Volume 114 No. 11 2017, 39-46 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu ijpam.eu Automated Segmentation of Brain Parts from MRI Image Slices 1 N. Madhesh and 2 Hema P. Menon 1 Department of Computer Science and Engineering, Amrita School of Engineering, Coimbatore. magicalmajestic@gmail.com 2 Amrita Vishwa Vidyapeetham, Amrita University, India. p_hema@cb.amrita.edu Abstract Segmentation of brain images has been a prominent area of research in the field of medical imaging in the recent past. This work concentrates on segmentation of brain images acquired using Magnetic Resonance Imaging (MRI) technique. Brain image consists of many different parts that are of interest to practitioners. Automatic segmentation of all these parts has been a challenging task. In this paper a system for localizing the region of interest and automated segmentation of brain parts by using histogram of oriented gradients and SVM Classifier has been presented. The system is trained using specific parts of the brain image separately. Once properly trained, the system can segment the part with which it has been trained from the test dataset. Here, for discussion the region of interest around ventricles in axial view and corpus callosum in sagittal view has been considered. Key Words:Magnetic resonance imaging (MRI), histogram of oriented gradients (HOG), SVM classifier, region of Interest (ROI), image segmentation. 39
1. Introduction Automation of systems for analysis of medical images using image processing techniques has gained lot of prominance recently. In case of design of automated systems, region of interest plays an important role. Once the region of interest has been identified diagnosing of the diseases becomes an easier task. For example Alzheimer is affected by abnormalities in the ventricles, dyslexia by corpus callosum to name a few. Among the various modalities available for acquiring medical images, for brain imaging the use of MRI is more common. Segmentation of MRI images involves dividing the image into its homologues parts such as gray matter, white matter, cerebrum, cerbro-spinal fluid, ventricles, corpus callosum and cerebellum to name some of the commonly researched parts. Segmentation has been a challenging task in case of MRI images, because of the similarities between the gray scales among the different parts. Literature reveals that the existing segmentation technique is applicable for extraction of only some of the specified parts. There is no single method available for segmentation of all parts from the MRI brain image. Recently learning based models are gaining prominence in segmentation of medical images. In this regard the most commonly used method for training is the SVM classifier. Survey of the various segmentation methods of MRI Brain images has been discussed by many researchers [1]. Segmentation has also been an important processing task in development of brain atlas [2]. Most of the works on segmentation of brain images is in tumor detection and has been discussed by many researchers in the recent past. One such work has been reported by Ajaj et al [3], which describes image processing techniques for automatic detection of brain tumor in human brain using SVM. A similar work on brain tumor detection and classification using SVM is proposed by Praveen et al [4], in which skull detection and noise removal are done prior to extracting features with GLCM and classifying using SVM. Tumor has been extracted from MRI Brain images using marker based watershed algorithm by Benson et al [5] by combining several features like texture, edge, orientation, and color to extract the brain tumor. Marcel et al [6] discuss a brain tumor segmentation framework based on outlier detection using a three stage approach through Atlas based segmentation. Many researches in brain tumor detection from MRI and CT images has been done using neural networks and genetic algorithms [7][8]. Feature detection plays an important role in segmentation of parts which are similar in nature. Among the many feature extraction methods present in this work histogram of oriented gradients (HOG) and bag of features are used. HOG has been used mainly for face detection, vehicle identification and many such object detection tasks. HOG features has been used by many researchers for detecting the face [9][10][11]. For extracting hog descriptors count of the edge orientation that occurs in a local neighborhood of an image pixel depending on the gradient is calculated. Diqing et al [12] has used boosted hog features and SVM to detects pedestrians and vehicles from traffic scene. Garima et. al., discusses the use of HOG features for hand gesture recognition [13]. Dvais et al 40
[14] discusses the use of Dlib a machine learning toolkit for segmenting images. It supports to implementing a kernel based methods and Bayesian networks for feature ranking, clustering, regression, anomaly detection, and classification. This is found to be a useful tool for segmenting images. 2. Materials and Methods A. Data Set Details For implementation axial and sagittal slices of the MRI brain image were considered. The dataset consisted of 80 axial slices of which 57 were used for training and 23 for testing; 70 sagittal slices from which 20 were used for testing and 50 for training. The commonly available MRI brain images from the internet were used for implementation purpose. Sample of the axial and sagittal images used is shown in figure 1 and 2 respectively. Figure 1: (a) to (h) Sample input images for MRI axial slice Figure 2: (a) to (h) Sample input images for MRI sagittal slice 41
