UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation

A brain tumor is one of a deadly disease that needs high accuracy in its medical surgery. Brain tumor detection can be done through magnetic resonance imaging (MRI). Image segmentation for the MRI brain tumor aims to separate the tumor area (as the region of interest or ROI) with a healthy brain and provide a clear boundary of the tumor. This study classifies the ROI and non-ROI using fully convolutional network with new architecture, namely UNet-VGG16. This model or architecture is a hybrid of U-Net and VGG16 with transfer Learning to simplify the U-Net architecture. This method has a high accuracy of about 96.1% in the learning dataset. The validation is done by calculating the correct classification ratio (CCR) to comparing the segmentation result with the ground truth. The CCR value shows that this UNet-VGG16 could recognize the brain tumor area with a mean of CCR value is about 95.69%

AN OVERVIEW OF IMAGE SEGMENTATION ALGORITHMS

Image segmentation is a puzzled problem even after four decades of research. Research on image segmentation is currently conducted in three levels. Development of image segmentation methods, evaluation of segmentation algorithms and performance and study of these evaluation methods. Hundreds of techniques have been proposed for segmentation of natural images, noisy images, medical images etc. Currently most of the researchers are evaluating the segmentation algorithms using ground truth evaluation of (Berkeley segmentation database) BSD images. In this paper an overview of various segmentation algorithms is discussed. The discussion is mainly based on the soft computing approaches used for segmentation of images without noise and noisy images and the parameters used for evaluating these algorithms. Some of these techniques used are Markov Random Field (MRF) model, Neural Network, Clustering, Particle Swarm optimization, Fuzzy Logic approach and different combinations of these soft techniques

Measuring the Angular Velocity of a Propeller with Video Camera Using Electronic Rolling Shutter

Noncontact measurement for rotational motion has advantages over the traditional method which measures rotational motion by means of installing some devices on the object, such as a rotary encoder. Cameras can be employed as remote monitoring or inspecting sensors to measure the angular velocity of a propeller because of their commonplace availability, simplicity, and potentially low cost. A defect of the measurement with cameras is to process the massive data generated by cameras. In order to reduce the collected data from the camera, a camera using ERS (electronic rolling shutter) is applied to measure angular velocities which are higher than the speed of the camera. The effect of rolling shutter can induce geometric distortion in the image, when the propeller rotates during capturing an image. In order to reveal the relationship between the angular velocity and the image distortion, a rotation model has been established. The proposed method was applied to measure the angular velocities of the two-blade propeller and the multiblade propeller. The experimental results showed that this method could detect the angular velocities which were higher than the camera speed, and the accuracy was acceptable

Glioblastoma: Vascular Habitats Detected at Preoperative Dynamic Susceptibility-weighted Contrast-enhanced Perfusion MR Imaging Predict Survival

[EN] Purpose: To determine if preoperative vascular heterogeneity of glioblastoma is predictive of overall survival of patients undergoing standard-of-care treatment by using an unsupervised multiparametric perfusion-based habitat-discovery algorithm. Materials and Methods: Preoperative magnetic resonance (MR) imaging including dynamic susceptibility-weighted contrast material-enhanced perfusion studies in 50 consecutive patients with glioblastoma were retrieved. Perfusion parameters of glioblastoma were analyzed and used to automatically draw four reproducible habitats that describe the tumor vascular heterogeneity: high-angiogenic and low-angiogenic regions of the enhancing tumor, potentially tumor-infiltrated peripheral edema, and vasogenic edema. Kaplan-Meier and Cox proportional hazard analyses were conducted to assess the prognostic potential of the hemodynamic tissue signature to predict patient survival. Results: Cox regression analysis yielded a significant correlation between patients' survival and maximum relative cerebral blood volume (rCBV(max)) and maximum relative cerebral blood flow (rCBF(max)) in high-angiogenic and low-angiogenic habitats (P < .01, false discovery rate-corrected P < .05). Moreover, rCBF(max) in the potentially tumor-infiltrated peripheral edema habitat was also significantly correlated (P < .05, false discovery rate-corrected P < .05). Kaplan-Meier analysis demonstrated significant differences between the observed survival of populations divided according to the median of the rCBV(max) or rCBF(max) at the high-angiogenic and low-angiogenic habitats (log-rank test P < .05, false discovery rate-corrected P < .05), with an average survival increase of 230 days. Conclusion: Preoperative perfusion heterogeneity contains relevant information about overall survival in patients who undergo standard-of-care treatment. The hemodynamic tissue signature method automatically describes this heterogeneity, providing a set of vascular habitats with high prognostic capabilities.Study supported by H2020 European Institute of Innovation and Technology (POC-2016.SPAIN-07) and Universitat Politecnica de Valencia (PAID-10-14). J.J.A., E.F.G., and J.M.G.G. supported by Secretaria de Estado de Investigacion, Desarrollo e Innovacion (DPI2016-80054-R, TIN2013-43457-R). E.F.G. supported by CaixaImpulse program from Fundacio Bancaria "la Caixa" (LCF/TR/CI16/10010016). E.F.G and A.A.B. supported by the Universitat Politecnica de Valencia Instituto Investigacion Sanitaria de La Fe (C05).Juan -Albarracín, J.; Fuster García, E.; Pérez-Girbés, A.; Aparici-Robles, F.; Alberich Bayarri, A.; Revert Ventura, AJ.; Martí Bonmatí, L.... (2018). Glioblastoma: Vascular Habitats Detected at Preoperative Dynamic Susceptibility-weighted Contrast-enhanced Perfusion MR Imaging Predict Survival. Radiology. 287(3):944-954. https://doi.org/10.1148/radiol.2017170845S944954287

