A Hybrid Brain Tumor Classification and Detection Mechanism Using Knn and Hmm

Author(s): R. Meenakshi, P. Anandhakumar.

Journal Name: Current Medical Imaging

Volume 11 , Issue 2 , 2015

Become EABM
Become Reviewer

Abstract:

In medical image processing, detecting Magnetic Resonance Imaging (MRI) brain tumor is one of the most important task. A brain tumor is a collection of cells that have grown and multiplied uncontrollably. It is formed by undesired cells, either normally found in the disparate part of the brain, such as, lymphatic tissue, glial cells, neurons, blood vessels, skull or spread from cancers mainly located in other organs. Brain tumors are classified based on the type of tissue involved in the brain. Generally, it can be classified into two types such as, benign (non-cancerous) and malignant (cancerous). MRI brain tumor identification and detection is an important, but time consuming task performed by medical experts. This paper presents an automatic MRI brain detection and classification method based on K-Nearest Neighbor (KNN) classifier and Hidden Markov Model (HMM) classifier. The proposed method consists of three stages, such as, preprocessing, feature extraction and classification. Here, the Gaussian filtering technique is used to preprocess the given image by eliminating the noise and filtering the image. The feature extracting involve extracting the first order statistical features, second order statistical features and moment invariant features. Finally, the K-NN and HMM classifiers are employed to classify the given image as normal or abnormal. The experimental results evaluate the performance of the proposed algorithm in terms of sensitivity, specificity and classification rate.

Keywords: Brain tumor, Feature Extraction, Gaussian Filtering, Hidden Markov Model (HMM), K-Nearest Neighbor (KNN), Magnetic Resonance Image (MRI), Moment Invariant Feature Extraction.

Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 11
ISSUE: 2
Year: 2015
Page: [70 - 76]
Pages: 7
DOI: 10.2174/157340561102150624143233
Price: $58

Article Metrics

PDF: 15