Three-Phase Automatic Brain Tumor Diagnosis System Using Patches Based Updated Run Length Region Growing Technique

  • T. Kalaiselvi
  • P. Kumarashankar
  • P. SriramakrishnanEmail author
Original Paper


Manually finding and segmenting brain tumor is a tedious process in MR brain images due to the unpredictable appearance of tissues with a different pattern, contour, mass, and positions. The proposed work has three phases automatic tumor diagnosis system for tumorous slice detection, segmentation, and visualization from MRI human head volumes. The proposed method has an automatic classification followed by segmentation and is called as patch-based updated run length region growing technique (PR2G). In the first phase, classification is done through training and testing process using SVM classifier with 8 × 8 patches. Three optimal features are chosen using infinite feature selection (IFS) method. The purpose of the first phase is to automatically cluster the input MRI image into a normal or tumorous slice and localize the tumor. The second phase aims to segment the tumor in abnormal tumorous slices identified by the first phase using run length region growing technique. Finally, the third phase contains a post metric evaluation like 3D tumor volume construction and estimation from actual and segmented tumor volume using Carelieri’s estimator. Classification accuracy is measured using sensitivity, specificity, accuracy, and error rates also calculated using false alarm (FA) and missed alarm (MA). Segmentation accuracy is calculated using Dice similarity, positive predictive value (PPV), sensitivity, and accuracy. Datasets used for this experiment are collected from whole brain atlas (WBA) and BraTS repositories. Experimental results show that the PR2G achieves 97% of classification accuracy and 80% of Dice segmentation accuracy.


Tumor detection Feature extraction Tumor patches Brain tumor BraTS dataset Region growing Run length 



The authors wish to thank Dr. R. Rajeswaran, Radiologist, Sri Ramachandra University Medical College, Chennai, for their help in qualitative validation. Further, we acknowledge the support of 3D Doctor licensed software purchased under DST project: SP/YO/011/2007 used for volume rendering process of this research work.

Compliance with Ethical Standards

Conflict of Interest

The authors have no conflict of interests and the paper has not been submitted elsewhere.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.


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Copyright information

© Society for Imaging Informatics in Medicine 2019

Authors and Affiliations

  1. 1.School of Computer Science and Technologies, Department of Computer Science and ApplicationsThe Gandhigram Rural Institute (Deemed to be University)GandhigramIndia
  2. 2.School of Computing, Department of Computer ApplicationsKalasalingam Academy of Research and Education (Deemed to be University)KrishnankoilIndia

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