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Selective segmentation of brain abnormalities in colour MRI images using variational model

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sep2024

Akmal Shafiq Badarul Azam

School of Mathematical Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Sarawak Branch, Mukah Campus, Mukah, Malaysia

Abdul Kadir Jumaat

School of Mathematical Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Shah Alam, Selangor, Malaysia

Institiute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi MARA Shah Alam, Selangor, Malaysia

Shafaf Ibrahim

School of Computing Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Shah Alam, Selangor, Malaysia

Nor Farihah Azman

School of Mathematical Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Shah Alam, Selangor, Malaysia

Sarah Farhana Zamalik

School of Mathematical Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Sarawak Branch, Mukah Campus, Mukah, Malaysia

Muhammad Zulkhairi Zakariah

School of Mathematical Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Sarawak Branch, Mukah Campus, Mukah, Malaysia
Abstract

Early detection of brain abnormalities is vital for enhancing patient outcomes and survival rates. However, accurately identifying and segmenting these abnormalities from MRI images remains a persistent challenge. This study assesses the efficacy of the Selective Local Image Fitting (SLIF) model in segmenting brain abnormalities from colour MRI images and compares its performance with converted greyscale counterparts. The rationale behind this comparison stems from standard practice in image segmentation, where colour images are often converted to greyscale before the segmentation task. Converting the image might degrade data by diminishing its dimensions, potentially affecting segmentation computations. This study intends to evaluate the influence of colour information on segmentation accuracy and efficiency by directly assessing the SLIF model on both colour and converted greyscale images. Segmentation accuracy was evaluated using metrics such as the Dice Similarity Coefficient (DSC), Matthews Correlation Coefficient (MCC), and Intersection-over-Union (IoU). Efficiency was determined by measuring the average elapsed processing time.  Experimental results demonstrate that colour MRI brain images outperform their converted greyscale counterparts in segmentation accuracy, as colour providing essential supplementary information for precise abnormality delineation. Despite a slight increase in average elapsed processing time for colour images, the enhanced accuracy justifies this trade-off. These findings emphasize the importance of colour MRI in enhancing diagnostic accuracy, especially in detecting brain abnormalities. This study can be extended in future work to evaluate the segmentation accuracy and efficiency of brain abnormalities in 3D colour and greyscale MRI images.

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Keyword: Active Contour Model, Brain Abnormalities, Colour MRI Images, Level Set Model, Selective Variational Segmentation, Local Image Fitting

DOI: 10.24191/esteem.v20iSeptember.1854.g1820
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