Brain Tumor Detection in MR Imaging Using DW-MTM Filter and Region-Growing Segmentation Approach

Medical images magnetic resonance imaging (MRI) analysis is a very challenging domain especially in the segmentation process for predicting tumefactions with high accuracy. Although deep learning techniques achieve remarkable success in classification and segmentation phases, it remains a rich area to investigate, due to the variance of tumefactions sizes, locations and shapes. Moreover, the high fusion between tumors and their anatomical appearance causes an imprecise detection for tumor boundaries. So, using hybrid segmentation technique will strengthen the reliability and generality of the diagnostic model. This paper presents an automated hybrid segmentation approach combined with convolution neural network (CNN) model for brain tumor detection and prediction, as one of many offered functions by the previously introduced IoMT medical service “DiaMe”. The developed model aims to improve extracting region of interest (ROI), especially with the variation sizes of tumor and its locations; and hence improve the overall performance of detecting the tumor. The MRI brain tumor dataset obtained from Kaggle, where all needed augmentation, edge detection, contouring and binarization are presented. The results showed 97.32% accuracy for detection, 96.5% Sensitivity, and 94.8% for specificity.

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An Effective Brain Tumor Detection and Segmentation Technique by Using Watershed Algorithm and Optimized Neural Network

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2016.5700 ◽

2016 ◽

Vol 13 (10) ◽

pp. 7238-7249

Author(s):

Anand J Dhas ◽

S. S Vinsley

Keyword(s):

Brain Tumor ◽

Optimization Technique ◽

Region Growing ◽

Texture Features ◽

Tumor Detection ◽

Research Area ◽

Input Image ◽

Skull Stripping ◽

Image Shape ◽

Ann Classifier

In recent years brain tumor detection using MRI images is an effective clinical research area since MRI images does not make any tissue damage with its radiation and provides useful information about the tissue we are using MRI images in our proposed work for the brain tumor detection. Our proposed brain tumor detection method consists of four sections, i.e., pre-processing, segmentation, feature extraction and classification. Initially the input image fetched from the MRI database will be subjected to skull stripping in order to remove the unwanted region from the image. Then the skull stripped image is segmented using efficient watershed segmentation algorithm. Afterwards from the segmented image shape, intensity and texture features will be extracted. Then that extracted features is given as the input to the ANN classifier. Here the ANN classifier is optimized by well-known ABC optimization technique in order to get the enhanced classification accuracy. Thus from the classified abnormal images the tumor and edema region will be separated using modified region growing algorithm. The results will be analyzed to demonstrate the performance of the proposed segmentation and classification technique with other existing techniques.

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