An efficient automated methodology for detecting and segmenting the ischemic stroke in brain MRI images

Sivakumar, P.; Ganeshkumar, P.

doi:10.1002/ima.22231

Cited by 23 publications

(14 citation statements)

References 8 publications

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“…Table shows the comparisons of the proposed method with conventional methods. The conventional method used ANFIS classification approach for detecting the stroke regions in brain MRI images and they obtained 97.1% of sensitivity, 93.6% of specificity and 99.6% of accuracy. Karthik and Menaka used curvelet transform for detecting the stroke regions in brain MRI images.…”

Section: Resultsmentioning

confidence: 99%

Computer aided detection and diagnosis methodology for brain stroke using adaptive neuro fuzzy inference system classifier

Anbumozhi

2019

Int J Imaging Syst Tech

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A stroke or "brain attack" occurs when the blood flow to an area of the brain is interrupted. In this article, ischemic stroke is detected and diagnosed using the following stages: noise reduction, enhancement, skull removal, feature extraction and k-means clustering. The impulse noises in brain magnetic resonance imaging (MRI) image are reduced using directional filtering algorithm.The noise reduced brain image is further enhanced using oriented local histogram equalization technique. The skull is removed from the enhanced brain image. Features are extracted and stroke region is segmented using k-means clustering and adaptive neuro fuzzy inference system (ANFIS) classifier. The main objective of this article is to develop a methodology for the detection of stroke using MRI brain images. K E Y W O R D Sdiagnosis, features, impulse noise, skull, stroke

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Section: Resultsmentioning

confidence: 99%

Computer aided detection and diagnosis methodology for brain stroke using adaptive neuro fuzzy inference system classifier

Anbumozhi

2019

Int J Imaging Syst Tech

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“…The specificity values indicate that the proportion of negatives region that was correctly identified by the algorithm. The highest specificity was accomplished as 0.9930 by [23] followed by 0.9828 [62] and 0.9700 by [69]. The highest accuracy reading in segmenting stroke lesion in brain MRI by the watershed algorithm is due to its effectiveness to combine elements from both discontinuity and similarity-based [70], [71].…”

Section: Comparison Of Acute Ischemic Stroke Segmentation Methods Basmentioning

confidence: 97%

“…The main limitations of the conventional segmentation such as region growing segmentation techniques are inadequate to segment the minor lesions and have less stability [62], [63]. Thus, a normalized graph cut segmentation technique is introduced to segment the stroke affected region to overcome this limitation [62]. In a work by Khadem [64], the min-cut/max-flow algorithm of graph cut image segmentation is applied in segmenting MRI brain image.…”

Section: Hybrid Methodsmentioning

confidence: 99%

“…From all the studies done, the highest accuracy was 99.10% achieved by [71] and [52] followed by 98.76% [62] and 98.43% [31]. The higher the sensitivity value, the higher the percentage of positives region were correctly recognized as lesion tissues.…”

Section: Comparison Of Acute Ischemic Stroke Segmentation Methods Basmentioning

confidence: 99%

“…The higher the sensitivity value, the higher the percentage of positives region were correctly recognized as lesion tissues. The highest sensitivity was recorded as 0.9719 [62] followed by 0.9410 [47] and 0.8935 by [64]. The specificity values indicate that the proportion of negatives region that was correctly identified by the algorithm.…”

Section: Comparison Of Acute Ischemic Stroke Segmentation Methods Basmentioning

confidence: 99%

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A Review of MRI Acute Ischemic Stroke Lesion Segmentation

Isa

Kipli

Mahmood

et al. 2020

IJIE

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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.

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A bio‐inspired AlexNet‐DrpXLm architype for an effective brain stroke lesion detection and classification

Melingi¹,

Mojjada²,

Reddy³

et al. 2022

Concurrency and Computation

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In this article, a bio-inspired AlexNet-DrpXLm architype is proposed for an effective brain stroke lesion detection and classification within a short period. Here, the input CT image datasets are collected from Himalayan Institute of Medical Sciences, then the images are preprocessed to take away the noises and also enhance the quality of the images. After that, the input images are trained and the features are extracted with the help of AlexNet model, and then classified as the brain images of normal and abnormal. The last three layers of the AlexNet model are replaced with the dropout extreme learning machine (DrpXLM) classifier to classify the images in efficient manner. The DrpXLM weight parameters are tuned by improved dolphin swarm optimization algorithm. The proposed method attains higher accuracy 99.54%, high precision 90.43%, high F-score 89.40%, higher specificity 93.56%, higher sensitivity 93.56%, lower computational time 0.02 s, and the proposed method is compared to the existing methods, like random decision forest with gravitational search algorithm, hybrid native Bayes and sample weighted with random forest classification algorithm, and random forest with fractional-order Darwinian particle swarm optimization.

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An efficient automated methodology for detecting and segmenting the ischemic stroke in brain MRI images

Cited by 23 publications

References 8 publications

Computer aided detection and diagnosis methodology for brain stroke using adaptive neuro fuzzy inference system classifier

Computer aided detection and diagnosis methodology for brain stroke using adaptive neuro fuzzy inference system classifier

A Review of MRI Acute Ischemic Stroke Lesion Segmentation

A bio‐inspired AlexNet‐DrpXLm architype for an effective brain stroke lesion detection and classification

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