A Hybrid Segmentation Approach for Brain Tumor ...

A Hybrid Segmentation Approach for Brain Tumor Extraction and Detection Najlae Idrissi*

Fatima Ezzahra Ajmi

TIT-Team Faculty of Sciences and Techniques Beni Mellal, Morocco Email: idrissi [email protected]

Faculty of Sciences and Techniques Beni Mellal, Morocco Email: [email protected]

Abstract—Nowadays, medical image processing and particularly MRI images is the one of the most challenging field and emerging to help specialists in their diagnostics. In this context and to detect automatically suspicious regions or tumors, this paper presents a new approach called hybrid segmentation inspired by both mathematical morphology operators and morphological watershed segmentation. Our approach’s advantage comes from the complementarity between these two approaches. The morphological operators extract roughly the tumor region and eventually can affect healthy structures while the watershed method provides details of various brain’s structures and therefore the fusion of these two approaches improves significantly the segmentation and the extraction of the tumor zone. Keywords—MRI, brain tumor, segmentation, mathematical morphology, watershed, fusion.

I.

I NTRODUCTION

Nowadays, brain tumor is one of the main cause for increasing mortality among children and adults. It has been concluded from the research of most of the developed countries that number of people suffering and dying from brain tumors has been increased to 300 per year during past few decades[1]. More than 612,000 Americans will be diagnosed with a brain tumor and over a million people in the United States are living with brain tumors that have not yet been detected according to the National Brain Tumor Fondation (NBTF) and American Brain Tumor Association (ABTA)[2] where approximately 4,300 children younger than age 20 will be diagnosed with primary brain tumors, of which 3,050 will be under age 15. As this number is candidate to increase, tools and methods to detect, extract the tumors and to analyze their behavior are becoming more widespread and must take into consideration the type of tumor, the type of images to be used and depending therefrom, the different approaches to use or to develop.

(primary, metastatic) and their models of growth of malignity (benign, malignant). 1) Primary tumor: A primary tumor is tumor that arises from cells in the brain or from the covering of the brain. It has started in the brain. The most common primary brain tumors are: gliomas, meningiomas, pituitary adenomas, medullablastomas and central nervous system [6]. 2) Metastatic tumor: A metastatic or secondary tumor is the one that has spread to the brain coming from another part of the body. Secondary tumors are about three times more common than primary tumors of the brain. Solitary metastasized brain cancers may occur but are less common than multiple tumors. Most often, cancers that spread to the brain to cause secondary brain tumors originate in the lung, breast, kidney, or from melanomas in the skin [6]. 3) Benign tumor: Benign tumors represent half of all primary brain tumors. Their cells do not progess abruptly; but look relatively normal, grow slowly, and do not spread (metastasize) to other healthy tissues in the body or invade brain tissue. Moles are the common example of benign tumors. 4) Malignant tumor: A malignant tumors are usually a fast growing cancer that spread to other areas of the brain and spine and invade neighboring tissues but they rarely spread to other areas of the body or may recur after treatment. B. Brain tumors’s images To identify which tumor is it, the patient must perform several imaging tests that can produce a detailed picture of the brain. Computed tomography (CT) and magnetic resonance imaging (MRI) are most commonly used to locate brain tumor.

The word tumor, also called neoplasm, is defined as the abnormal growth of the tissues that results when cells divide more than they should or do not die when they should[5].

1) CT scans: Computed Tomography (CT) is a scanning technique allowing the generation of tomographic images of every part of the human body without overlapping of surrounding structures using special X-ray equipment to create crosssectional pictures the body. These scans provide important information about the cause of the stroke and the location and extent of brain damage. CT scans are clearer pictures of the brain than regular X-rays(Fig.1).

