access icon free Diagnosis of brain tumours by MRI binarisation with variable fuzzy level

Brain tumour detection is still a challenging problem both in medical and image processing. In this study, two scenarios are applied to diagnose human brain tumours, classical image processing (CIP) and a new algorithm called binary image with variable fuzzy level (BIVFL). Magnetic resonance imaging (MRI) process from the BraTs 2019 database. Edge detection and prognosis area cropping are parts of the high-computational CIP algorithm. For the CIP, the best filter is the combination of the horizontal and vertical modes of the Prewitt filter. In the BIVFL, by changing the fuzzy level for binarisation images two clusters are filled with tumour and no tumour areas, and the tumour area is extracted in various accuracies. Sensitivity, specificity, precision, and accuracy of the BIVFL are varied according to the fuzzy level, and the best value of sensitivity is 0.9, but the value of three other parameters is 1 for an interval of fuzzy levels. The BIVFL is a simple and fast tumour area extraction algorithm, and also it is easy to implement with digital signal processors. Histogram and power signal-to-noise ratio of the BIVFL is remarkable.

Inspec keywords: image segmentation; biomedical MRI; image classification; medical image processing; image processing; brain; tumours; edge detection; feature extraction

Other keywords: MRI binarisation; magnetic resonance imaging process; variable fuzzy level; human brain tumours; classical image processing; medical image processing; BraTs 2019 database; binarisation images two clusters; Prewitt filter; tumour area extraction algorithm; binary image; high-computational CIP algorithm; brain tumour detection

Subjects: Patient diagnostic methods and instrumentation; Image recognition; Biology and medical computing; Medical magnetic resonance imaging and spectroscopy; Computer vision and image processing techniques; Biomedical magnetic resonance imaging and spectroscopy

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