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Proceedings ArticleDOI

Ischemic Stroke detection using Image processing and ANN

08 May 2014-pp 1416-1420
TL;DR: An automated algorithm to detect the stroke using Image processing techniques is given and the advantage is that the strokes can be detected in its early stage.
Abstract: Ischemic stroke is a condition in which brain stops working due to lack of blood supply resulting in death of brain cells. Magnetic Resonance Imaging is widely used to detect Ischemic Strokes in brain. This paper gives an automated algorithm to detect the stroke using Image processing techniques. The algorithm consists of six phases. Data in the form of MRI images is collected in first phase. The preprocessing is performed including filtering on the raw data collected. Midline is traced in third phase for acquiring a symmetrical image. It is followed by bifurcation of image in fourth phase. Finally the image quality matrix is formed for texture analysis in fifth phase and neural network is applied in sixth phase for classification of normal and infected brain. The advantage is that the strokes can be detected in its early stage. The algorithm proposed is simple, efficient and less time consuming. The efficiency is found to be 98%.
Citations
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Journal ArticleDOI
TL;DR: An unsupervised featured learning approach based on stacked sparse autoencoder (SSAE) framework for automatically learning the features for accurate segmentation of stroke lesions from brain MR images significantly outperforms the state-of-the-art methods in terms of precision, DC, and recall.

52 citations

Journal ArticleDOI
R. Karthik1, R. Menaka1
TL;DR: The aim of this work is to review the current state-of-the-art techniques employed for segmentation, classification and detection of stroke lesion and present the key challenges in it.
Abstract: The fast advancements in the field of computer vision, progress in radiology, image processing, modelling and simulation have changed the medical science to diagnose people in an efficient way. To be exact, the headways in medical imaging have prompted better diagnostic planning and accuracy in surgical methodology with little human–machine intervention. Stroke remains the third driving reason for death, after heart attack and cancer. Automatic computer-aided diagnosis of brain diseases has been gaining significant attention in the last two decades. The aim of this work is to review the current state-of-the-art techniques employed for segmentation, classification and detection of stroke lesion and present the key challenges in it. By investigating the advanced aspects and significant pitfalls of the different surveyed techniques, an overview on the performance of these methods is presented in this work.

20 citations


Cites background or methods from "Ischemic Stroke detection using Ima..."

  • ...[60] GLCM features MRI Standalone and Realized in MATLAB 5 Oliveira et al....

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  • ...[60] proposed a similar kind of symmetry-based approach for MRI images....

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Journal ArticleDOI
TL;DR: A hybrid approach based on the combination of TFE using GLCM and UFLbased on the k-means clustering is proposed in this work, which results in more discriminative feature set compared with the traditional approaches.

9 citations

Journal ArticleDOI
TL;DR: The soft computing method known as fuzzy cognitive mapping was proposed for diagnosis of the risk of ischemic stroke and showed a superior performance with a total accuracy of (93.6 ± 4.5)%.
Abstract: Stroke is the third major cause of mortality in the world. The diagnosis of stroke is a very complex issue considering controllable and uncontrollable factors. These factors include age, sex, blood pressure, diabetes, obesity, heart disease, smoking, and so on, having a considerable influence on the diagnosis of stroke. Hence, designing an intelligent system leading to immediate and effective treatment is essential. In this study, the soft computing method known as fuzzy cognitive mapping was proposed for diagnosis of the risk of ischemic stroke. Non-linear Hebbian learning method was used for fuzzy cognitive maps training. In the proposed method, the risk rate for each person was determined based on the opinions of the neurologists. The accuracy of the proposed model was tested using 10-fold cross-validation, for 110 real cases, and the results were compared with those of support vector machine and K-nearest neighbours. The proposed system showed a superior performance with a total accuracy of (93.6 ± 4.5)%. The data used in this study is available by emailing the first author for academic and non-commercial purposes.

7 citations


Additional excerpts

  • ...Gupta and Mishra [19] proposed diagnosis of ischemic stroke using MRI images and artificial neural network (ANN)....

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  • ...Sci., 2013, 9, (9), pp. 1099–1105 [19] Gupta, S., Mishra, A.R.M.: ‘Ischemic stroke detection using image processing and ANN’....

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References
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Journal ArticleDOI
TL;DR: Validation against data collected from 270 stroke patients suggests that the first set of variables yielded predictions that match or exceed the predictive power reported in any comparable work in the available literature.

198 citations

Journal ArticleDOI
TL;DR: This critical appraisal explores different semi-automatic or fully automatic 2D/3D medical image analysis methods and mathematical models applied to human, animal and synthetic ischemic stroke to tackle one of the following three problems: segmentation of infarcted and/or salvageable tissue, prediction of final isChemic tissue fate (death or recovery) and dynamic simulation of the lesion core and/ or penumbra evolution.

131 citations

Journal ArticleDOI
TL;DR: This study demonstrated that CAD is valuable for detection of small AIH on brain CT using a knowledge-based classification system incorporating rules that make use of quantified imaging features and anatomical information.

125 citations

Proceedings ArticleDOI
13 Nov 2009
TL;DR: 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.
Abstract: 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.

113 citations


"Ischemic Stroke detection using Ima..." refers methods in this paper

  • ...[5] Mayank Chawla, Saurabh Sharma, Jayanthi Sivaswamy, Kishore L....

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  • ...Mayank Chawla [5] has been able to detect stroke automatically by the algorithm which involves a two-level classification scheme to detect abnormalities using features derived in the intensity and the wavelet domain....

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Journal ArticleDOI
TL;DR: The application of the proposed method for early detection of ischemic stroke is demonstrated to improve efficiency and accuracy of clinical practice and the results are quantitatively evaluated by a human expert.

88 citations


"Ischemic Stroke detection using Ima..." refers methods in this paper

  • ...[4] N. Hema Rajini, R. Bhavani, “Computer aided detection of ischemic stroke using segmentation and texture features”, Measurement 46, Science Direct, Page 1865–1874, 2013....

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  • ...Hema Rajini [4] used K-means clustering technique together with classifiers like SVM and ANN....

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