Research Paper AUTOMATIC STROKE LESION SEGMENTATION FROM DIFFUSION WEIGHTED MRI IMAGES S.Karthikeyan 1, Dr. M.Ezhilarasi 2

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1 Research Paper AUTOMATIC STROKE LESION SEGMENTATION FROM DIFFUSION WEIGHTED MRI IMAGES S.Karthikeyan 1, Dr. M.Ezhilarasi 2 Address for Correspondence 1 Assistant Professor, Department of Electronics and Communication Engineering, Info Institute of Engineering, Coimbatore 2 Head of the Department, Department of Electronics & Instrumentation, Kumaraguru College of Technology, Coimbatore, India ABSTRACT: Brain stroke is malfunction occurring in the brain due to disturbance or interruption in the blood supply. Ischemic stroke is one among them, where the condition exists when the blood supply to the brain is stopped. In this paper, an automated brain stroke detection method is proposed. The proposed method consists of four phases. Initial phase is the pre-processing of image by applying mean filter and morphological operations. Second phase combines the information from multiple sources of Diffusion Weighted Magnetic Resonance Imaging (DWI) images with higher b-values. Third phase constructs Quality matrix for combined DWI images to detect and segment lesions. Finally neural network is applied to classify the segmented lesions. KEYWORDS: DWI, Ischemic stroke, Stroke Detection, Brain stroke, lesions, morphological operations. 1 INTRODUCTION During stroke, brain cells die due to lack of oxygen and glucose. A patient who is a victim of stroke is suspected to be under medical emergency. Lack of attention to the victim at the time of stroke leads to death [1] [3] [7]. 35 % of the people who experience stroke become disabled, losing their mobility and the ability to perform the simplest life tasks. Stroke can be classified into two types: ischemic and hemorrhage. About 85 % of all strokes are of ischemic type [4]. It occurs as a result of an occlusion of arteries due to thrombus. Hemorrhagic stroke occurs when a weakened blood vessel breaks or bursts. There is a possibility for co-occurrence of both ischemic and hemorrhagic strokes. Its symptoms are causing permanent damage to the central nervous system, health complications and finally death. The risk factors for stroke include old age, hypertension [2], or mild ischemic attack, diabetes; high cholesterol and cigarette smoking.there are a various diagnostic exams that can be carried out to detect the presence of a stroke. Some visible factors, such as the sudden face weakness, arm drift and abnormal speech, are generally used to predict the presence or absence of stroke to different extent. Unfortunately, these factors are not perfect for diagnosing or detection of stroke. Physicians generally depend on various medical imaging systems for stroke detection and identification. The diagnosis and detection of an ischemic stroke is difficult and it has to be supported by brain imaging techniques. The widely used techniques were Computed tomography (CT) and Magnetic Resonance imaging (MRI) [6]. CT remains the most important of the brain imaging tool because of its wider availability, lower cost, effectiveness and sensitiveness to early stroke. In CT the soft brain tissue, a hemorrhage appears as a bright region and ischemic stroke appears as a dark. Blood is easily visible in CT image as a distinct hyper dense lesion which is easy to detect the hemorrhagic stroke than ischemic stroke. But initial sign of ischemic stroke is a hypo dense lesion which is not available for first few hours when stroke occurs. So the detection of ischemic stroke is fairly a toughest job. But the advances in MRI technology have improved the potential visualization and provision to detect small lesions in brain images [11]. This has resulted in the opportunity to detect cerebral micro bleeds and small hemorrhages in the brain associated with the risk of ischemic stroke and intra cerebral bleeding [9]. Fig.1 clearly shows the occurrence of ischemic stroke in visualization. Fig 1: Demonstration of how Ischemic Stroke occurs a) Blood flow in arteries, b) plaque formation in arteries c) rupture plaque affecting the blood flow d) Clotting of blood, formation of strokes 2. RELATED WORK In this research, a deep investigation is made on the Ischemic stroke which is the major focused area within the contextual scope of medical diagnosis. The classified Carotid arteries run on both the side of the human neck (internal and external carotid arteries), where internal carotid arteries supply oxygen blood to the brain and external carotid supplies oxygen

