Automatic Brain Stroke Detection using Histogram Based Classification Methods (original) (raw)

Automated segmentation and classification technique for brain stroke

International Journal of Electrical and Computer Engineering (IJECE), 2019

Difussion-Weighted Imaging (DWI) plays an important role in the diagnosis of brain stroke by providing detailed information regarding the soft tissue contrast in the brain organ. Conventionally, the differential diagnosis of brain stroke lesions is performed manually by professional neuroradiologists during a highly subjective and time-consuming process. This study proposes a segmentation and classification technique to detect brain stroke lesions based on diffusion-weighted imaging (DWI). The type of stroke lesions consists of acute ischemic, sub-acute ischemic, chronic ischemic and acute hemorrhage. For segmentation, fuzzy c-Means (FCM) and active contour is proposed to segment the lesion's region. FCM is implemented with active contour to separate the cerebral spinal fluid (CSF) with the hypointense lesion. Pre-processing is applied to the DWI for image normalization, background removal and image enhancement. The algorithm performance has been evaluated using Jaccard Index, Dice Coefficient (DC) and both false positive rate (FPR) and false negative rate (FNR). The average results for the Jaccard index, DC, FPR and FNR are 0.55, 0.68, 0.23 and 0.23, respectively. First statistical order method is applied to the segmentation result to obtain the features for the classifier input. For classification technique, bagged tree classifier is proposed to classify the type of stroke. The accuracy results for the classification is 90.8%. Based on the results, the proposed technique has potential to segment and classify brain stroke lesion from DWI images.

Automated stroke lesion detection and diagnosis system

2017

This study proposes a technique for automated detection and diagnosis of stroke lesions based on diffusion-weighted imaging (DWI). The technique consists of several stages which are pre-processing, segmentation, feature extraction, and classification. The proposed analytical framework of this study is based on Fuzzy C-Means (FCM) segmentation, statistical parameters for features extraction and rule-based classification. The three-dimensional (3D) view is developed to enable observing directions of the gained 3D structure along the three axes. The segmentation results have been validated by using Jaccard and Dice indices, false positive rate (FPR), and false negative rate (FNR). The results for Jaccard, Dice, FPR and FNR of acute stroke are 0.7, 0.84, 0.049 and 0.205, respectively. The accuracy for acute stroke is 90% and chronic stroke is 70%, while the sensitivity and the specificity is 84.38% and 83.33%, respectively.

Detection of Brain Stroke from CT Scan Image

Journal of Dhaka International University, 2018

Brain is our most important organ. Brain stroke is an abnormal incident that causes catastrophic damages in the brain. It is caused when flow of blood to an area of brain is cut off. When this happens, brain cells are being deprived of oxygen and begin to die. Accurate detection of a stroke in the brain is a complex and challenging task. If the stroke portion can be segmented and displayed separately, the treatment process becomes more efficient. This paper provides an efficient process for proper detection of brain stroke from CT scan images. A CT scan image of brain is taken as input. After performing some basic image processing operations, some morphological operations are executed. Then connected components are calculated. Finally analysis of some region properties is done. The system has been tested with different CT scan images. In most of the cases the system came up with satisfactory results.

Brain stroke computed tomography images analysis using image processing: A review

IAES International Journal of Artificial Intelligence (IJ-AI), 2021

Stroke is the second-leading cause of death globally; therefore, it needs immediate treatment to prevent the brain from damage. Neuroimaging technique for stroke detection such as computed tomography (CT) has been widely used for emergency setting that can provide precise information on an obvious difference between white and gray matter. CT is the comprehensively utilized medical imaging technology for bone, soft tissue, and blood vessels imaging. A fully automatic segmentation became a significant contribution to help neuroradiologists achieve fast and accurate interpretation based on the region of interest (ROI). This review paper aims to identify, critically appraise, and summarize the evidence of the relevant studies needed by researchers. Systematic literature review (SLR) is the most efficient way to obtain reliable and valid conclusions as well as to reduce mistakes. Throughout the entire review process, it has been observed that the segmentation techniques such as fuzzy C-mean, thresholding, region growing, k-means, and watershed segmentation techniques were regularly used by researchers to segment CT scan images. This review is also impactful in identifying the best automated segmentation technique to evaluate brain stroke and is expected to contribute new information in the area of stroke research.

