Earticle

초록

영어

The analysis of MRI images is a manual process carried by experts which need to be automated to accurately classify the normal and abnormal images. We have proposed a reduced, three staged model having pre-processing, feature extraction and classification steps. In preprocessing the noise has been removed from grayscale images using a median filter, and then grayscale images have been converted to color (RGB) images. In feature extraction, red, green and blue channels from each channel of the RGB has been extracted because they are so much informative and easier to process. The first three color moments mean, variance, and skewness are calculated for each red, green and blue channel of images. The features extracted in the feature extraction stage are classified into normal and abnormal with K-Nearest Neighbors (k-NN). This method is applied to 100 images (70 normal, 30 abnormal). The proposed method gives 98.00% training and 95.00% test accuracy with datasets of normal images and 100% training and 90.00% test accuracy with abnormal images. The average computation time for each image was .06s.

목차

Abstract
 1. Introduction
 2. Objective
 3. Related Work
 4. Proposed Model
  4.1. Preprocessing
  4.2. Feature Extraction
  4.3. Classification
 5. Implementation and Experimental Results
  5.1. Database
  5.2. Experimentation Environment
  5.3. Ratio of Training and Test Images
  5.4. Performance Evaluation
  5.5. Time Complexity
 6. Discussion
 7. Conclusion and Future Work
 References

키워드

저자

• Muhammad Fayaz [ University of Malakand, KPK, Pakistan ]
• Abdul Salam Shah [ SZABIST, Islamabad, Pakistan ] Corresponding Author
• Fazli Wahid [ SZABIST, Islamabad, Pakistan ]
• Asadullah Shah [ International Islamic University Malaysia (IIUM), Kuala Lumpur, Malaysia ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605