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Erschienen in:
Abdominal Imaging in Clinical Applications: Computer Aided Diagnosis Approaches Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Optimized Multi Threshold Brain Tumor Image Segmentation Using Two Dimensional Minimum Cross Entropy Based on Co-occurrence Matrix

verfasst von : Taranjit Kaur, Barjinder Singh Saini, Savita Gupta

Erschienen in: Medical Imaging in Clinical Applications

Verlag: Springer International Publishing

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Abstract

The present chapter proposes an automatic segmentation method that performs multilevel image thresholding by using the spatial information encoded in the gray level co-occurrence matrix (GLCM). The 2D local cross entropy approach that has been designed by extending the one dimensional (1-D) cross entropy thresholding method to a two dimensional (2D) one using the GLCM, serves as a fitness function. The use of conventional exhaustive search based implementations for multilevel thresholding are computationally expensive. Under such conditions evolutionary algorithm like particle swarm optimization (PSO) has been used. The effectiveness of this method was tested on brain tumor MR images and comparison was done with seven other level set based segmentation algorithms, using three different measures (1) Jaccard, (2) Dice and (3) Root mean square error (RMSE). The results demonstrate that average metric values were equal to 0.881902, 0.936394 and 0.070123 for proposed approach, which were significantly better than existing techniques.

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Vorheriges Kapitel Nature Inspired Optimization Algorithms for CT Liver Segmentation
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Metadaten
Titel
Optimized Multi Threshold Brain Tumor Image Segmentation Using Two Dimensional Minimum Cross Entropy Based on Co-occurrence Matrix
verfasst von
Taranjit Kaur
Barjinder Singh Saini
Savita Gupta
Copyright-Jahr
2016
Buch
Medical Imaging in Clinical Applications

Print ISBN: 978-3-319-33791-3

Electronic ISBN: 978-3-319-33793-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-33793-7_20