Hybrid Segmentation and Features Extraction of Ischemic stroke on CT brain
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International Journal of Engineering Trends and Technology (IJETT) | 
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| © 2016 by IJETT Journal | ||
| Volume-42 Number-3 |
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| Year of Publication : 2016 | ||
| Authors : Ms. Pavithra S, Ms. Sivasankari S, Mr. Sunderlin Shibu. D |
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| DOI : 10.14445/22315381/IJETT-V42P221 |
Citation
Ms. Pavithra S, Ms. Sivasankari S, Mr. Sunderlin Shibu. D " Hybrid Segmentation and Features Extraction of Ischemic stroke on CT brain", International Journal of Engineering Trends and Technology (IJETT), V42(3),96-101 December 2016. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Abstract
Ischemic stroke is the blocking of blood clot in the artery which supply blood to the brain. Computed Tomography (CT) images are widely used to diagnose brain stroke. Segmentation is the process of segmenting ROI in an image. Feature extraction is a method of transforming large data into small amount of data. The traditional segmentation technique shows disadvantages like over segmentation in images. The main objective of this project is to eliminate over segmentation in brain stroke images by using Marker based water shed segmentation and the results are compared with k-means clustering technique and then performing features extraction. Initially the images are undergoing segmentation techniques and features extraction process. The primary segmentations are performed initially by using K-means clustering technique and then secondary segmentation are done by using Marker based water shed technique with the help of internal marker and external marker. The shape and statistical features were obtained from gray level co occurrence matrix method and histogram technique. By using features extraction technique can differentiate normality and abnormality in the brain. The abnormality of the brain is measured by calculating features such as statistical and shape by dividing the brain into two equal parts. Mathematical calculations and comparison of results with standard dataset values are performed in order to check whether the brain belongs to normal or abnormal condition.
References
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Keywords
Brain, Ischemic stroke, Segmentation, K-means clustering, Marker based water shed Transformation, Features extraction.