A Hybrid Approach of Using Particle Swarm Optimization and Volumetric Active Contour without Edge for Segmenting Brain Tumors in MRI Scan

Segmentation of brain tumors in magnetic resonance imaging is a one of the most complex processes in medical image analysis because it requires a combination of data knowledge with domain knowledge to achieve highly results. Such that, the data knowledge refers to homogeneity, continuity, and anatomical texture. While the domain knowledge refers to shapes, location, and size of the tumor to be delineated. Due to recent advances in medical imaging technologies which produce a massive number of cross-sectional slices, this makes a manual segmentation process is a very intensive, time-consuming and prone to inconsistences. In this study, an automated method for recognizing and segmenting the pathological area in MRI scans has been developed. First the dataset has been pre-processed and prepared by implementing a set of algorithms to standardize all collected samples. A particle swarm optimization is utilized to find the core of pathological area within each MRI slice. Finally, an active contour without edge method is utilized to extract the pathological area in MRI scan. Results reported on the collected dataset includes 50 MRI scans of pathological patients that was provided by Iraqi Center for Research and Magnetic Resonance of Al Imamain Al-Kadhimain Medical City in Iraq. The achieved accuracy of the proposed method was 92% compared with manual delineation

Automated screening of MRI brain scanning using grey level statistics

This paper describes the development of an algorithm for detecting and classifying MRI brain slices into normal and abnormal by relying on prior-knowledge, that the two hemispheres of a healthy brain have approximately a bilateral symmetry. We use the modified grey level co-occurrence matrix method to analyze and measure asymmetry between the two brain hemispheres. 21 co-occurrence statistics are used to discriminate the images. The experimental results demonstrate the efficacy of our proposed algorithm in detecting brain abnormality with high accuracy and low computational time. The dataset used in the experiment comprises 165 patients with 88 patients having different brain abnormalities whilst the remainder do not exhibit any detectable pathology. The algorithm was tested using a ten-fold cross-validation technique with 100 repetitions to avoid the result depending on the sample order. The maximum accuracy achieved for the brain tumours detection was 97.8% using a Multi-Layer Perceptron Neural Network

Mid-callosal Plane Determination Using Preferred Directions From Diffusion Tensor Images

The corpus callosum is the major brain structure responsible for inter-hemispheric communication between neurons. Many studies seek to relate corpus callosum attributes to patient characteristics, cerebral diseases and psychological disorders. Most of those studies rely on 2D analysis of the corpus callosum in the mid-sagittal plane. However, it is common to find conflicting results among studies, once many ignore methodological issues and define the mid-sagittal plane based on precary or invalid criteria with respect to the corpus callosum. In this work we propose a novel method to determine the mid-callosal plane using the corpus callosum internal preferred diffusion directions obtained from diffusion tensor images. This plane is analogous to the mid-sagittal plane, but intended to serve exclusively as the corpus callosum reference. Our method elucidates the great potential the directional information of the corpus callosum fibers have to indicate its own referential. Results from experiments with five image pairs from distinct subjects, obtained under the same conditions, demonstrate the method effectiveness to find the corpus callosum symmetric axis relative to the axial plane.941

Employing Symmetry Features for Automatic Misalignment Correction in Neuroimages

A novel method to automatically compute the symmetry plane and to correct the 3D orientation of neuro-images is presented. In acquisition of neuroimaging scans, the lack of perfect alignment of a patient's head makes it challenging to evaluate brain images. By deploying a shape-based criterion, the symmetry plane is defined as a plane that best matches external surface points on one side of the head, with their counterparts on the other side. In our method, the head volume is represented as a re-parameterized surface point cloud, where each location is parameterized by its elevation (latitude), azimuth (longitude), and radius. The search for the best matching surfaces is implemented in a multi-resolution paradigm, and the computation time is significantly decreased. The algorithm was quantitatively evaluated using in both simulated data and in real T1, T2, Flair magnetic resonance patient images. This algorithm is found to be fast (< 10s per MR volume), robust and accurate (< .6 degree of Mean Angular Error), invariant to the acquisition noise, slice thickness, bias field, and pathological asymmetries

Mid-sagittal plane detection for advanced physiological measurements in brain scans

