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

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Abstract

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.

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Acknowledgements

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.

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Authors and Affiliations

  1. School of Computer Science and Technologies, Department of Computer Science and Applications, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Tamil Nadu, 624302, India

    T. Kalaiselvi & P. Kumarashankar

  2. School of Computing, Department of Computer Applications, Kalasalingam Academy of Research and Education (Deemed to be University), Krishnankoil, Tamil Nadu, 626126, India

    P. Sriramakrishnan

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  1. T. Kalaiselvi
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Correspondence to P. Sriramakrishnan.

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Kalaiselvi, T., Kumarashankar, P. & Sriramakrishnan, P. Three-Phase Automatic Brain Tumor Diagnosis System Using Patches Based Updated Run Length Region Growing Technique. J Digit Imaging 33, 465–479 (2020). https://doi.org/10.1007/s10278-019-00276-2

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