Isfahan University of Medical Sciences Journal of Isfahan Medical School 1027-7595 32 284 2014 06 22 Automatic Segmentation and Classification of Brain Hemorrhage Regions Using Multi-class Support Vector Machine in Computed Tomography (CT-Scan) Images Automatic Segmentation and Classification of Brain Hemorrhage Regions Using Multi-class Support Vector Machine in Computed Tomography (CT-Scan) Images 631 646 14331 FA Bahareh Shahangian MSc Student, Department of Telecommunications, School of Electrical Engineering, Islamic Azad University, Najafabad Branch, Isfahan, Iran Hossein Pourghassem Assistant Professor, Department of Telecommunications, School of Electrical Engineering, Islamic Azad University, Najafabad Branch, Isfahan, Iran Journal Article 2013 12 18 Background: Brain hemorrhage due to head trauma is one of the most common causes of death. Early diagnosis of location and type of brain hemorrhage is crucial. For saving the patients completely, it is necessary to detect the correct location and type of the hemorrhage in an early stage. In this study, we introduced an automatic brain hemorrhage detection and classification algorithm to improve and accelerate the process of physicians’ decision-making.Methods: To achieve the purpose, at first a segmentation algorithm was usedto detect and separate the hemorrhage regions from other parts of the brain. Then, a number of appropriate features from each detected hemorrhage region were extracted and then by using genetic algorithm, the most convenient features were selected. The utilized computed tomography (CT-scan) images in this research were collected from Kashani hospital CT-scan center (Isfahan, Iran) and were of 70 men and women between the ages of 15-60 years.Findings: Using the proposed segmentation and classification algorithm, the segmentation accuracy for different types of hemorrhages [epidural (EDH), intracerebral (ICH) and subdural (SDH)] were obtained as 96.87%, 96.10%, 92.15%, respectively. Also, intraventricular hemorrhage (IVH) was detected and separated from other types of hemorrhage with the accuracy rate of 91.82% and classified with accuracy rate of 94.13%.Conclusion: In this research, an independent and automatic brain hemorrhage detection and classification algorithm was assessed. Our proposed algorithm is an attempt to improve and accelerate the process of physicians’ decision-making to save the patients’ lives. By using the proposed algorithm, we were able to detect and classify four kinds of dangerous hemorrhages in CT-scan images and accelerat the process of the victim’s treatment. Background: Brain hemorrhage due to head trauma is one of the most common causes of death. Early diagnosis of location and type of brain hemorrhage is crucial. For saving the patients completely, it is necessary to detect the correct location and type of the hemorrhage in an early stage. In this study, we introduced an automatic brain hemorrhage detection and classification algorithm to improve and accelerate the process of physicians’ decision-making.Methods: To achieve the purpose, at first a segmentation algorithm was usedto detect and separate the hemorrhage regions from other parts of the brain. Then, a number of appropriate features from each detected hemorrhage region were extracted and then by using genetic algorithm, the most convenient features were selected. The utilized computed tomography (CT-scan) images in this research were collected from Kashani hospital CT-scan center (Isfahan, Iran) and were of 70 men and women between the ages of 15-60 years.Findings: Using the proposed segmentation and classification algorithm, the segmentation accuracy for different types of hemorrhages [epidural (EDH), intracerebral (ICH) and subdural (SDH)] were obtained as 96.87%, 96.10%, 92.15%, respectively. Also, intraventricular hemorrhage (IVH) was detected and separated from other types of hemorrhage with the accuracy rate of 91.82% and classified with accuracy rate of 94.13%.Conclusion: In this research, an independent and automatic brain hemorrhage detection and classification algorithm was assessed. Our proposed algorithm is an attempt to improve and accelerate the process of physicians’ decision-making to save the patients’ lives. By using the proposed algorithm, we were able to detect and classify four kinds of dangerous hemorrhages in CT-scan images and accelerat the process of the victim’s treatment. https://jims.mui.ac.ir/article_14331_0503bf609757acf2e75aa8cbc0d8323b.pdf