Brain tumor is an abnormal mass of tissue in which cells grow and multiply uncontrollably, seemingly unchecked by the mechanisms that control normal cells. Two large families are recognized and which depend on the origin of the tumours

2) MRI images: Magnetic Resonance Imaging (MRI) is the most advanced medical imaging technique employs a strong magnetic field and radio waves to produce high quality images of each part of the body at issue by visualising their

A. Brain tumor

The suggested method consists of four steps: the first step called pre-treatment is the enhancement process applied by various filters. The second step is the image segmentation based on mathematical morphology operators and morphological watershed. The next step is the fusion of the fusion of the images obtained from the segmentation step performed prior. The last step is the extraction of localised brain tumor. The tests carried out and results are discussed in section IV. Finally concluding remarks and some perspectives are drawn in section V. II. Figure 1: CT scan of brain tumor[4]

I MAGE SEGMENTATION : R ELATED WORK

In the litterature, the image segmentation is a process aiming to decompose an image into a set of homogeneous regions (classes/subsets) according to one or more criteria. Color (or gray level), texture, the form, surface normal and surface curvatures are the widely used homogeneity criteria and often selected[13], [14], [15], [12]. Their choice is usually based on the nature of the treated images and the objectives set[11], [8]. As time consumption of brain tumor segmentation done by medical experts from MRI images is a crucial drawback, many segmentation techniques are developed by the image processing researchers highlighting them[17], [18], [16].

Figure 2: MRI brain image finer details of its internal structure(Fig.2). MRI is often used when treating brain tumours or other cancers. From these high-resolution images, we can derive detailed anatomical information to examine human brain development and discover abnormalities. This technique mainly used to detect the differences in the tissues and structures is much better than computed tomography for tracking the size of brain tumor and other brain related problems[7]. C. Brain MRI images processing The MRI-scan images are an ideal source for detecting, identifying and classifying the right brain tumor but the most of the current conventional diagnosis techniques are based on ”‘visual/mental”’ human experience for interpreting and judging these images which can increases the possibility to have false detection and identification of the brain tumor [8]. So, many researches have been conducted to automate the analysis of these images applying digital image processing that ensures the quick and precise detection of the tumor[8], [4]. One of the most efficient techniques to extract information or region of interest (ROI) from MRI images that has wide application in medical field is the segmentation process[9], [10] The proposed work is mainly based on segmentation and extraction of the tumor region for further analysis such as classification and identification. The remainder paper is organized as follows. In section II, some related image segmentation techniques are summarized. Description of the proposed approach for automatic tumor detection from MRI brain images is presented in section III.

Indeed, in medical imaging, segmentation is very important task and constitutes a strong challenge to accurately detect the tumor zone. The problem of segmentation of tumors in MRI can be approached under two angles. The first one concerns the objective pursued by the application[8] as to: •

consider the tumor as the only purpose of the segmentation and then use a dedicated method to this task;

•

or to consider the tumor, and possibly oedemas, as individual entities of the brain; then the segmentation is to isolate all brain structures.

The second point of view is the choice to use different acquisition types or ponderations (T1, T2, PD, ARM, ...) of IRM images[19], [20], each providing a series of slices and different resolutions have been used, in : •

Unimodal: use only one type of acquisition as T1 or T2;

•

multimodal: combine several acquisitions under different contrast.

Several common approaches have appeared in the recent literature on medical-image segmentation[21], [22]. We give a brief description of each one. Although each technique is described separately, multiple techniques may often be used in conjunction for solving different segmentation problems. For more details, refer to [21], [22], [23]. Existing methods for image segmentation can be classified into two major classes according to two points of view: image processing and pattern recognition[8], [24] providing the following categories: •

Threshold based segmentation. Histogram thresholding and slicing techniques are widely used to segment the image[32], [33]. They may be applied directly to

an image, but can also be combined with pre- and post-processing techniques. •

Edge based segmentation. With this classical technique, an edge filter is applied to the image wherein detected edges are assumed to represent object boundaries, and used to identify these objects. Edge-based models[28], [29], [30], [31] use local edge information for image segmentation to classify pixels as edge or non-edge according to the used filter. These models do not assume homogeneity of image intensities, and thus can be applied to images with intensity inhomogeneities. However, this type of methods are in general quite sensitive to the initial conditions and often suffer from serious boundary leakage problems in images with weak object boundaries and low contrast.

