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Related Experiment Video

Updated: Jun 18, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

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

Mayank Chawla¹, Saurabh Sharma, Jayanthi Sivaswamy

¹Centre for Visual Information Technology, International Institute of Information Technology, Hyderabad, India.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 8, 2009

Summary

This study introduces an automated method for detecting stroke abnormalities like infarcts and hemorrhage in CT scans. The system achieves high accuracy in identifying these conditions at both patient and slice levels.

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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

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Published on: September 25, 2019

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Area of Science:

Medical Imaging
Radiology
Artificial Intelligence in Medicine

Background:

Computed tomographic (CT) imaging is crucial for stroke diagnosis.
Accurate detection and classification of stroke subtypes (acute infarct, chronic infarct, hemorrhage) are essential for effective treatment.
Current methods may require manual interpretation, leading to potential variability.

Purpose of the Study:

To develop and evaluate an automated method for detecting and classifying stroke-related abnormalities in non-contrast CT images.
To achieve high accuracy and recall at both patient and slice levels.
To provide a robust tool for aiding clinicians in stroke diagnosis.

Main Methods:

The method involves image enhancement using windowing operations.
Abnormality detection utilizes mid-line symmetry and domain knowledge for rotation- and translation-invariant analysis.
A two-level classification scheme employs features from intensity and wavelet domains.

Main Results:

The automated method achieved 90% accuracy and 100% recall at the patient level for abnormality detection.
At the slice level, the system demonstrated an average precision of 91% and a recall of 90%.
Evaluation was performed on a dataset comprising 347 image slices from 15 patients.

Conclusions:

The proposed automated method effectively detects and classifies stroke abnormalities in non-contrast CT images.
The system shows high performance metrics, suggesting its potential as a valuable tool in clinical practice.
Further validation on larger datasets could confirm its utility in routine stroke diagnosis.