Wyniki wyszukiwania - BazTech

1

Automated segmentation and classification of brain stroke using expectation-maximization and random forest classifier

Subudhi Asit, Dash Manasa, Sabut Sukanta

Biocybernetics and Biomedical Engineering

|

2020

|

Vol. 40, no. 1

277--289

EN

Magnetic resonance imaging (MRI) is effectively used for accurate diagnosis of acute ischemic stroke. This paper presents an automated method based on computer aided decision system to detect the ischemic stroke using diffusion-weighted image (DWI) sequence of MR images. The system consists of segmentation and classification of brain stroke into three types according to The Oxfordshire Community Stroke Project (OCSP) scheme. The stroke is mainly classified into partial anterior circulation syndrome (PACS), lacunar syndrome (LACS) and total anterior circulation stroke (TACS). The affected part of the brain due to stroke was segmented using expectation-maximization (EM) algorithm and the segmented region was then processed further with fractional-order Darwinian particle swarm optimization (FODPSO) technique in order to improve the detection accuracy. A total of 192 scan of MRI were considered for the evaluation. Different morphological and statistical features were extracted from the segmented lesions to form a feature set which was then classified with support vector machine (SVM) and random forest (RF) classifiers. The proposed system efficiently detected the stroke lesions with an accuracy of 93.4% using RF classifier, which was better than the results of the SVM classifier. Hence the proposed method can be used in decision-making process in the treatment of ischemic stroke.

2

Combination of hand-crafted and unsupervised learned features for ischemic stroke lesion detection from Magnetic Resonance Images

Bharathi Praveen Gurunath, Agrawal Anita, Sundaram Ponraj, Sardesai Sanjay

Biocybernetics and Biomedical Engineering

|

2019

|

Vol. 39, no. 2

410--425

EN

Detection of ischemic stroke lesions plays a vital role in the assessment of stroke treatments such as thrombolytic therapy and embolectomy. Manual detection and quantification of stroke lesions is a time-consuming and cumbersome process. In this paper, we present a novel automatic method to detect acute ischemic stroke lesions from Magnetic Resonance Image (MRI) volumes using textural and unsupervised learned features. The proposed method proficiently exploits the 3D contextual evidence using a patch-based approach, which extracts patches randomly from the input MR volumes. Textural feature extraction (TFE) using Gray Level Co-occurrence Matrix (GLCM) and unsupervised feature learning (UFL) based on k-means clustering approaches are employed independently to extract features from the input patches. These features obtained from the two feature extractors are then given as input to the Random Forest (RF) classifier to discriminate between normal and lesion classes. A hybrid approach based on the combination of TFE using GLCM and UFL based on the k-means clustering is proposed in this work. Hybrid combination approach results in more discriminative feature set compared with the traditional approaches. The proposed method has been evaluated on the Ischemic Stroke Lesion Segmentation (ISLES) 2015 training dataset. The proposed method achieved an overall dice coefficient (DC) of 0.886, precision of 0.979, recall of 0.831 and accuracy of 0.8201. Quantitative measures show that the proposed approach is 28.4%, 27.14%, and 5.19% higher than the existing methods in terms of DC, precision, and recall, respectively.

3

Application of MODWT and log-normal distribution model for automatic epilepsy identification

Li M., Chen W., Zhang T.

Biocybernetics and Biomedical Engineering

|

2017

|

Vol. 37, no. 4

679--689

EN

In this paper, a novel approach based on the maximal overlap discrete wavelet transform (MODWT) and log-normal distribution (LND) model was proposed for identifying epileptic seizures from electroencephalogram (EEG) signals. To carry out this study, we explored the potentials of MODWT to decompose the signals into time-frequency sub-bands till sixth level. And demodulation analysis (DA) was investigated to reveal the subtle envelope information from the sub-bands. All obtained coefficients were then used to calculate LDN features, scale parameter (s) and shape parameter (m), which were fed to a random forest classifier (RFC) for classification. Besides, some experiments have been conducted to evaluate the performance of proposed model in the consideration of various wavelet functions as well as feature extractors. The implementation results demonstrated that our proposed technique has yielded remarkable classification performance for all the concerned problems and outperformed the reported methods in terms of the universality. The major finding of this research is that the proposed technique was capable of classifying EEG segments with satisfied accuracy and clinically acceptable computational time. These advantages have make our technique an attractive diagnostic and monitoring tool, which helps doctors in providing better and timely care for the patients.