MRI image segmentation using machine learning networks and level set approaches

The segmented brain tissues from magnetic resonance images (MRI) always pose substantive challenges to the clinical researcher community, especially while making precise estimation of such tissues. In the recent years, advancements in deep learning techniques, more specifically in fully convolution neural networks (FCN) have yielded path breaking results in segmenting brain tumour tissues with pin-point accuracy and precision, much to the relief of clinical physicians and researchers alike. A new hybrid deep learning architecture combining SegNet and U-Net techniques to segment brain tissue is proposed here. Here, a skip connection of the concerned U-Net network was suitably explored. The results indicated optimal multi-scale information generated from the SegNet, which was further exploited to obtain precise tissue boundaries from the brain images. Further, in order to ensure that the segmentation method performed better in conjunction with precisely delineated contours, the output is incorporated as the level set layer in the deep learning network. The proposed method primarily focused on analysing brain tumor segmentation (BraTS) 2017 and BraTS 2018, dedicated datasets dealing with MRI brain tumour. The results clearly indicate better performance in segmenting brain tumours than existing ones

Segmentasi Citra MRI Tumor Otak Menggunakan Gaussian Mixture Model dan Hybrid Gaussian Mixture Model - Spatially Variant Finite Mixture Model dengan Algoritma Expectation-Maximization

Tumor otak merupakan salah satu bagian dari tumor pada sistem saraf. Berbagai penelitian telah dilakukan untuk membantu tenaga medis dalam menangani tumor otak, salah satunya dengan melakukan pendeteksian tumor otak melalui segmentasi citra medis berdasarkan MRI. Pada kasus citra MRI, segmentasi dilakukan untuk memisahkan Region of Interest (ROI) atau segmen yang dianggap penting dalam sudut pandang medis, dengan segmen-segmen lainnya (Non-ROI) termasuk noise. Metode segmentasi citra yang umum digunakan adalah model based clustering dengan Gaussian Mixture Model (GMM). Namun, kelemahan GMM adalah antar pixel pada citra dianggap independen sehingga hasil segmentasi tidak memiliki ketahanan terhadap noise dalam segmentasi citra. Untuk mengurangi efek negatif dari noise, dalam penelitian ini akan digunakan model Markov Random Field (MRF) yang secara penuh mempertimbangkan dependensi spasial antara pixel dan proporsi probabilitas label secara eksplisit akan dimodelkan sebagai vektor probabilitas. Sehingga metode yang digunakan adalah Gaussian Mixture Model (GMM) dan GMM yang dibatasi secara spasial oleh Markov Random Fields, atau yang diberi nama Spatially Variant Finite Mixture Model (SVFMM), dimana inisial parameter didapatkan dari GMM, sehingga model yang diajukan adalah hybrid GMM-SVFMM. Dalam proses inferensi, metode estimasi maximum likelihood digunakan untuk mengestimasi parameter model yang diusulkan menggunakan algoritma Expectation-Maximization (EM). Hasil penelitian menunjukkan bahwa segmentasi citra MRI tumor otak dengan hybrid GMM-SVFMM mampu memberikan hasil yang lebih akurat untuk memisahkan ROI dengan noise, dibandingkan jika menggunakan metode GMM. ================================================================================================== A brain tumor is one part of the tumor in the nervous system. Various studies have been conducted to assist medical personnel in dealing with brain tumors, one of them is by performing brain tumor detection through image-based medical segmentation of MRI. In the case of MRI, segmentation is performed to separate the Region of Interest (ROI) or segments that are considered important in the medical point of view, with other segments (Non-ROI) including noise. The commonly used image segmentation method is the model-based clustering with Gaussian Mixture Model (GMM). However, the weakness of GMM is that between the pixels in the image are considered independent, so that the segmentation results do not have the noise robustness in image segmentation. To minimize the negative effects of the noise, in this research we will use the Markov Random Field (MRF) model which fully takes into account the spatial dependencies between pixels. The proportion of label of pixels probabilities will be explicitly modeled as probability vectors. At the same time, pixel component functions are also relatively related to neighboring pixels. This scenario could be implemented as the GMM that is spatially limited by MRF, called the Spatially Variant Finite Mixture Model (SVFMM), in which the initial parameter generated from the GMM, so the proposed model is hybrid GMM-SVFMM.. In the inference process, the maximum likelihood estimation method is used to estimate the proposed model parameters using the Expectation-Maximization (EM)algorithm. The results from the correct classification ratio (CCR) showed that MRI-based brain image segmentation couple with hybrid GMM-SVFMM was able to provide more accurate results to separate the ROI with noise compared to GMM