2 blood to the face, scalp and neck [2]. Carotid artery disease usually occurs inside the carotid arteries due to plaque, a waxy substance which stops the circulation of blood to the brain, leads to strokes [5]. If the blood flow is halted for few minutes, the brain cell starts to die causing impairment of the brain control. This leads to brain damage such as paralysis, vision impairment or death. According to the medical pathology, a stroke can occur other than Carotid artery which in turn called as intracerebral haemorrhage [11]. Harden plaque & Rupture plaque are the two classified plaques which occur in Carotid arteries. Harden plaque reduces the blood flow to the brain and rupture plaque forms blood clotting and stops the blood flow to the brain. Rupture can completely block the blood flow of the brain which leads to stroke. In this paper, a novel methodology is proposed to detect ischemic strokes in human brain [8]. The detailed architecture of the stroke segmentation and characterization is mentioned clearly in the fig. 2 and fig. 3 respectively. 3. METHODOLOGY The proposed methodology stated in fig.4 consists of four main phases, initial pre-processing phase is subjected to pre-processing techniques so that the feature extraction module and segmentation module works fine. Let us assume the film artefacts present in the images are removed manually before preprocessing. Second phase selects the most prominent information image from multiple sources of Diffusion Weighted Magnetic Resonance Imaging (DWI) images with higher b-values. Region growing algorithm [10] is used to extract rich and more information from the images by growing region over stroke lesion. Here, stroke affected region from the highest b values source is mapped and grown region is segmented. Next, image quality matrix is built to the segmented stroke lesion and finally stroke affected region is classified using probabilistic neural network. 3.1 IMAGE PRE-PROCESSING Generally MRI images are high information images with host parameters including tissue density etc. Since some noise matters occur due to water diffusion and blood flow in human brain, this leads the image disturbance. In order to obtain a quality image for post processing, morphological operations are used. Here various morphological operations [1] are applied to the image without disturbing the objects present in the image. Initially mean filter (Fig 2) is applied to remove noise and line fuzz. Then dilation operations is applied to fill the holes and to obtain smoothness in the image by enhancing the contour lines. Next to dilation, erosion operation is performed to detach the image pixel objects associated by a small connexion of pixels. Fig 2. Applying mean filter for noise removal 3.2 MODIFIED REGION GROWING Images with higher b- values are taken into consideration; initial seed and seed points are selected using local seed point selection by local user criterion. The region is grown by growing or connecting the local seed defined by region membership criterion (based on local features such as pixel intensity, color and texture etc). Fig 3. modified region growing algorithm It uses 4x4 connectivity with threshold assumption based on region membership criterion and condition is to exceed the given threshold. Dissolve algorithm is applied based on mean value by merging the split block with adjacent blocks that satisfies the given condition of the particular threshold. Dissolve algorithm is adopted and used in order to segment the most significant regions. Fig.4. states the architecture of the proposed algorithm Fig.5. Results of modified region growing algorithm The grown region is now considered as the seed and examining the adjacent seed regions by adding the blocks within the specified threshold. The mean value is used to determine the possible combination of blocks for merging. 3.3 BUILDING IMAGE QUALITY MATRIX Image quality matrix is fully based on structural similarity index measure (SSIM) which compares local pixel intensity with structural similarity. The entire process is carried to normalize the grown region for accurate segmentation of the stroke lesion. The process is carried out in two phases i) local structure similarity index ii) feature similarity index LOCAL STRUCTURE SIMILARITY INDEX Local structure similarity index calculates relationship with other pixels in a 4x4 neighbourhood. This returns grown region information in an image whose quality index is measured and viewed as a segmented image. Here

