Texture Feature Abstraction Based on Assessment of HOG and GLDM Features for Diagnosing Brain Abnormalities in MRI Images

Texture Feature Abstraction Based on Assessment of HOG and GLDM Features for Diagnosing Brain Abnormalities in MRI Images

Sudheesh K V

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L.Basavaraj

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Visvesvaraya Technological University

Texture Feature Abstraction Based on Assessment of HOG and GLDM Features for Diagnosing Brain Abnormalities in MRI Images

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Abstract

The brain tumors are increasing rapidly among the younger generation. The survival of the subject can gradually be increased if the tumors are detected at early stages. Magnetic Resonance Imaging (MRI) is an important technique in detecting the tumors. The images are corrupted by random unwanted information, complicating the automatic feature extraction and the analysis of clinical data. Many methods are existing in present day to remove the unwanted information from the images. Automatic classification is essential because it reduces the cause of human error and where the accuracy is not affected. The work emphasizes on removal of noises from the MRI using the hybrid KSL technique which is the combination of Kernel, Sobel and low pass filter. Features are the properties which describe the whole image. Features from these images are extracted using shape, texture and intensity based techniques. The feature extracted are HOG and GLDM.

References

14 Cites in Article

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Sudheesh K V. 2018. \u201cTexture Feature Abstraction Based on Assessment of HOG and GLDM Features for Diagnosing Brain Abnormalities in MRI Images\u201d. Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 18 (GJCST Volume 18 Issue D2).

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GJCST Volume 18 Issue D2

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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Classification

GJCST-D Classification I.4.1

Submission ReceivedDecember 9, 2017
Peer Review Double Blind
Handling Editor
Accepted January 3, 2018
Published January 15, 2018

Version of record

v1.2

Issue date

September 29, 2018

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Article Matrices

Total Score: 102

Country: India

Subject: Global Journal of Computer Science and Technology - D: Neural & AI

Authors: Sudheesh K V, L.Basavaraj, (PhD/Dr. count: 0)

View Count (all-time): 261

Total Views (Real + Logic): 5786

Total Downloads (simulated): 1543

Publish Date: 2018 09, Sat

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Total Views: 5786

Total Downloads: 1543

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