Research Article Open Access

BRAIN TUMOR CLASSIFICATION BASED ON CLUSTERED DISCRETE COSINE TRANSFORM IN COMPRESSED DOMAIN

V. Anitha1 and S. Murugavalli1
  • 1 , India
Journal of Computer Science
Volume 10 No. 10, 2014, 1908-1916

DOI: https://doi.org/10.3844/jcssp.2014.1908.1916

Submitted On: 13 February 2014 Published On: 6 May 2014

How to Cite: Anitha, V. & Murugavalli, S. (2014). BRAIN TUMOR CLASSIFICATION BASED ON CLUSTERED DISCRETE COSINE TRANSFORM IN COMPRESSED DOMAIN. Journal of Computer Science, 10(10), 1908-1916. https://doi.org/10.3844/jcssp.2014.1908.1916

Abstract

This study presents a novel method to classify the brain tumors by means of efficient and integrated methods so as to increase the classification accuracy. In conventional systems, the problem being the same to extract the feature sets from the database and classify tumors based on the features sets. The main idea in plethora of earlier researches related to any classification method is to increase the classification accuracy.The actual need is to achieve a better accuracy in classification, by extracting more relevant feature sets after dimensionality reduction. There exists a trade-off between accuracy and the number of feature sets. Hence the focus in this study is to implement Discrete Cosine Transform (DCT) on the brain tumor images for various classes. Using DCT, by itself, it offers a fair dimension reduction in feature sets.Later on, sequentially K-means algorithm is applied on DCT coefficients to cluster the feature sets. These cluster information are considered as refined feature sets and classified using Support Vector Machine (SVM) is proposed in this study. This method of using DCT helps to adjust and vary the performance of classification based on the count of the DCT coefficients taken into account. There exists a good demand for an automatic classification of brain tumors which grealtly helps in the process of diagnosis. In this novel work, an average of 97% and a maximum of 100% classification accuracy has been achieved. This research is basically aiming and opening a new way of classification under compressed domain. Hence this study may be highly suitable for diagnosing under mobile computing and internet based medical diagnosis.

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Keywords

  • Discrete Cosine Transform (DCT)
  • Support Vector Machine (SVM)
  • Magnetic Resonance Image (MRI)