•

Region based segmentation. Region-based models[25], [26], [27] aim to identify each region of interest by using a certain region descriptor to guide the motion of the active contour. However, it is very difficult to define a region descriptor for images with intensity inhomogeneities. Most of region-based models are based on the assumption of intensity homogeneity. Where an edge based technique may attempt to find the object boundaries and then locate the object itself by filling them in, a region based technique takes the opposite approach, by (e.g.) starting in the middle of an object and then growing outward until it meets the object boundaries.

•

•

Clustering techniques. Although clustering is sometimes used as a synonym for (agglomerative) segmentation techniques, we use it here to denote techniques that are primarily used in exploratory data analysis of high-dimensional measurement patterns. In this context, clustering methods attempt to group together patterns that are similar in some sense. This goal is very similar to what we are attempting to do when we segment an image, and indeed some clustering techniques can readily be applied for image segmentation. Statistical approaches. These approaches are based on a statistical modeling of images that consists of a measurement model and a prior model. Each anatomical structure is associated with a class of which is calculated statistical characteristics. In this context, each case is considered as the result of a stochastic process and is associated with a set of a random variable. The main problem is the estimation of the probability densities ”‘measurement model”’ from observations and that the decision knowing these densities”’a prior model”’. For estimating probabilities the most used algorithms are Expectation-Minimization (EM) or Maximum a Posteriori (MAP) [38], [39] and for measurement model is supposed that each region of image is associated with a specific distribution and then the probability density of the image is a mixture of probability densities. In general, the Gaussian mixture is considered. The prior model takes into account smoothness and piecewise contiguous nature of the tissue regions and is modeled by a 3-D Markov random field (MRF) which is robust to noise[22].

•

Deformables models. These techniques derived from the fundamental work introduced by Kass and al.[34] are widely used in segmenting medical images[35]. Deformable models are curves or surfaces defined within an image domain that can move under the influence of internal forces, which are defined in the inner of curve or surface itself, and external forces, which are computed from the image data. The internal forces are performed to keep the model smooth during deformation. The external forces are defined to move the model toward an object boundary or other desired features within an image[36], [37]. Compared to other contours methods, deformable models have two main advantages: the ability to directly generate closed parametric curves and surfaces from images and to incorporate a smooth constraint into the model. Various names, such as snakes, active contours or surfaces, balloons, and deformable contours or surfaces, have been used in the literature to reference to deformable models[46], [45], [47].

•

Evidence theory. Also known as the Dempster-Shafer theory, the evidence theory originates from the work of Dempster on the lower and upper bounds[40]. Shafer in his work[41] formulated the evidence theory as an extension to the probability theory which take into account the imprecision and the uncertainty of MR images to treat such imperfect data. Moreover, this theory provides combination tools to merge data issued from several sources (MR acquisition protocols) while taking into account their complementarity, their redundancy and their possible opposition. Thus, this theory is convenient to a multi-echoes segmentation approach[42], [43].

III.

T HE PROPOSED APPROACH

The main idea of the proposed hybrid approach is to take advantages of both approaches (Mathematical Morphology (MM)and Watershed Method (WM)) to attain an efficient and reliable result of extraction of the tumor. The segmentation process is summarized in Figure 3. The proposed approach is made up of of three main phases. The preprocessing phase to enhance brain MRI images, the segmentation process or the implementation of two approaches MM and WM and then the fusion phase to extract tumoral zone.

A. Preprocessing In this phase, image is enhanced by removing existing noise and improving the finer details. Many filters like linear filter, Gaussian or average filters are used to remove the noise from images. In this paper we used the median filter because it is less sensitive to outliers. The value of pixel is determined by the median of the neighboring pixels. For the contrast enhancement, it can be achieved by using different mathematical morphology operators or wavelet transform. Here, we use Gaussian high pass filter to enhance the boundaries of the structures in the image.

Unfortunately, this transformation very often leads to an over-segmentation of the image. To overcome this problem, Meyer and Beucher proposed a strategy called markercontrolled segmentation[54]. This approach is based on the idea that machine vision systems often roughly ”know” from other sources the location of objects to be segmented. We used this approach in our work. C. Fusion The last part combines the results obtained from morphological operators and the watershed method to detect and extract the tumoral zone from MR images. IV.