Unsupervised classification of multilook polarimetric SAR data using spatially variant wishart mixture model with double constraints

This paper addresses the unsupervised classification problems for multilook Polarimetric synthetic aperture radar (PolSAR) images by proposing a patch-level spatially variant Wishart mixture model (SVWMM) with double constraints. We construct this model by jointly modeling the pixels in a patch (rather than an individual pixel) so as to effectively capture the local correlation in the PolSAR images. More importantly, a responsibility parameter is introduced to the proposed model, providing not only the possibility to represent the importance of different pixels within a patch but also the additional flexibility for incorporating the spatial information. As such, double constraints are further imposed by simultaneously utilizing the similarities of the neighboring pixels, respectively, defined on two different parameter spaces (i.e., the hyperparameter in the posterior distribution of mixing coefficients and the responsibility parameter). Furthermore, the variational inference algorithm is developed to achieve effective learning of the proposed SVWMM with the closed-form updates, facilitating the automatic determination of the cluster number. Experimental results on several PolSAR data sets from both airborne and spaceborne sensors demonstrate that the proposed method is effective and it enables better performances on unsupervised classification than the conventional methods

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

A gaussian mixture-based approach to synthesizing nonlinear feature functions for automated object detection

Feature design is an important part to identify objects of interest into a known number of categories or classes in object detection. Based on the depth-first search for higher order feature functions, the technique of automated feature synthesis is generally considered to be a process of creating more effective features from raw feature data during the run of the algorithms. This dynamic synthesis of nonlinear feature functions is a challenging problem in object detection. This thesis presents a combinatorial approach of genetic programming and the expectation maximization algorithm (GP-EM) to synthesize nonlinear feature functions automatically in order to solve the given tasks of object detection. The EM algorithm investigates the use of Gaussian mixture which is able to model the behaviour of the training samples during an optimal GP search strategy. Based on the Gaussian probability assumption, the GP-EM method is capable of performing simultaneously dynamic feature synthesis and model-based generalization. The EM part of the approach leads to the application of the maximum likelihood (ML) operation that provides protection against inter-cluster data separation and thus exhibits improved convergence. Additionally, with the GP-EM method, an innovative technique, called the histogram region of interest by thresholds (HROIBT), is introduced for diagnosing protein conformation defects (PCD) from microscopic imagery. The experimental results show that the proposed approach improves the detection accuracy and efficiency of pattern object discovery, as compared to single GP-based feature synthesis methods and also a number of other object detection systems. The GP-EM method projects the hyperspace of the raw data onto lower-dimensional spaces efficiently, resulting in faster computational classification processes

Two and three dimensional segmentation of multimodal imagery

The role of segmentation in the realms of image understanding/analysis, computer vision, pattern recognition, remote sensing and medical imaging in recent years has been significantly augmented due to accelerated scientific advances made in the acquisition of image data. This low-level analysis protocol is critical to numerous applications, with the primary goal of expediting and improving the effectiveness of subsequent high-level operations by providing a condensed and pertinent representation of image information. In this research, we propose a novel unsupervised segmentation framework for facilitating meaningful segregation of 2-D/3-D image data across multiple modalities (color, remote-sensing and biomedical imaging) into non-overlapping partitions using several spatial-spectral attributes. Initially, our framework exploits the information obtained from detecting edges inherent in the data. To this effect, by using a vector gradient detection technique, pixels without edges are grouped and individually labeled to partition some initial portion of the input image content. Pixels that contain higher gradient densities are included by the dynamic generation of segments as the algorithm progresses to generate an initial region map. Subsequently, texture modeling is performed and the obtained gradient, texture and intensity information along with the aforementioned initial partition map are used to perform a multivariate refinement procedure, to fuse groups with similar characteristics yielding the final output segmentation. Experimental results obtained in comparison to published/state-of the-art segmentation techniques for color as well as multi/hyperspectral imagery, demonstrate the advantages of the proposed method. Furthermore, for the purpose of achieving improved computational efficiency we propose an extension of the aforestated methodology in a multi-resolution framework, demonstrated on color images. Finally, this research also encompasses a 3-D extension of the aforementioned algorithm demonstrated on medical (Magnetic Resonance Imaging / Computed Tomography) volumes