Dalam kajian tentang gambar otak magnetic resonance (MR), terdapat banyak penyelidik menggunakan pelbagai teknik segmentasi dari yang sederhana sampai ke teknik segmentasi yang rumit untuk mengekstrak kawasan yang ingin dikaji. Teknik yang kompleks seperti penggolong, Markov Random Field (MRF) dan model boleh ubah bentuk biasanya digunakan dalam segmentasi. Pengiraan isipadu tisu-tisu akan membantu mengenali penyakit. Baru-baru ini, support vector machine (SVM) telah banyak digunakan dalam aplikasi pengecaman pola. Ia berfungsi sebagai alat pengitlakan dan penggolong yang baik. SVM adalah salah satu teknik pembelajaran berselia dalam rangkaian neural. Sehingga kini, tidak banyak kajian menggunakan SVM dalam bidang pengolahan imej terutamanya imej otak MR. Projek ini mengkajikan kaedah segmentasi SVM untuk mengasingkan White Matter (WM), Gray Matter (GM) dan Cerebrospinal Fluid (CSF). Pertama sekali, algoritma yang cepat dan kuat telah disediakan untuk mengupas tengkorak keluar dari gambar otak MR. Ini merupakan langkah pra-pemprosesan yang penting. Algoritma itu melibatkan kombinasi teknik morphology dan teknik segmentasi. Kemudian, SVM akan digunakan untuk mengkelaskan gambar otak MR kepada tiga jenis rangkaian: WM, GM dan CSF. Projek ini menggunakan satu set gambar otak MR yang terdiri daripada 3 pesakit yang berbeza. Daripada keputusan projek ini, SVM telah terbukti sebagai alat segmentasi yang baik dalam segmentasi gambar otak MRI.
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In the research on magnetic resonance (MR) brain image, there are a lot of researchers using various segmentation techniques ranging from simple image segmentation technique to complex image segmentation technique in order to extract the tissues of research interest. The complex technique such as clustering, Markov Random Field (MRF) and deformable model are usually being used. The calculation of tissues volume will help recognize diseases. Recently, the support vector machine (SVM) has been widely used in pattern recognition application. It serves as a good generalization and classifier tool. SVM is one of the supervised learning techniques of neural network. There has not been much research using the SVM in image processing field especially when it comes to the MR brain images. This project presents SVM image segmentation method on magnetic resonance image of brain to segment the White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF). Firstly, a fast and robust algorithm also has been developed to strip the skull out of the MR brain image. This is an important pre-processing step. The algorithm involves combination of morphology and image segmentation techniques. Next, the SVM will be used to classify the skull stripped MR image into three types of tissues: WM, GM and CSF. This project takes a set of MR brain images of 3 different patients. From the outcome of this project, the SVM has been proven to be a good segmentation tool in MRI brain image segmentation.
magnetic resonance (MR) brain image; various segmentation techniques ranging from simple image segmentation technique to complex image segmentation technique in order to extract the tissues of research interest; SVM image segmentation method on magnetic resonance image of brain to segment the White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF).
Mri brain image segmentation_Chiah Hock Chuan_E3_2010_875003659_00003084353_NI