The IUP Journal of Telecommunications
Feature Extraction and Classification Techniques for Brain Tumor Images: A Review

Article Details
Pub. Date : Nov, 2019
Product Name : The IUP Journal of Telecommunications
Product Type : Article
Product Code : IJTC41910
Author Name : Sowjanya Velugubantla and Sasibhushana Rao Gottapu
Availability : YES
Subject/Domain : Engineering
Download Format : PDF Format
No. of Pages : 9

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Abstract

In medical fields, detection of brain tumors and their feature extraction is very important. By observing various image modalities, different anatomy structures of human brain can be visualized. It is very difficult to identify the abnormal tissue structures of brain using simple imaging techniques. Image processing technique used to detect brain tumors and classify them contains several steps. The steps include preprocessing, feature extraction and classification. To classify and recognize the images, feature extraction techniques are applied. The paper reviews suitable brain tumor texture feature extraction techniques and classification methods. These methods are very useful to classify the benign and malignant tumors of brain. These techniques are more beneficial to extract the hidden information of an image, and this feature set gives high classification accuracy.


Description

Over the past several years, for the purpose of medical imaging, more and more sophisticated equipment are being used. However, with original radiology screening techniques, visual assessment is strenuous and time-consuming, and every single scan is prone to interpretation error. Therefore, analysis of biomedical digital images is still a challenging task for the researchers. The present-day technological developments in imaging have also brought many changes in the diagnosis, treatment planning and treatment verification procedures. The precision, speed in diagnosis process and non-invasive clinical procedures have also drastically improved (Sinha and Bhagwati, 2014).


Keywords

Texture feature extraction, SST, Geometrical features, Support Vector Machine (SVM)

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