Computer-Aided Detection of Brain Tumors Using Morphological Reconstruction

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Computer aided detection (CAD) systems helps the detection of abnormalities in medical images using advanced image processing and pattern recognition techniques. CAD has advantages in accelerating decision-making and reducing the human error in detection process. In this study, a CAD system is developed which is based on morphological reconstruction and classification methods with the use of morphological features of the regions of interest to detect brain tumors from brain magnetic resonance (MR) images. The CAD system consists of four stages: the preprocessing, the segmentation, region of interest specification and tumor detection stages. The system is evaluated on REMBRANDT dataset with 497 MR image slices of 10 patients. In the classification stage the performance of CAD has achieved accuracy of 93.36% with Decision Tree Algorithm, 94.89% with Artificial Neural Network (Multilayer Perceptron), 96.93% with K-Nearest Neighbour Algorithm and 96.93% with  Meta-Learner (Decorate) Algorithm. These results show that the proposed technique is effective and promising for detecting tumors in brain MR images and enhances the classification process to be more accurate. The using morphological reconstruction method is useful and adaptive than the methods used in other CAD applications.


Biomedical image processing; Image classification; Morphological reconstruction; Tumor Detection; computer aided detection.

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