A Review of MRI Acute Ischemic Stroke Lesion Segmentation

Immediate treatment of a stroke can minimize long-term effects and even help reduce death risk. In the ischemic stroke cases, there are two zones of injury which are ischemic core and ischemic penumbra zone. The ischemic penumbra indicates the part that is located around the infarct core that is at risk of developing a brain infarction. Recently, various segmentation methods of infarct lesion from the MRI input images were developed and these methods gave a high accuracy in the extraction and detection of the infarct core. However, only some limited works have been reported to isolate the penumbra tissues and infarct core separately. The challenges exist in ischemic core identification are traditional approach prone to error, time-consuming and tedious for medical expert which could delay the treatment. In this paper, we study and analyse the segmentation algorithms for brain MRI ischemic of different categories. The focus of the review is mainly on the segmentation algorithms of infarct core with penumbra and infarct core only. We highlight the advantages and limitations alongside the discussion of the capabilities of these segmentation algorithms and its key challenges. The paper also devised a generic structure for automated stroke lesion segmentation. The performance of these algorithms was investigated by comparing different parameters of the surveyed algorithms. In addition, a new structure of the segmentation process for segmentation of penumbra is proposed by considering the challenges remains. The best accuracy for segmentation of infarct core and penumbra tissues is 82.1% whereas 99.1% for segmentation infarct core only. Meanwhile, the shortest average computational time recorded was 3.42 seconds for segmenting 10 slices of MR images. This paper presents an inclusive analysis of the discussed papers based on different categories of the segmentation algorithm. The proposed structure is important to enable a more robust and accurate assessment in clinical practice. This could be an opportunity for the medical and engineering sector to work together in designing a complete end-to-end automatic framework in detecting stroke lesion and penumbra

Characterization of Brain Stroke Using Image and Signal Processing Techniques

Cross-sectional imaging approaches play a key role in assessing bleeding brain injuries. Doctors commonly determine bleeding size and severity in CT and MRI. Separating and identifying artifacts is extremely important in processing medical images. Image and signal processing are used to classify tissues within images closely linked to edges. In CT images, a subjective process takes a stroke ‘s manual contour with less precision. This chapter presents the application of both image and signal processing techniques in the characterization of Brain Stroke field. This chapter also summarizes how to characterize the brain stroke using different image processing algorithms such as ROI based segmentation and watershed methods

Computational Modeling for Abnormal Brain Tissue Segmentation, Brain Tumor Tracking, and Grading

This dissertation proposes novel texture feature-based computational models for quantitative analysis of abnormal tissues in two neurological disorders: brain tumor and stroke. Brain tumors are the cells with uncontrolled growth in the brain tissues and one of the major causes of death due to cancer. On the other hand, brain strokes occur due to the sudden interruption of the blood supply which damages the normal brain tissues and frequently causes death or persistent disability. Clinical management of these brain tumors and stroke lesions critically depends on robust quantitative analysis using different imaging modalities including Magnetic Resonance (MR) and Digital Pathology (DP) images. Due to uncontrolled growth and infiltration into the surrounding tissues, the tumor regions appear with a significant texture variation in the static MRI volume and also in the longitudinal imaging study. Consequently, this study developed computational models using novel texture features to segment abnormal brain tissues (tumor, and stroke lesions), tracking the change of tumor volume in longitudinal images, and tumor grading in MR images. Manual delineation and analysis of these abnormal tissues in large scale is tedious, error-prone, and often suffers from inter-observer variability. Therefore, efficient computational models for robust segmentation of different abnormal tissues is required to support the diagnosis and analysis processes. In this study, brain tissues are characterized with novel computational modeling of multi-fractal texture features for multi-class brain tumor tissue segmentation (BTS) and extend the method for ischemic stroke lesions in MRI. The robustness of the proposed segmentation methods is evaluated using a huge amount of private and public domain clinical data that offers competitive performance when compared with that of the state-of-the-art methods. Further, I analyze the dynamic texture behavior of tumor volume in longitudinal imaging and develop post-processing frame-work using three-dimensional (3D) texture features. These post-processing methods are shown to reduce the false positives in the BTS results and improve the overall segmentation result in longitudinal imaging. Furthermore, using this improved segmentation results the change of tumor volume has been quantified in three types such as stable, progress, and shrinkage as observed by the volumetric changes of different tumor tissues in longitudinal images. This study also investigates a novel non-invasive glioma grading, for the first time in literature, that uses structural MRI only. Such non-invasive glioma grading may be useful before an invasive biopsy is recommended. This study further developed an automatic glioma grading scheme using the invasive cell nuclei morphology in DP images for cross-validation with the same patients. In summary, the texture-based computational models proposed in this study are expected to facilitate the clinical management of patients with the brain tumors and strokes by automating large scale imaging data analysis, reducing human error, inter-observer variability, and producing repeatable brain tumor quantitation and grading

An anatomical assessment of brain infarcts : a MRI study

An infarct is an area which has lost its blood supply due to obstruction, thrombosis or embolism. It is the third leading cause of death in the Western world, following non-cerebral cardiovascular disease and cancer. This research study focused on determining the infarct prevalence according to age, sex and brain areas most affected by infarcts. The prevalence of different infarct types was also determined. Brain MRI statistics were obtained from a Private Radiology practice in Pretoria for a 13-month period. A total of 1844 brain MRI examinations were evaluated, of which 299 patients presented with infarcts. Their age and sex were noted and their individual reports were obtained to record the anatomical structures and brain lobes that were infarcted. The infarct types were also noted. Diffusion-weighted images were used to measure new infarcts, while FLAIR images were used to measure old infarcts. Results showed an overall incidence of 16.10% and vascular structures accounted for 26.63% of these. Most infarcts were new (56.80%) and mainly affected patients aged 70–79 years (31.36%). Normal cerebral infarcts (72.49%) and embolic infarcts (14.50%) were the most common. The parietal lobe (34.91%) and right middle cerebral artery (11.54%) presented with the most infarcts. The right hemisphere (34.91%) presented with slight infarct predominance, but this was not significant when compared to the left (31.95%) hemisphere (Chi square p>0.05). No significant difference was found concerning the overall male to female ratio (Chi square p>0.05). Females aged 18–39 years of age presented with three times more infarcts than their male counterparts. This may possibly be due to their use of oral contraceptives and pregnancy, which increases the risk of thrombosis and embolism. Females over 80 years also presented with higher infarct prevalence, which is expected, since men die at earlier ages due to other co-morbidities such as cancer.Dissertation (MSc)--University of Pretoria, 2009.AnatomyMScUnrestricte