Analysis and Improvement of CT-Scan/MRI Medical Image Quality in Stroke Patients with Hybrid Thresholding Method: A Case Study in Padang BMC Hospital

- Sumijan (1), Pradani Ayu Widya Purnama (2)
(1) Faculty of Computer Science, Universitas Putra Indonesia YPTK Padang, Padang, 25000, Indonesia
(2) Faculty of Computer Science, Universitas Putra Indonesia YPTK Padang, Padang, 25000, Indonesia
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Sumijan, -, and Pradani Ayu Widya Purnama. “Analysis and Improvement of CT-Scan MRI Medical Image Quality in Stroke Patients With Hybrid Thresholding Method: A Case Study in Padang BMC Hospital”. International Journal on Advanced Science, Engineering and Information Technology, vol. 13, no. 3, May 2023, pp. 876-87, doi:10.18517/ijaseit.13.3.17230.
Stroke is one of the main problems in the medical field, one of the top 3 causes of death in the world. 1) Coronary heart as much as 13%, 2) Cancer as much as 12%, 3) Stroke as much as 7.9%. Stroke often occurs in developing countries, such as Indonesia. One effort to minimize the risk of stroke is to take preventive measures in stroke patients, both before and after a stroke. The way to prevent stroke is by primary and secondary measures. Prevention of stroke symptoms can be avoided with fast and appropriate treatment in stroke care according to medical service standards. This study applies texture analysis and improves the quality of CT-Scan or Magnetic Resonance Imaging (MRI) brain images using the Hybrid Thresholding (HT) method, which is a combination of edge detection methods and P-Files. Differences in the texture of images of brain hemorrhages that indicate stroke and clarify the quality of images of brain hemorrhages that indicate strokes with the parameters of contrast, correlation, energy, and homogeneity. The results of segmentation and feature extraction and texture are then classified using the Backprogation Neural Network (BNN) method with variations in learning rate values. This study produces the best test at a learning rate of 0.1 with an error percentage of 2%. The results of the classification calculation of the area of brain hemorrhage and analysis of image quality in classifying stroke brain with an accuracy level of 27/3 x 100% = 90%.

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