B. Proposed Method For segmenting the brain image where each slice from the orthogonal stack contains many important parts to be detected, a learning based approach is proposed in this paper. The basic concept is to train a system with a particular region of interest (ROI) so that when a new image is given as input, the system can automatically localize and segment out that region from the image. This is done by extracting the features from the ROI and then training a classifier for region identification. In this work Histogram of Oriented Gradients features and an SVM classifier has been used. By training the system each time with a different part all necessary regions can be extracted from the MRI brain images. Dlib is a tool which provides a similar feature for identifying regions and in this work the applicability of the same for segmenting brain images has been assessed. C. Histogram of Oriented Gradients Histogram of oriented gradients is used to find the exact object from the given images. It identifies the shape and appearance of the object by calculating the intensity gradients. HOG descriptor divides the image into two types, larger region (called as block) and smaller region (called as cells). The image is divided into small connected regions, each of which has several pixels. The histogram of gradient direction is calculated for all the pixels and concatenated by the descriptor, and then the intensity values are calculated in larger region for contrast normalization, to improve the accuracy of detected objects. A rectangle is drawn over an image with non-overlapping cells. Here each and every pixel is considered as a cell. Each of these cells have boxes that have been found by dividing the cell size by cell size of the pixels and each cell contains array to store histograms of all orientation inside the cells, after that histograms has been calculated for group of pixels by calling rows and columns of every pixels. Once the cells and their histograms are found the blocks are obtained. While creating edge orientation histogram, assigning a parameter values will controls the amount of edge orientation, it calculate the histogram between two nearest neighbors. An image contains full of gradients because of edges between the objects. The direction of the gradient is determined by highest intensity value presented in the overall image. Gradients can be signed or unsigned; signed gradients show the direction of highest intensity and unsigned gradients show the orientation of the highest intensity. Finally the overlap between the blocks is controlled by cell stride parameter. The features thus obtained are then passed to an SVM classifier for training. 42
3. Results and Discussion For the purpose of discussion the region of interest around ventricles and corpus callosum has been presented in this section. The necessary regions are marked manually from the dataset for training the system. The results obtained for the sample input images shown in figure 1 and 2 are given in figure 3 and 4 respectively. Figures show the bounding box around the identified regions and the segmented ROI. Figure 3: (a)-(d) Extracted HOG features, the bounding box and segmented region of interest around the ventricles from axial MRI slices Figure 4: (a)-(d) Extracted HOG features, boundin box and segmented region of interest around the corpus callosum from sample Sagittal MRI slices 43
The accuracy of such a system depends on the training dataset and the feature selection method. If trained with a larger number of images the accuracy at which the ROI is localized also would increase. 4. Conclusion In this work an efficient method for automatic identification and further segmentation of any Region of Interest (ROI) from brain MRI images has been proposed. This has been done using the HOG features and SVM classifier. The use of Dlib for segmentation of brain MRI images has also been proposed in this paper. The segmentation of ventricles and corpus callosum has been discussed here. In same way any part from the brain MRI image can be segmented. Such a tool would be very useful in cases which require automatic segmentation on all parts present in brain MRI image slices, as in case of Atlas creation. References [1] Reshma hiralal, Hema P. menon, A survey of Brain MRI image Segmentation Methods and the issues involved, Advances in Intelligent Systems and Computing (2016), 245-259. [2] Hema P. Menon, Majo john, Generation of Medical Atlas from Brain MR Images through Segmentation, Intelligent and Advance Systems (ICIAS) (2010). [3] Ajaj Khan, Nikhat Ali Syed, Image Processing Techniques for Automatic Detection of Tumor in Human Brain Using SVM, International Journal of Advanced Research in Computer and Communication Engineering 4 (2015). [4] Praveen G.B., Anita Agrawal, Hybrid approach for brain tumor detection and classification in magnetic resonance images, Communication, Control and Intelligent Systems (CCIS) (2015). [5] Benson C.C., Lajish V.L., Kumar R., Brain tumor extraction from MRI brain images using marker based watershed algorithm, International Conferences on Advances in Computing, Communications and informatics (ICACCI) (2015). [6] Marcel Prastawa, Elizabeth Bullitt, Sean Ho, Guido Gerig, A brain tumor segmentation framework based on outlier detection: Medical Image Analysis (2004), 275-283. [7] Neethu S., Venkatraman D., Stroke Detection in Brain Using CT Images, Artificial Intelligence and Evolutionary Algorithms in Engineering Systems (2014), 379-386. [8] Bhuvana D., Bhagavathi Sivakumar P., Brain tumor detection and classification in MRI images using probabilistic neural networks, Second international conference on emerging research in 44
computing, information, communication and application (2014), 796-801. [9] Cerna L.R., Camara-Chavez G., Menotti D., Face Detection: Histogram of Oriented Gradients and Bag of Feature Method, proceedings of international multi conference of engineers and computer scientists (2011). [10] Deniz O., Bueno G., Salido J., De la Torre F., Face Recognition using Histogram of Oriented Gradients, Pattern Recognition Letters (2011), 1598-1603. [11] Rekha N., Kurian M.Z., Face Detection in Real Time Based on HOG, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) 3(4) (2014). [12] Diquing Sun Junzo Watada, Detecting Pedestrians and Vehicles in Traffic Scene Based on Boosted HOG Features and SVM, 9 th international symposium on intelligent signal processing (2015). [13] Sheenu Garima, joshi Renu Vig, Histogram of Oriented Gradient Investigation for Static Hand Gestures, International Conference on Computing Communication and Automation (2015). [14] Davis E., King, Dlib-ml, A Machine Learning Toolkit, Journal of Machine Learning Research 10 (2009), 1755-1758. 45
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