Abstract:
This paper aims to develop a fully automatic Magnetic Resonance Imaging Brain tumor segmentation and detection to tackle the problem of manual segmentation, which is an error-prone, sensitive, and time-absorbing process. We enhance our previous framework for optical scanning holography to detect abnormal tissue regions in Magnetic Resonance Imaging in terms of acquisition speed, precision, and data size. The proposed method combines the in-line holography setup, performed by a heterodyne fringe pattern, and a Magnetic Resonance Imaging display assured by a spatial light modulator. The extraction of the maximum peaks In-phase component of the scanned current gives a reliable precision to the tumor's position. Simultaneously, this position is applied in an Active Contour Model to perform a fast segmentation of the region corresponding to the tumors. Various images of brain tumors from the BRATS database, which have different contrast and shape, are used to test the proposed method. The suggested method achieves high accurate detection of tumor tissue by returned parameters (L,c) by the Generalized Optical Scanning Holography method. In addition, compared to active contour-based methods, the proposed method offers faster and more reliable performance with very short average computation time per image.
Keywords:
Infrared thermometry, Crop water stress index, Baseline equations, Real-time, Workswell Wiris Agro R infrared camera.
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