E XPERIMENTS AND RESULTS

Figure 4 illustrates the various results got during the process of segmentation. This is a visual evaluation. The first line (a) presents the original brain MR images considered as input of our systme, each one contains a tumor. (b) and (c) represent the binary images obtained using erosion and dilataion functions respectively. The final result of applying mathematical morphology is shown in (d). The results of watershed approach are illustrated on (e). Final segmented images based on the fusion of the two techniques previously mentionned is provided in (f). The white area corresponds to the zone tumour. Figure 3: Schema of our proposed approach

B. Segmentation process After enhancing images, we proceed to the segmentation by two approaches : mathematical morphology and watershed method in parallel as shown in figure 3. 1) Mathematical morphology: After enhancing initial image using adaptive threshold depending on the intensity of the image, we applied some morphological operators introduced by G. Matheron [48] and J. Serra [49] that allow the transformation of an image based on its shape. Morphological operations may be considered as form filters that eliminate information from an image according to the shape of objects in the image and retaining only the interest information in the image. There are two basic morphological operators: erosion and dilation. Opening and closing are two derived operations in terms of erosion and dilation. These operators are based on some structural element. For more details, see[50].

To be more objective, we use two quantitative criteria namely Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity (SSIM)[44] to measure the quality of the segmentation. The PSNR is the ratio between the original image Io and the segmented one Is . The higher the PSNR, the closer the segmented image is to the original while the SSIM is based on the idea that the human visual system is highly adapted to process structural information, and the algorithm attepts to measure the change in this information between and original and segmented image. PSNR and SSIM formula are given respectively by equation eq:psnr and eq:ssim. ( P SN R(Io , Is ) = 10.log10

M AX 2 M SE(Io , Is )

) (1)

where the MSE is the mean error square.

SSIM (Io , Is ) =

(2µo µs + c1 )(2σos + c2 ) + µ2s ) + c1 )(σo2 + σs2 + c2 )

(µ2o

(2)

2) Watershed method: Watershed transform is a powerful tool for image segmentation, initially proposed by Digabel and Lantu e´ joul[51] and later improved by Beucher and Lantuéjoul[52] for segmenting images of bubbles and SEM metallographic images.

where µx , σx2 and σxy are respectively mean of image x, variance of image x, covariance between image x and y. ci is constant. Based on[57], the value of c1 is (0.01x255)2 and c2 is (0.03x255)2 .

The Watershed transformation can be classified as a regionbased segmentation approach. The idea intuitive behind this method comes from geography: it is that of a landscape or topographic relief which is flooded by water. Watersheds being breaklines of attraction areas of rain falling on the area. As a result, the region is partitioned into basins separated by dams, called watershed lines or simply watersheds. More details are found in[55], [53], [56].

Tab I summarizes results obtained by calculating the PSNR and SSIM measure quality between original images and segmented one. The improvement rates obtained by PSNR exceed largely the 90% comparing our approach with only mathematical morphology though there is a slight improvement in the comparison with the watershed. As regards the SSIM criterion, the values fluctuate between 20% and 70% for the watershed and between 40% and 75% for the mathematical morphology.

exploit this approach with other mathematical models in order to refine more the segmentation and the extraction of the tumours for a better diagnosis as well modeling each tumor. R EFERENCES

Figure 4: Results of our proposed approach: Hybrid segmentation based on Morphological operators and Watershed: (a)-original brain MR images containing tumors; (b)-images results obtained using the morphological operators; (c)-images results obtained using watershed method; (d)-detected tumors as white portion on segmented MR images; (e)- manually segmented brain MR images. That enables us to conclude that the results are promising and our approach improves obviously and quantitatively the desired segmentation. V.

C ONCLUSION

The main objective of this work is to help the specialists in their diagnosis for detecting and extracting cerebral tumours. For this, we presented a hybrid approach in order to exploit strongly the advantages provided by the used approaches. Our approach is based on the fusion of the best results got respectively by mathematical morphology and the watershed after obviously a pretreatment which is used to clean the images of all that can deteriorate our results and in order to extract clearly and reliably the zone tumour in cerebral MR images. The tests were carried out on a set of MR images and the got results are encouraging and promising. We noted that the watershed provides better results than mathematical morphology, therefore our future works is to

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