Objective: The process of diagnosing many neurodegenerative diseases, such as Parkinson's and progressive supranuclear palsy, involves the study of brain magnetic resonance imaging (MRI) scans in order to identify and locate morphological markers that can highlight the health status of the subject. A fundamental step in the pre-processing and analysis of MRI scans is the identification of the mid-sagittal plane, which corresponds to the mid-brain and allows a coordinate reference system for the whole MRI scan set. Approach: To improve the identification of the mid-sagittal plane we have developed an algorithm in Matlab® based on the k-means clustering function. The results have been compared with the evaluation of four experts who manually identified the mid-sagittal plane and whose performances have been combined with a cognitive decisional algorithm in order to define a gold standard. Main results: The comparison provided a mean percentage error of 1.84%. To further refine the automatic procedure we trained a machine learning system using the results from the proposed algorithm and the gold standard. We tested this machine learning system and obtained results comparable to medical raters with a mean absolute error of 1.86 slices. Significance: The system is promising and could be directly incorporated into broader diagnostic support systems

AUTOMATED MIDLINE SHIFT DETECTION ON BRAIN CT IMAGES FOR COMPUTER-AIDED CLINICAL DECISION SUPPORT

Midline shift (MLS), the amount of displacement of the brain’s midline from its normal symmetric position due to illness or injury, is an important index for clinicians to assess the severity of traumatic brain injury (TBI). In this dissertation, an automated computer-aided midline shift estimation system is proposed. First, a CT slice selection algorithm (SSA) is designed to automatically select a subset of appropriate CT slices from a large number of raw images for MLS detection. Next, ideal midline detection is implemented based on skull bone anatomical features and global rotation assumptions. For the actual midline detection algorithm, a window selection algorithm (WSA) is applied first to confine the region of interest, then the variational level set method is used to segment the image and extract the ventricle contours. With a ventricle identification algorithm (VIA), the position of actual midline is detected based on the identified right and left lateral ventricle contours. Finally, the brain midline shift is calculated using the positions of detected ideal midline and actual midline. One of the important applications of midline shift in clinical medical decision making is to estimate the intracranial pressure (ICP). ICP monitoring is a standard procedure in the care of severe traumatic brain injury (TBI) patients. An automated ICP level prediction model based on machine learning method is proposed in this work. Multiple features, including midline shift, intracranial air cavities, ventricle size, texture patterns, and blood amount, are used in the ICP level prediction. Finally, the results are evaluated to assess the effectiveness of the proposed method in ICP level prediction

Automatic linear measurements of the fetal brain on MRI with deep neural networks

Timely, accurate and reliable assessment of fetal brain development is essential to reduce short and long-term risks to fetus and mother. Fetal MRI is increasingly used for fetal brain assessment. Three key biometric linear measurements important for fetal brain evaluation are Cerebral Biparietal Diameter (CBD), Bone Biparietal Diameter (BBD), and Trans-Cerebellum Diameter (TCD), obtained manually by expert radiologists on reference slices, which is time consuming and prone to human error. The aim of this study was to develop a fully automatic method computing the CBD, BBD and TCD measurements from fetal brain MRI. The input is fetal brain MRI volumes which may include the fetal body and the mother's abdomen. The outputs are the measurement values and reference slices on which the measurements were computed. The method, which follows the manual measurements principle, consists of five stages: 1) computation of a Region Of Interest that includes the fetal brain with an anisotropic 3D U-Net classifier; 2) reference slice selection with a Convolutional Neural Network; 3) slice-wise fetal brain structures segmentation with a multiclass U-Net classifier; 4) computation of the fetal brain midsagittal line and fetal brain orientation, and; 5) computation of the measurements. Experimental results on 214 volumes for CBD, BBD and TCD measurements yielded a mean $L_1$ difference of 1.55mm, 1.45mm and 1.23mm respectively, and a Bland-Altman 95% confidence interval ($CI_{95}$) of 3.92mm, 3.98mm and 2.25mm respectively. These results are similar to the manual inter-observer variability. The proposed automatic method for computing biometric linear measurements of the fetal brain from MR imaging achieves human level performance. It has the potential of being a useful method for the assessment of fetal brain biometry in normal and pathological cases, and of improving routine clinical practice.Comment: 15 pages, 8 figures, presented in CARS 2020, submitted to IJCAR