Brain tumour classification using two-tier classifier with adaptive segmentation technique

V. Anitha; S. Murugavalli

Brain tumour classification using two-tier classifier with adaptive segmentation technique

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Author(s): V. Anitha ¹ and S. Murugavalli ²
- Affiliations: 1: Department of Information Technology, Sri Muthukumaran Institute of Technology, Chikkarayapuram, near Mangadu, Chennai 600069, Tamil Nadu, India ;
  2: Department of Computer Science and Engineering, Panimalar Engineering College, Banglore Trunk Road, Varadharajapuram, Nazathpettai, Poonamallee, Chennai 600123, Tamil Nadu, India
Source: Volume 10, Issue 1, February 2016, p. 9 – 17
DOI: 10.1049/iet-cvi.2014.0193 , Print ISSN 1751-9632, Online ISSN 1751-9640

Received 24/07/2014, Accepted 22/06/2015, Revised 25/05/2015, Published 01/02/2016

A brain tumour is a mass of tissue that is structured by a gradual addition of anomalous cells and it is important to classify brain tumours from the magnetic resonance imaging (MRI) for treatment. Human investigation is the routine technique for brain MRI tumour detection and tumours classification. Interpretation of images is based on organised and explicit classification of brain MRI and also various techniques have been proposed. Information identified with anatomical structures and potential abnormal tissues which are noteworthy to treat are given by brain tumour segmentation on MRI, the proposed system uses the adaptive pillar K-means algorithm for successful segmentation and the classification methodology is done by the two-tier classification approach. In the proposed system, at first the self-organising map neural network trains the features extracted from the discrete wavelet transform blend wavelets and the resultant filter factors are consequently trained by the K-nearest neighbour and the testing process is also accomplished in two stages. The proposed two-tier classification system classifies the brain tumours in double training process which gives preferable performance over the traditional classification method. The proposed system has been validated with the support of real data sets and the experimental results showed enhanced performance.

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Brain tumour classification using two-tier classifier with adaptive segmentation technique

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