Computer aided detection of ischemic stroke using segmentation and texture features

Measurement, 2013

Computed tomography images are widely used in the diagnosis of ischemic stroke because of its faster acquisition and compatibility with most life suppor t devices. This paper presents a new approach to automated detection of ischemic stroke using segmentation, midline shift and image feature characteristics, which separate the ischemic stroke region from healthy tissues in computed tomography images. The proposed method consists of five stages namely, pre-processing, segmentation, tracing midline of the brain, extraction of texture features and classification. The application of the proposed method for early detection of ischemic stroke is demonstrated to improve efficiency and accuracy of clinical practice. The results are quantitative ly evaluated by a human expert. The average overlap metric, average precision and average recall between the results obtained using the proposed approach and the ground truth are 0.98, 0.99 and 0.98, respectively. A classification with accuracy of 98%, 97%, 96% and 92% has been obtained by SVM, k-NN, ANN and decision tree.

A method for automatic detection and classification of stroke from brain CT images

… in Medicine and …, 2009

Computed tomographic (CT) images are widely used in the diagnosis of stroke. In this paper, we present an automated method to detect and classify an abnormality into acute infarct, chronic infarct and hemorrhage at the slice level of non-contrast CT images. The proposed method consists of three main steps: image enhancement, detection of mid-line symmetry and classification of abnormal slices. A windowing operation is performed on the intensity distribution to enhance the region of interest. Domain knowledge about the anatomical structure of the skull and the brain is used to detect abnormalities in a rotation-and translation-invariant manner. A two-level classification scheme is used to detect abnormalities using features derived in the intensity and the wavelet domain. The proposed method has been evaluated on a dataset of 15 patients (347 image slices). The method gives 90% accuracy and 100% recall in detecting abnormality at patient level; and achieves an average precision of 91% and recall of 90% at the slice level.

Characterization of Brain Stroke Using Image and Signal Processing Techniques

IntechOpen eBooks, 2021

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.

Automatic Brain Tissue Detection of Acute Ischemic Stroke Patients

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023

Currently, the different algorithms for detecting tumor range and shape in brain MR images are being implemented and it is now possible to find out the degree of tumor with regard to the given tumor area. The information was gathered via research of various statistical analysis methods which are all based on those individuals who have been diagnosed with brain tumors, and then risk factors and symptoms that appear for all individuals diagnosed with brain tumors were discovered. The advancement of research in medicine day and night aims to provide modern therapeutic approaches. The surgeon physically examines this image in order to identify and diagnose brain tumors. However, this procedure accurately measures the stage and scale of the tumor and accurately distinguishes the stage of the tumor based on the location of the tumor. This dissertation employs k-means and fuzzy c-means algorithms to segment brain tumors and classify tumor cells using CNN (convolution neural network). This approach enables the accurate and reproducible segmentation of tumor tissue equal to manual segmentation. Additionally, it decreases research time and accurately determines the stage of tumor from a given region of tumor.

Detection and Segmentation of Ischemic Stroke Using Textural Analysis on Brain CT Images

2015

The detection of the brain strokes from Computed Tomography CT images needs convenient processing technique starting from image enhancement to qualify the brain image by isolation process, region growing and logical operators (OR and AND). Morphological techniques (opening and close) with the logical operator produce a good result. These results with the help of the simplest segmentation process, which is the thresholding process, are used to extract a stroke region from the CT image of the brain. The median filter is applied to remove the noise from the image. The statistical features calculated using first-order histogram were utilized in the detection of the stroke region.

Image Processing In Stroke Classification

Cross-sectional imaging approaches expect a key part in assessing depleting mind wounds. Experts regularly choose depleting size and reality in CT and MRI. Separating and recognizing relics is basic in dealing with clinical pictures. Picture and sign taking care of are used to describe tissues inside pictures solidly associated with edges. In CT pictures, a close to home cycle takes a stroke ’s manual structure with less precision. This segment presents the utilization of both picture and sign taking care of strategies in the depiction of Brain Stroke field. This part moreover summarizes how to depict the brain stroke using different picture dealing with computations, for instance, ROI based division and watershed procedures.