3 image quality assessment is carried out from pixel based to structure based; where each structures (region) denotes the segmented stroke lesion (denoted in Fig 10) FEATURE SIMILARITY INDEX Feature similarity index stated in Fig 6 will consider low level features such as phase congruency (PC) and gradient magnitude (GM), where these both features plays a main characteristics in low level feature extraction. Phase Congruency is defined as PC(x)=E(x)/(ε+ n A n (x)), Where, E(x) = F 2 (x)+ H 2 (x), ɛ is a positive constant, A n (x) = e n (x) 2 + o n (x) 2, e n (x),o n (x)=[g(x)*m n e,g(x)*m n o], M n e & M n o - denotes even and odd symmetric filters, e n (x),o n (x) - denotes even and odd quadratic pair of filters. The similarity index is calculated as follows stated in equation 1, firstly two components of PC or GM is taken into consideration and single similarity score is pooled using the formulae given below S PC (x) = (2PC 1 (x).pc 2 (x)+t 1 )/PC 1 2 (x)+pc 2 2 (x)+t 1 for PC and S G (x) = (2G 1 (x).g 2 (x)+t 2 )/G 1 2 (x)+g 2 2 (x)+t 2 for GM Fig.6. states the feature similarity index measure computed using phase congruency 3.4 PROBABILISTIC NEURAL NETWORK Segmented region using the proposed methodology is validated by classifying the stroke affected region. Initial setup is carried to convert matrix data (rows, columns) into vector. Fig7 shows the image matrix is directly converted into 1-D vector using MATLAB. Fig 7. M-by-N matrix input to a 1-D vector The extracted low level features and normal features include contrast, variance, standard deviation, curtosis, mean and smoothness are taken into consideration. The input is given as 1-D vector where the first layer calculates the distance between the input vector and trained vector. The output of the first layer results in the closest element in the training vector. The Second layer computes the sum for each class of input using weight function resulting the probability for output vector. Finally the maximum probability of the corresponding output vector is taken into consideration and produces 0 and 1 classes. If the resulting output class of the given input vector is 1, then the chance for stroke presence is high. 3.5 ALGORITHM Preprocess the given input image (reference_input) Perform uniform blocking Fix the initial seed and seed points Calculate the seed selection parameter Apply automatic thresholding (assumption) Select the pixels as seeds which is higher than the threshold Select the first seed with maximum mean and grow the region by connecting the seeds using membership criterion Calculate the distance between the region mean and pixel intensity Stop growing the seeds when it satisfies the criteria of Intensity = Mean (µ)-threshold(t) Segment the grown region (grown) Calculate Image quality matrix using local structure similarity index (grown,reference_input) Convert the quality matrix into vector Apply probabilistic neural network Classify the vector

4 4 EXPERIMENTAL RESULTS Data acquisition is achieved from one local scanning centre where it facilitates with various kinds of scanners. Data is collected for stroke patients and non-stroke patients with different b values. Apparent diffusion coefficient (ADC) is derived from multiple patients with quantified b values of range 2000 is compared with healthier patients. In order to validate the proposed method, lesion region is marked apparently by experts to aid as proof. Data set is formed for more than 10 volunteers with healthy condition and 5 of stroke affected. Experiment was carried out on data sets with an average intensity value of Apparent diffusion coefficient at mm^2/s^-1 with a radius of 40 (intensity) and 4 (spatial) for healthier patients. The proposed method segments in greater accuracy with an average intensity of ADC at mm^2/s^-1 and algorithm works fine due to improved b values. The screenshot obtained using proposed method is compared with expert marking stated in Fig 8. As per manual investigation, the screenshot of the proposed method stated in Fig 9 and Fig 10 shows that the proposed method works fine in segmenting lesion region. Implementation results manifest the efficiency of the method in detecting large and small lesion. Fig 8. Manually segmented part marked by expert neurologist Fig 9. Region grown over stroke region Fig10. Stroke Lesions segmented by our proposed method 5 CONCLUSION Most of brain stroke detection and segmentation technique needs multi attribute MRI data and iterative multi classification with necessary assumptions to count the tissue classes. These techniques are traditional and require image preprocessing with local and global registration. Our presented method overcomes these limitations without any initial assumptions. The process claimed in the presented model is fully automatic and more robust to implement in real time for fast processing and segmentation of stroke lesions. REFERENCES [1] Gursharn Singh and Anand Mittal, Various Image Enhancement Techniques - A Critical Review, International Journal of Innovation and Scientific Research, vol. 10, no. 2, pp , October [2] Persson, M.; Fhager, A.; Trefna, H.D.; Yinan Yu; McKelvey, T.; Pegenius, G.; Karlsson, J.-E.; Elam, M., "Microwave-Based Stroke Diagnosis Making Global Prehospital Thrombolytic Treatment

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