Diagnosis of brain tumours by MRI binarisation with variable fuzzy level. Issue 16 (23rd February 2021)

• Record Type: Journal Article
• Authors: Shirali, Armaghan; Shiri, Nabiollah
• Is Part Of: IET image processing. Volume 14:Issue 16(2020)
• Page Start: 4269
• Page End: 4276
• Publication Date: 2021-02-23
• Subjects: tumours -- image segmentation -- image classification -- biomedical MRI -- image processing -- feature extraction -- brain -- edge detection -- medical image processing; binary image -- variable fuzzy level -- magnetic resonance imaging process -- BraTs 2019 database -- high‐computational CIP algorithm -- Prewitt filter -- binarisation images two clusters -- MRI binarisation -- brain tumour detection -- medical image processing -- human brain tumours -- classical image processing -- tumour area extraction algorithm; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.1209
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.