Segmentasi Citra MRI Tumor Otak Menggunakan Modified Stable Student-t Burr Mixture Model Dengan Pendekatan Bayesian

Salah satu pendekatan komputasi untuk mendapatkan gambaran lokasi tumor otak pada citra MRI tumor otak yaitu segmentasi citra digital. Teknik segmentasi citra yang sering digunakan yaitu clustering, dimana pixel dalam citra akan dikelompokkan berdasarkan intensitas warna (derajat keabuan/grayscale) yang sama. Model based clustering merupakan metode pengelompokkan yang mengoptimalkan kemiripan antara objek berdasarkan pada distribusi probabilistik data. Model mixture yang paling sering digunakan dalam model based clustering khusunya pada segmentasi citra adalah Gaussian mixture model (GMM). Namun, histogram pada citra MRI tumor otak cenderung menunjukkan pola yang miring dan tidak simetri. Sehingga penggunaan GMM memiliki kelemahan yaitu kurang fleksibel terhadap bentuk data, karena distribusi normal memiliki bentuk simetris dan berekor pendek. Oleh karena itu diperlukan pendekatan distribusi yang mampu mengatasi penyimpangan dari distribusi normal. Modified Stable Student-t Burr Distribution atau distribusi MSTBurr dikembangkan dengan tujuan membuat distribusi yang adaptif terhadap perubahan data inputnya. Solusi analitis untuk estimasi parameter distribusi MSTBurr bukan pekerjaan yang mudah karena fungsi likelihood dari distribusi stable tidak bisa direpresentasikan sebagai bentuk analisis yang sederhana. Sehingga, cara untuk mendapatkan estimasi parameter dari distribusi MSTBurr adalah menggunakan pendekatan Bayesian dengan Marcov Chain Monte Carlo (MCMC). Hasil analisis menunjukkan bahwa MSTBurr Mixture Model lebih mampu menangkap pola citra MRI tumor otak. ================================================================================================================== One of the computational approach to get an overview of the location of brain tumors in brain tumor MRI images are digital image segmentation. Image segmentation technique frequently used is clustering, where the pixels in the image will be grouped based on the same color intensity (grayscale). Model-based clustering is a grouping method that optimizes the similarity between objects based on the probabilistic distribution of data. The most common mixture model used in model-based clustering, especially in image segmentation is a Gaussian Mixture Model (GMM). However, the histogram of the brain tumor MRI image tends to show a skewed and asymmetric. So the use of GMM has a weakness that is less flexible to the form of data because the normal distribution has a symmetrical shape and short-tailed. Therefore, it needs a distribution approach that able to overcome the deviation from the normal distribution. Modified Stable Student-t Burr Distribution or MSTBurr distribution has been developed with the aim of creating an adaptive distribution of changes to its input data. Analytical solution for estimating MSTBurr distribution parameters is not an easy job because the likelihood function of this distribution cannot be represented as a simple form of analysis. Thus, the way to obtain parameter estimates from the MSTBurr distribution is using the Bayesian approach couple with Markov Chain Monte Carlo (MCMC). The results of the analysis showed that the MSTBurr Mixture Model could have a better ability to capture the pattern image of the MRI brain tumor