Comparison of Different Image Processing Methods with Spatial Information in Clinical Brain MRI

• Published in: PROCEEDINGS OF 2020 12TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICAL TECHNOLOGY, ICBBT 2020
• Main Authors: Isa, Iza Sazanita; Saad, Mohamad Khairul Faizi Mat; Kadir, Muhammad Haris Khusairi Mohmad; Afandi, Ahmad Afifi Ahmad; Karim, Noor Khairiah A.; Sulaiman, Siti Noraini
• Format: Proceedings Paper
• Language: English
• Published: ASSOC COMPUTING MACHINERY 2020
• Subjects: Computer Science; Engineering
• Online Access: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001143762400015

The technology expansion in medical imaging has been become important part of clinical practice particularly in interdisciplinary research field. A computer aided diagnostic processing has crucially contribute to the development of algorithm and computing languages in imaging technology especially in medical field. However various methods and technique has been applied on processing the MRI images without any golden standard procedures or methods. This paper is studied different methods of image processing methods for image filtering, image enhancement and image segmentation by using global spatial techniques on MRI brain images of clinical routine. A common standard method for processing the MRI images has been varied depending on clinical applications and intentions. As more challenges arise, the processing and analyzing MRI images with different modalities are also significant so that high quality information can be produced for disease diagnosis and treatment planning. Therefore, this study is conducted to study the comparison of various methods for processing the clinical MRI images for most important elements in image processing that are filtering, enhancement and segmentation. All the comparisons methods are computationally developed and tested using MATLAB programming and the performance of each methods are evaluated based on qualitative and quantitative measurement. The results present most suitable methods for filtering, enhancing and segmenting the T2-WI MRI images particularly for determine the WMH lesions on brain image.