2]. Corresponding regions of two hemispheres have approximately identical anatomical properties. A pathology presence breaks this symmetry. The idea is to exploit this asymmetry to detect, localize and segment a tumor in the brain.
Our segmentation method is represented in the flow chart 3. It consists of five steps.A parallelogram represents each step. In the first step, the preprocessing is skull stripping only. In the second phase, the proposed method computes an asymmetry map based on the detection of the symmetry axis or symmetry plane in case of volumetric approach. The detection of symmetry axis is a challenging task due to the relative symmetry of the brain and the possible misalignment of the patient’s head in the MRI scanner. The third step consists of finding the initial seeds based on the asymmetry map. How is that? The asymmetry map shows pixels that are very likely to be asymmetric in both halves of the brain. A presence of connected pixels of this king means a possible presence of the tumor. A threshold of the asymmetry map can be established to allow the extraction of only the highly probable pixels that can be used as initial seeds for the proposed method. The fourth step is to define a suitable homogeneity criteria representing the tumor properties. Based on the sub-region of the initial seeds, the homogeneity criterion can simply be a range of intensities or a more sophisticated set of criteria that take into consideration a possible shape and size of the tumor. The fifth step is to apply the classical region growing method using the previous initial seeds and the defined homogeneity criteria to segment the tumor. This step produces a pathology mask from that a 3D tumor reconstruction can be done.
III. RESULT ANDDISCUSSION
To measure the segmentation results of the proposed method, we perform several tests on a well-known dataset of a magnetic resonance scans of low- and high-grade glioma patients with ground truth from manual delineations by several human experts and realistically synthetic brain tumor dataset with its corresponding known golden truth [9].
The quantitative comparison between the binary pathology mask and the ground truth shows a promising result and can be measured using the Dice Similarity Coefficient (Dice), Accuracy Coefficient (AC), Sensitivity and Specificity.
The previous similarity coefficients measure pixel-wise (pixel by pixel) the spatial overlap between the segmented regions and the ground truth. Dice [10], for example, varies from 0 to 1. One means a total overlap of the segmented
Fig. 4. a patient brain with a simulated high-glioma tumor (yellow).
Fig. 5. A computed asymmetry map based on the possible symmetry between both halves of the brain.
region. A value between 0 and 1 means partial overlap and a dice value of 0 means no overlap. Its formulas are as follow:
Dice= 2∗T P
2∗T P +F P +F N (1) Accuracy= T P +T N
T P+F P +F N+T N (2)
Sensitivity= T P
T P +F N (3)
Specif icity= T N
T N+F P (4)
where :
TP True Positive, Pixels correctly segmented as a tumor in ground truth and by our method.
FP True Positive, Pixels not segmented as a tumor in ground truth, but considered as a tumor by our method.
TN True Negative, Pixels not classified as a tumor in ground truth and by our method.
FN False Negative, Pixels classified as a tumor in ground truth, but not designated as a tumor by the proposed method.
The score of Dice can be evaluated up to 0,89 for some slices corresponding to the segmentation of a simulated tumor
Fig. 6. Comparison of a binary segmentation (Seg) with the reference image (Ref) or simply the truth, with the true positive pixels (TP), the true negatives (TN), the false positives (FP) and the false negatives (FN). The figure is adapted from [11]
of high-glioma. As shown in figure 7, the 3D reconstruction of the tumor reveals promising results.
Fig. 7. Segmentation result of a simulated pathology reconstructed in 3D
IV. CONCLUSION
We presented a new fully automatic approach for a region- oriented segmentation method of MRI scans by combining the asymmetry map computation and a classical segmentation method. We intend to advance our method and establish full results using all the previous metrics on the datasets of BRATS 2012 http://www2.imm.dtu.dk/projects/BRATS2012/. We will also evaluate the performance of the segmentation using
another class of scores that consider the distances between segmentation boundaries. The results are in progress.
Further method improvements are remaining such as: com- bining the segmentation results of multiple imaging modalities, finding the right set of criteria that may characterize the tumor region, etc.
ACKNOWLEDGMENT
I would like to thank M. Youssef FAKHRI and my col- leagues of LaRIT Laboratory.
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